The widespread deployment of Internet of Things (IoT) applications relies on indoor positioning systems that are both accurate and sustainable. However, conventional RSSI fingerprinting methods suffer from signal fluctuations in dynamic environments and require costly manual updates to maintain long-term performance. To address these challenges, this paper proposes a dynamic indoor positioning framework that integrates TabNet and transfer learning (TL). A small number of reference points are first deployed, and an offline fingerprint database is efficiently constructed using pedestrian dead reckoning (PDR) and Kriging interpolation, significantly reducing manual effort. A TabNet-based localization model is then trained to learn the nonlinear RSSI–location mapping while mitigating signal instability. Moreover, a reliability evaluation mechanism is introduced to identify high-confidence online position estimates, which are incorporated via TL to continuously update the localization model without additional data collection. Experimental results demonstrate that the proposed method outperforms state-of-the-art localization approaches and maintains stable performance over time, with the root mean square error increase remaining below 0.2 m after 20 days.

The accumulation of inertial equipment errors in the GNSS/INS integrated system can degrade the accuracy of the train's position, velocity, and attitude (PVA) determination in the GNSS-difficult scenarios. To address this issue, a method for improving train attitude accuracy by employing inertial quaternion modelling is proposed. This method involves designing the quaternion estimation model assisted by the train acceleration in the inner-loop’s inertial derivation model (inner-loop estimation). When the INS operates in combined mode with the odometer, the inner-loop KF measurement model causes rotation errors due to the accelerometer’s bias. These errors can be transferred into the odometer, making it difficult to maintain the system's accuracy. To deal with this problem, the external-loop quaternion model is introduced. Utilizing error-quaternion as the coupling medium will ensure that the inner-loop’s quaternion and inertial-bias errors can be estimated and corrected in the external-loop, which involves integrating the inner-loop estimation model with the odometer/INS to generate the double-loop quaternion enhancement method. The experiment results on the Qinghai-Tibet Railway demonstrate that the proposed method has an obvious improvement in the PVA determination compared with odometer/INS method, and it can track the PVA references better and improve capability to maintain the PVA determination accuracy in GNSS-difficult scenarios.

Global navigation satellite system (GNSS) refractometry is a new technique that utilizes two pairs of GNSS receivers and antennas to collect the refracted signal in the medium and the direct signal in the air, respectively. In the previous study, a linear model that uses wheat height, air temperature, and the amplitude ratio (AR) as inputs was utilized to estimate the volumetric water content (VWC) of wheat. In this study, a second-order nonlinear function is utilized to describe the relationship among GNSS AR, wheat VWC, wheat height, and air temperature, leading to an improved model for estimating wheat VWC. The function coefficients are determined by exploiting the least-squares method to the field measurements collected from April 5, 2023, to June 5, 2023. Once the function coefficients and inputs are obtained, the model can be easily used to calculate the wheat VWC. The model was validated using an independent dataset of field measurements collected from April 5, 2024, to June 5, 2024. The results show that the improved model performs significantly better than the previous linear model, and the root-mean-square (rms) error of the improved model-based GNSS wheat VWC estimation is 0.224 kg/m3 when the in situ wheat VWC ranges from 1.404 to 6.521 kg/m3. This model can help precisely control the timing and water usage of agricultural irrigation, thereby optimizing water management for crops.

Sea level change is becoming increasingly complex in the context of global climate change. Accurate and reliable methods for monitoring water levels are essential for advancing the research of oceanic variations. The GNSS Interferometric Reflectometry (GNSS-IR) technique emerges as a complementary approach, leveraging signal-to-noise ratio (SNR) oscillations from reflected GNSS signals to estimate sea surface level. In GNSS-IR sea level retrieval, various factors including dynamic sea surface variations, surface roughness, and observation noise can introduce biases in reflector height (RH). These biases subsequently lead to phase errors in the SNR fitting process. A linear model is often employed to correct such phase deviations. The core challenge lies in the linear phase correction model’s inability to resolve phase deviations exceeding ±π, which introduces integer-cycle ambiguities. These errors, driven by the combined effects of multiple sources of uncertainty, result in RH residuals clustered around ±30–50 cm. To mitigate this issue, a two-stage correction method is developed: (1) a LightGBM (Light Gradient Boosting Machine) classifier identifies and corrects ±2π phase biases by analyzing SNR quality metrics (e.g., peak-to-noise ratio, full width at half maximum), environmental parameters (e.g., sea surface height change rate), and fitting residuals; (2) a sliding-window robust estimation refines RH values by dynamically compensating for residual outliers and tidal fluctuations. Validation across three coastal GNSS stations (SC02, CALC, FLCK) demonstrates significant improvements. The LightGBM model achieved 97.9~99.3% classification accuracy, effectively isolating and correcting 72–93% of ±2π deviations. Combined with robust estimation, the method reduced root mean square error (RMSE) by up to 48.6% compared to classical Lomb-Scargle Periodogram (LSP) results. Residual distributions transitioned from bimodal clusters to centralized peaks near zero, confirming the elimination of stratification artifacts.

Accurate path tracking control is crucial for the performance of autonomous vehicle (AV). However, a single control algorithm struggles to adapt to complex dynamic scenarios with varying error states and path curvatures, leading to challenges in slow convergence and poor tracking performance. To address this, this paper proposes an adaptive fusion control scheme termed AMPIPPO that integrates an attention mechanism and a physical information model into Proximal Policy Optimization (PPO). First, FLnSM and NMPC are combined through PPO by balancing the two controllers. Then, the attention mechanism is incorporated to utilize error information and path curvature as state inputs for generating action outputs, significantly enhancing the adaptability of PPO. Additionally, physical information (PI) is implemented to strengthen both model-driven and data-driven approaches, achieving an accurate model estimation. Furthermore, this paper demonstrates the convergence of policy iterations in the AMPIPPO algorithm. Finally, a control scheme FLnSM-NMPC-AMPIPPO is proposed to achieve accurate tracking and enhance the adaptability of AV in dynamic scenarios. Compared to single controllers, the FLnSM-NMPC-AMPIPPO method exhibits higher tracking accuracy and faster convergence. Compared to other RL, AMPIPPO demonstrates superior performance in sparse data scenarios and significantly enhances AV prediction accuracy. The proposed FLnSM-NMPC-AMPIPPO demonstrates excellent performance through numerical simulations and experiments.

This paper firstly reports the combined technique of GNSS positioning and GNSS-IR (GNSS+IR) for imaging the underground coal mining induced ground subsidence deformation over an area of ~10000m2based on a single GNSS station collected observations. The GNSS positioning is utilized to measure the movements of the GNSS antenna; and phase of the reflected GNSS SNR series is utilized to calculate vertical and horizontal distance from the ground specular reflection point to the antenna. The ground subsidence and plane coordinate of the ground specular reflected point can be obtained based on the GNSS antenna movements and the vertical and horizontal distance. An analytical function is developed to describe ground subsidence around the GNSS station; the function coefficients can be estimated by using the least-squares-method to the estimations of the ground subsidence and plane coordinate at the ground reflection points. After obtaining the coefficients, subsidence deformation around the GNSS station can be imaged based on the analytical function. The preliminary results show that there is a good agreement between the proposed method based results and the reference data sets, with the RMSE less than 5cm when the in-situ ground subsidence is in the range from 0cm to 300cm.

Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technology that uses GNSS signals reflected over Earth’s surface, offering advantages such as high spatio-temporal resolution and cost-effectiveness. GPS navigation signals convey navigation information through the navigation bits, in which phase jumps occur randomly at 20 ms interval. However, navigation information is meaningless in GNSS-R and limits the coherent integration time. This study presents a technique to mitigate the impact of the navigation bits on the generation of DDMs by using the raw GNSS-R data from the Fengyun-3E (FY-3E) satellite. Sample results show that, after correcting for the navigation bits, the signal-to-noise ratio (SNR [dB]) of DDMs increased by ~0.4 dB over sea to up to 3 dB over sea ice, showing that this improvement is very significant for coherent targets. The proposed navigation bit correcting method in this study significantly improves the usability of these DDMs, and opens the door to other applications that require larger integration times.

Sea surface rainfall intensity is an important sea state parameter and has an important impact on marine navigation safety and global climate. At present, the methods for measuring sea surface rainfall intensity include rain gauge, weather radar and remote sensing. However, increasing the spatial and temporal resolution and decreasing the cost are still a challenge. GNSS reflectometry (GNSS-R) is an emerging remote sensing technology, which may effectively handle the resolution and cost issues. In this paper, by making use of spaceborne GNSS-R data, three models based on random forest are developed for retrieving sea surface rainfall intensity. The normalized bistatic radar cross section (NBRCS), the leading edge slope (LES), and the signal to noise ratio (SNR) from the CYGNSS are used as the key variables for the rainfall intensity retrieval. The results show that the model only considering wave effects has the best accuracy, with the coefficient of determination (fi2) of 0.79 and the root mean square error (RMSE) of 1.05 mm/hr. Compared with the model without considering wind and wave effects, the fi2improves by 12.9% and the RMSE improves by 16%. The results also show that the effect of wind on rainfall intensity retrieval should not be considered, if the effect of wave is already considered.

Fire is one of the mutable hazards that damage properties and destroy forests. However, fire-like objects easily influence many existing image-based fire detection methods. In order to improve the performance of fire identification, this paper proposes a new fire identification algorithm by merging fire segmentation and multi feature fusion of fire. First, the improved YCbCr models in the reflection and non-reflection environment are constructed according to the color model. Simultaneously, the reflection and non-reflection conditions can be judged according to the segmented area. Second, the seed points are determined according to the weighted average of centroid of each connected region. Simultaneously, the fine segmentation of fire image is implemented according to the entropy of average contrast and uniformity within the connected region. Experiments show that the segmentation is not affected by image noises. Finally, the quantitative indicators of fire identification are given according to the coefficient of variation of area, the dispersion of centroid and the circularity. Cases show that the proposed identification method of fire not only accurately identifies fire, but also has a lower computation complexity than the deep learning method.

Visual-inertial odometer (VIO) can enable localization and navigation in indoor environments without the support of infrastructure, however the realization of VIO on smartphones is still a challenge. To build a highly precise smartphone-based VIO for pedestrian localization, a deep learning (DL)-based integrated localization algorithm is proposed, which uses the measurements of visual odometer (VO) and pedestrian dead reckoning (PDR). Specifically, the IMU data is used to estimate pedestrian’s location based on PDR, which can be readily implemented on smartphone. To address the scale ambiguity of monocular VO, this article innovatively proposes to use the gray wolf optimization (GWO) algorithm to determine the scale factor. Two effective DL algorithms, namely, back propagation (BP) and long short-term memory (LSTM) neural networks, are utilized to integrate the position estimate of monocular VO and that of PDR, and the proposed localization algorithm is named VP-BGL for convenience. Experimental data were collected from three different indoor scenarios for testing our proposed VP-BGL algorithm, and the three indoor scenarios were in a classroom, in an underground parking lot, and on a floor of an office building. The experimental results show that our proposed VP-BGL integrated localization algorithm has the best positioning compared to three exist VIOs. Compared to the state of art VIO, our proposed algorithm improves the accuracy in terms of root mean square error (RMSE) by 0.424 m on average for the three experimental fields.

The global navigation satellite system (GNSS) refraction pattern technique makes use of two pairs of GNSS receivers and antennas to collect the refracted signal in the medium and the direct signal in the air, respectively. Due to the sensitive response of the refracted signal to variation of the dielectric constant, the technique is suitable for measuring medium dielectric constant-related parameters such as snowpack density, vegetation water content, and soil moisture. In this article, a forward model related to the power ratio of the refracted signal to the direct one is developed to elucidate the mechanism of soil moisture-induced refracted signal strength attenuation. By making use of the simulating results derived from the model, a second-order polynomial function is established to describe the relationship between the power ratio, soil temperature, and soil moisture. Based on the function, an inversion algorithm, which takes the GNSS carrier-to-noise (C/N0) observations under high elevation angles (50°–60°) and soil temperature as the inputs, is proposed for the near-surface soil moisture estimation. The proposed algorithm is validated through a dataset collected in an experimental campaign over two years. The results demonstrate that there exists a good agreement between the proposed method-derived soil moisture estimations and ground-truth ones; and the root-mean-square error (RMSE) of the proposed algorithm-derived soil moisture estimation is 0.009 cm3cm−3 when the ground-truth soil moisture is in the range from 0.150 to 0.550 cm3cm−3. Because the observations were collected by using consumer-grade GNSS chips and antennas, this study also provides a basis for the design and development of the low-cost GNSS soil moisture sensor in the future.

This study utilizes L1B level data from reflected global navigation satellite system (GNSS) signals from the Cyclone GNSS (CYGNSS) mission to estimate sea surface significant wave height (SWH). The normalized bistatic radar cross Section (NBRCS), the leading edge slope (LES), the signal-to-noise ratio (SNR), and the delay-Doppler map average (DDMA) are used as the key variables for the SWH retrieval. Eight other parameters, including instrument gain and scatter area, are also utilized as auxiliary variables to enhance the SWH retrieval performance. A variety of multivariable regression models are investigated to clarify the relationship between the SWH and the variables by using the following five methods: stepwise linear regression, Gaussian support vector machine, artificial neural network, sparrow search algorithm–extreme learning machine, and bagging tree (BT). Results show that, among the five regression models developed, the BT model performs the best with the root mean square error (RMSE) of 0.48 m and the correlation coefficient (CC) of 0.82 when testing one million sets of data randomly selected, while the RMSE and CC of BT model are 0.44 m and 0.73 in the 4500 National Data Buoy Center (NDBC) buoy testing dataset. Meanwhile, the BT model also has the best generalization ability, which means that it performs well in practical applications. In addition, the impacts of different input variables, the size of the training dataset, and the sea surface wind speed are also investigated. These findings are anticipated to serve as helpful guides for creating future SWH retrieval algorithms that are more advanced.

Significant Wave Height (SWH) is a key parameter to characterize waves, which is typically used in sea state monitoring such as wave forecast to ensure ocean navigation safety. Satellite radar altimeter is probably the primary tool to obtain SWH information. However, it cannot be used for large-scale sea state monitoring unless many of theses satellites are deployed. In this article, we aim to study the potential of Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) in SWH measurement based on spaceborne Delay-Doppler Maps (DDMs) data. First, 3 observables (i.e., Delay-Doppler Map Average (DDMA), leading edge slope (LES) of normalized integrated delay waveform (NIDW) (LES-NIDW), and trailing edge slope (TES) of NIDW (TES-NIDW) derived from the DDMs are introduced for SWH estimation. Then, an empirical SWH retrieval model is proposed based on three observables. Subsequently, ERA5 SWH is used as reference data to verify the performance of the proposed model. The experimental results show that the Root Mean Square Error (RMSE) and Correlation Coefficient (CC) estimated by SWH of the three observables are better than 0.54 m and 0.88 m, respectively. Among them, the estimation performance based on DDMA observable is the best, with RMSE and CC of 0.49 m and 0.89 m. This study shows the potential of spaceborne GNSS-R in SWH retrieval.

In recent years, global navigation satellite systems-reflectometry (GNSS-R) technology has been increasingly considered for applications in sea surface monitoring. This paper presents a new method to retrieve the density of sea surface green algae by using the reflected signals of geostationary Earth orbit (GEO) satellites collected by shipborne receiver. Because GEO satellites are stationary relative to a fixed receiver on the earth’s surface, the reflected GEO satellite (GEO-R) signals are not affected by Doppler frequency or elevation angle, which can greatly simplify the modeling of the reflected power and realize continuous green algae monitoring in the same area. Specifically, the influence of green algae on GEO-R power through varying reflection coefficient and roughness was analyzed. Then, an empirical model was established to retrieve the green algae density by using the GEO-R power. Finally, the experimental data collected in the Qingdao Jiaozhou bay were used to verify the developed models, and the results show that the inversion accuracy of the green algae density model is better than 4%.

In this letter, we first propose two empirical models to retrieve significant wave height (SWH) using two GNSS Reflectometry (GNSS-R) observables derived from delay-Doppler map (DDM), namely DDM average (DDMA) and leading edge slope (LES). Then, we utilize minimum variance to establish a combined model to enhance the SWH estimation performance. Collocated ERA5 SWH data is utilized as the ground truth for the development and evaluation of the SWH models. The results show that the SWH estimates by the three models are highly consistent with ERA5 SWH data, with a root mean square error (RMSE) less than 0.502 m and a correlation coefficient (CC) higher than 0.88. In particular, the combined model has significantly smaller RMSE of 0.428 m and larger CC of 0.91; and compared with the combined model based on weighted average (WA) method and LES observable model based on integral delay waveform, the RMSE is improved by 20.15 % and 14.74 %, respectively. The performance of spaceborne GNSS-R SWH retrieval can be greatly enhanced by the construction of integrated model, as demonstrated by this letter.

A geophysical model function (GMF) for significant wave height (SWH) retrieval is developed based on the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data measured by the Cyclone GNSS (CYGNSS) satellites. The spreading characteristics of delay-Doppler maps (DDMs) generated by receivers onboard satellites are affected by the surface roughness, which is closely related to the SWH. Four GNSS-R observables [i.e., leading edge slope (LES) of normalized integrated delay waveform (NIDW), LES of normalized central delay waveform (NCDW), trailing edge slope (TES) of NCDW, leading edge waveform summation (LEWS) of NCDW] derived from DDM are first used in this letter to retrieve SWH. Collocated ERA5 SWH data are used as the ground truth to develop and evaluate the SWH models based on the four GNSS-R observables. The results show that there is high consistency between the SWH estimates and the ground truth, with a correlation coefficient of 0.88 and a root mean square error (RMSE) of 0.503 m. This letter demonstrates the feasibility of the spaceborne GNSS-R in SWH retrieval.

Global Navigation Satellite System interferometric reflectometry is an emerging remote sensing technique that can be used to measure a wide range of geophysical parameters. In this letter, a low-cost navigational GNSS chip and RHCP antenna were used to receive and process the interference GNSS signal in a winter wheat farmland. By simplifying the wheat crop as multi-layer equivalent mediums (EMs), the characteristics of GNSS SNR observations recorded by the instrument were analyzed. The confidence level of the Lomb-Scargle spectral analysis result was used to identify the peak frequencies of the GNSS SNR series. Based on the peak frequencies, the EM heights can be calculated. The height estimations were used to compare with thein situwheat height measurements. The results show that the estimated EM height is inversely proportional to thein situwheat height in the wheat stem extension stage, with a correlation coefficient of −0.9939; and the estimation is very close to thein situones in wheat heading and ripening stages, with a root-mean-square error of 5.8 cm when the wheat height ranges between 40 and 75 cm.

In the last 20 years, the reflected signal of Global Navigation Satellite System (GNSS) has been used for remotely sensing a series of geophysical parameters, resulting in two GNSS based remotely sensing techniques: GNSS reflectometry (GNSS-R) and GNSS interferometric reflectometry (GNSS-IR). In this letter, the refracted GNSS signal is first proposed to estimate near-surface soil moisture (SM). Amplitude of the refracted GNSS signal will attenuate when penetrated into soil due to refraction and propagation of the signal in the soil. Amplitude attenuation degree of the refracted signal is quantified as the amplitude ratio (AR) of the direct GNSS signal to the refracted signal. Two low-cost navigational GNSS chips and right-hand circularly polarized (RHCP) antennas are used to collect the refracted and direct GNSS signal in an experimental campaign, respectively. To simplify the modeling, the AR at elevation angle of 20° is used to develop the model to describe the relationship between SM, AR, and soil temperature (ST) in the letter; and the AR and ST observation can be converted into SM accurately with a 2nd-order polynomial. The modeled SMs are strongly correlated with the sensor-based ones with correlation coefficient of 0.947 and root-mean-square error (RMSE) of 0.013 cm3/cm3(or, 1.3%) when SM is between 0.272 and 0.489 cm3/cm3. The study also suggests that, based on the proposed method, the low-cost GNSS instrument can be treated as a new type of sensor monitoring SM in a cost-effective way.

This paper focuses on improving indoor Wi-Fi localization by mitigating the effect of fluctuation of received signal strength indication (RSSI). The RSSI data collected at each reference point is first transformed through translation and scaling. The BP (Back Propagation) neural network is then used to construct the ranging model using the transformed RSSI to determine the distances between the target point and each reference point. A genetic algorithm (GA) is developed to optimize the initial values of weights and biases of the BP neural network. For convenience, our proposed ranging model is denoted as GTBPD. A new localization algorithm is then proposed, which uses the GTBPD model and the sequential quadratic programming (SQP, an iterative nonlinear optimization algorithm), and the algorithm is denoted as GTBPD-LSQP for simplicity. Experiments were conducted in three areas of two different teaching buildings with complex environments. The performance of the proposed GTBPD-LSQP algorithm is evaluated and compared with four existing algorithms. The experimental results show that our proposed GTBPD-LSQP algorithm achieves significantly higher location accuracy than the four existing algorithms.

Due to the continuous intensification of human activities in the ocean, the frequent outbreaks of red tide have caused great harm to the marine environment and ecology. Thus, the rapid detection and monitoring of red tide become particularly important. At present, the main monitoring methods depend on artificial and buoy data, as well as optical satellite remote sensing. However, these methods may not be able to effectively deal with the characteristics of red tide bloom, such as suddenness and unpredictability. The global navigation satellite system-reflectometry (GNSS-R) is an emerging technology that makes use of navigation signals as a remote sensing opportunity to obtain Earth surface information. GNSS-R has already been proved to be capable of retrieving sea surface parameters (e.g., dielectric constant and sea surface roughness) closely related to the outbreak of a red tide. In this article, we proposed a new method to estimate red tide density, which utilizes an all-new model associating GNSS-R observations with sea surface red tide density. This method can remove the weather influence and greatly decrease the revisit period, which is much longer for optical red tide remote sensing methods. The Landsat-8 near-infrared data and TechDemoSat-1 (TDS-1) GNSS-R data of a red tide outbreak in the sea off the Tsingtao coast in China are used to build and test the proposed method. The results demonstrate that the correlation coefficient is 0.73, and the root mean square error of retrieved red tide density is 2.84%, which shows that the GNSS-R technology shows great potential to perform the rapid and preliminary red tide monitoring and judgment.

Rainfall detection (RD) and rainfall intensity (RI) retrieval are hot topics in the field of ocean remote sensing (RS). In the past, the sea surface RD and RI retrieval were usually based on X-band ocean radar image data. In this study, we aim to investigate the potential of global navigation satellite system-reflectometry (GNSS-R) for sea surface RD and RI retrieval based on delay Doppler maps (DDMs) data collected by the cyclone GNSS (CYGNSS) mission. First, the block-matching and 3-D filtering (BM3D) algorithm is proposed to improve the quality of DDM data. In addition, 12 GNSS-R observables derived from DDM are calculated, and an RD method based on the threshold of 12 GNSS-R observables is proposed based on the probability density function (PDF). When rainfall DDM data are detected, these data are used to develop and verify the sea surface RI retrieval model. The integrated multisatellite retrievals of global precipitation measurements (GPM-IMERG) data product are used as reference data to evaluate the performance of RD and RI retrieval model. The experimental results show that under very low wind speed (<5 m/s), the proposed trailing edge waveform summation of normalized integral delay waveform (TEWS-NIDW), TEWS of normalized center delay waveform (TEWS-NCDW), and TEWS of differential delay waveform (TEWS-DDW) observables are the best for RD, and the probability of detection of rainfall (PDr) is better than 75%. In the aspect of model retrieval performance, the root mean square error (RMSE) of the 12 observables is less than 4.66 mm/hr. Among them, the model based on TEWS-NCDW observables has the highest accuracy, better than 3.74 mm/hr.

This paper mainly studies the inversion of sea surface wind speed based on spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data. Delayed Doppler Map (DDM) is one of the most important data acquired by GNSS-R receivers. We make use of four DDM-based observations (DDM average (DDMA), signal-to-noise ratio (SNR), leading edge slope (LES) of Integrated Delay Waveform (IDW) and Normalized Bistatic Radar Cross Section (NBRCS)) to retrieve sea surface wind speed. We first propose a data filtering method based on the LES of IDW for data quality control. This method selects high -quality DDM data by adjusting LES threshold. Then, four wind speed inversion models are developed by using the four observations calculated from the clean DDM data. On this basis, five more wind speed inversion models are developed by using five weighted combinations of the four individual models. The nine models are tested and the results show that the wind speed estimation accuracy of the combined DDMA+NBRCS+SNR+LES model is respectively improved by 23.53%, 13.90%, 25.06% and 14.70% compared with the individual models of DDMA, SNR, LES, and NBRCS in the wind speed range of 0–20 m/s. Also, compared with dual combinations (DDMA+SNR, DDMA+NBRCS, and SNR+NBRCS), the accuracy of triple combination of DDMA+NBRCS+SNR is improved by 3.45%, 11.38%, 9.14% and 1.46%, respectively.

Soil moisture is fundamental to land surface hydrology, affecting flooding, groundwater recharge, and evapotranspiration. In this paper, a low-cost GNSS receiver is used to estimate soil moisture the first time. A new soil moisture estimation method based on the receiver SNR data is proposed. The relationship between amplitude attenuation factor (AAF) of SNR and soil moisture is investigated by using in-situ observations at first. Then, the retrieval method of SNR AAF is proposed. GPS data collected over a month in Chongqing, China was used to test the proposed method. Based on in-situ SNR and soil moisture observations, 1stand 2ndregression functions converting AAF into soil moisture were established by least squares method. The preliminary results show that there is a good agreement between the insitu soil moisture and the estimated one by the proposed method, with the RMSE smaller than 0.012 when soil moisture is in the range of 0.35 to 0.45.

With the widely deployed wireless access points (APs) and the worldwide popularization of smartphones, WiFi-based indoor positioning has attracted great attention to both industry and academia. Locating and tracking objects within an indoor environment plays an important role in Internet of Things application and service. However, it is a challenging problem to achieve high accuracy using WiFi positioning technique due to the high instability in received signal strength from AP. Thus, it is desirable to select APs by considering both signal strength and connection quality. In this article, an AP selection algorithm based on multiobjective optimization is proposed to improve indoor WiFi positioning accuracy. The self-adaptive AP selection algorithm can be easily applied to various real scenarios and the performance of the new method is considerably better than classical algorithms. Learning algorithm is exploited to obtain the optimal solution for the self-adaptive AP selection algorithm. Experiments are conducted and the proposed algorithm is compared with classical algorithms. The experimental results demonstrate that the performance of the self-adaptive AP selection algorithm is at least a few decimeters better than classical algorithms in terms of RMSE of position estimation. Meanwhile, the new method is robust to the random generation of initial particles and normalizing factor as their effect on the positional accuracy is less than 1 decimeter.

A geophysical model function (GMF) for sea ice concentration (SIC) retrieval is developed based on the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data measured by the TechDemoSat-1 (TDS-1) satellite. The spreading characteristics of onboard processed delay-Doppler maps (DDMs) change with the surface roughness, which can be related to the SIC. A GNSS-R observable termed as differential delay waveform (DDW) generated from DDM is first used in this article to estimate SIC. Collocated SIC data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are used as the ground truth to develop and evaluate the SIC model based on the right edge waveform summation (REWS) of DDW. All usable TDS-1 data collected from February 2015 to February 2016 are adopted, and data collected over land were excluded. SIC models of the northern and southern hemispheres (SH) are developed, respectively, for avoiding the impact of geometry. In general, the REWS-based model can achieve a root mean square error (RMSE) of 11.78% and a bias of 1.67% for the northern hemisphere, and 12.10% and 1.94% for the SH, respectively. This article demonstrates the capabilities of the spaceborne GNSS-R in SIC retrieval.

In the last decades, 3-D object recognition has received significant attention. Particularly, in the presence of clutter and occlusion, 3-D object recognition is a challenging task. In this article, we present an object recognition pipeline to identify the objects from cluttered scenes. A highly descriptive, robust, and computationally efficient local shape descriptor (LSD) is first designed to establish the correspondences between a model point cloud and a scene point cloud. Then, a clustering method, which utilizes the local reference frames (LRFs) of the keypoints, is proposed to select the correct correspondences. Finally, an index is developed to verify the transformation hypotheses. The experiments are conducted to validate the proposed object recognition method. The experimental results demonstrate that the proposed LSD holds high descriptor matching performance and the clustering method can well group the correct correspondences. The index is also very effective to filter the false transformation hypotheses. All these enhance the recognition performance of our method.

Point cloud boundary is an important part of the surface model. The traditional feature extraction method has slow speed and low efficiency and only achieves the boundary feature points. Hence, the point cloud feature regularization is proposed to obtain the boundary lines based on the fast extraction of feature points in this article. First, an improved k-d tree method is used to search the k neighbors of sampling point. Then, the sampling point and its k neighbors are used as the reference points set to fit a microcut plane and project to the plane. The local coordinate system is established on the microcut plane to convert 3-D into 2-D. The boundary feature points are identified by judging criterion of field force and then are sorted and connected according to the vector deflected angle and distance. Finally, the boundary lines are smoothed by the improved cubic B-spline fitting method. Experiments show that the proposed method can extract the boundary feature points quickly and efficiently, and the mean error of boundary lines is 0.0674 mm and the standard deviation is 0.0346 mm, which has high precision. This proposed method was also successfully applied to feature extraction and boundary fitting of Xinyi teaching building of the Wuhan University of Technology.

Indoor scene segmentation based on 3-D laser point cloud is important for rebuilding and classification, especially for permanent building structure. However, the existing segmentation methods mainly focus on the large-scale planar structures but ignore the other sharp structures and details, which would cause accuracy degradation in scene reconstruction. To handle this issue, an iterative Gaussian mapping-based segmentation strategy has been proposed in this article, which goes from rough segmentation to refined one iteratively to decompose the indoor scene into detectable point cloud clusters layer by layer. An improved model fitting algorithm based on the maximum likelihood estimation sampling consensus (MLESAC) algorithm is proposed for refined segmentation, which is called the Prior-MLESAC algorithm, to deal with the extraction of both vertical and nonvertical planar and cylindrical structures. The experimental results demonstrate that planar and cylindrical structures are segmented more completely by the proposed strategy, and more details of the indoor structure are restored than other existing methods.

In recent years, GNSS reflectometry (GNSS-R) research has mainly been focused on the Global Positioning System (GPS) while the use of Geostationary Earth Orbit (GEO) satellites has received little attention. This paper investigates the GEO satellite-based GNSS-R with a focus on the application of soil moisture retrieval. A new soil moisture estimation approach using GEO signals are proposed, which is termed GEO reflectometry (GEO-R). Two empirical models (linear and second-order) are developed for signal SNR ratio based GEO-R. Experimental datasets collected from different GEO systems were used to evaluate the proposed methods. The results demonstrate that the proposed GEO-R are able to monitor soil moisture reliably under bare soil condition, augmenting GNSS-R through significantly reduced processing complexity and increased temporal coverage.

In this paper we propose a new snow water equivalent (SWE) estimation method using GNSS-R method. The forward model is established to describe the relationship between antenna height (snow depth), snow density and multipath error by using combination of pseudorange and carrier-phase of GNSS dual-frequency signals. As the function of antenna height and snow density, the forward model is used to construct the fitness function based on least squares principle. Then, the problem of inversion of antenna height and snow density is transformed into a conventional multi-variable function optimization problem. The genetic algorithm is used to find the minimum of the fitness function to obtain the optimal snow depth and snow density estimates. The Galileo satellite navigation system data of an experimental campaign conducted in Harbin, China was used to test the proposed method. The preliminary results show that the proposed method can achieve SWE estimation accuracy of about 4cm.

The shape of the object is mainly described by feature points and lines. Since a feature point can be described by the intersection of two feature lines, feature lines are the key to determine the contour of the object. In this article, a novel method for the generation and regularization of point cloud feature line is presented, which consists of two main steps: extraction of the outline points according to the property of vectors distribution and cluster, feature points are sorted according to the vector deflection angle and distance and they are fitted using the improved cubic b-spline curve fitting algorithm. The performance of the proposed method is evaluated with both large and small point clouds acquired by terrestrial laser scanning devices in real-world scenes. The results show that the proposed method and the analysis of geometrical properties of neighborhoods (AGPN) method achieve very similar performance in the case of planar objects, accurately extracting the outline points of objects. However, in the presence of a curved surface, the proposed method significantly outperforms the existing methods in detecting outline points. The outlines are regularized by the improved cubic b-spline and it is superior to the traditional cubic b-spline curve fitting algorithm.

Global navigation satellite system reflectometry (GNSS-R) is a new remote sensing technique, which can be used to measure a wide range of geophysical parameters. GNSS-R makes use of the simultaneous reception of the direct transmission and the coherent surface reflections of the GNSS signal with either a single antenna or multiple separate antennas. This paper presents a new snow depth estimation method using a combination of pseudorange and carrier phase of GNSS dual-frequency signals. The proposed method is geometry-free and is not affected by ionospheric delays. The formulas of the amplitude attenuation factor of reflected signals, multipath-induced carrier-phase error, and pesudorange error for ground-based GNSS receivers are used to describe the combined signals. Using theoretical formulas instead of in situ measurement data, analytical linear models are established in advance to describe the relationship between snow depth and main frequency of combined signal time series. When the main frequency of the combined measurements is obtained by spectrum analysis, the model is used to determine snow depth. Two experimental data sets recorded in two different environments were used to test the proposed method. The results demonstrate that there exists good agreement between the proposed method and the ground-truth measurements.

Continuous and reliable monitoring of world-wide snowfall is important for study of climate change and water resource utilization. Both snow depth and snow water equivalent (SWE) are the measure of snowfall, but SWE is a more useful measure, which is defined as the product of snow depth and snow density. Global Navigation Satellite System reflectometry (GNSS-R) is a new remote sensing technology that can enable cost-effective global-scale and continuous monitoring of snowfall. This paper presents a new snow density estimation method based on GNSS-R by matching the envelope of the SNR amplitude with that of the modeled SNR amplitude. Field experimental data are used to evaluate the proposed model based snow density estimation method. The experimental results demonstrate that the RMS of the density estimation error is 0.02gcm-3.

Global Navigation Satellite System reflectometry (GNSS-R) is a new remote sensing technique which can be used to measure a wide range of geophysical parameters. GNSS-R makes use of the simultaneous reception of the direct transmission and the coherent surface reflections of GNSS signal with either a single antenna or two separate antennas. This paper presents a snow depth estimation method using pseudorange and carrier-phase combination of BDS (BeiDou Navigation Satellite System) dual-frequency (B1 and B2) signals. The proposed method is geometry free and is not affected by ionospheric delays. The GNSS receiver of Trimble R9 was used to collect BDS satellite signals in a ground-based experimental campaign recently conducted in Harbin, Heilongjiang Province, China. The GNSS data recorded during the experimental campaign were used to test the proposed method. The results demonstrate that there exists good agreement between the proposed method and the ground-truth measurements.

Snow is an important part of freshwater resources. Accurately measuring the snow depth is of great significance for studying the hydrological cycle and preventing flood hazards. In addition to the traditional ground -based direct measurement, snow depth can also be estimated by the spaceborne or airborne remote sensing. Compared with the traditional method, the latter has advantages in resource optimization and data processing. GNSS Reflectometry (GNSS-R) as an emerging technology can be used to estimate snow depth. In this paper, we present a new method to estimate snow depth. The method combines the carrier phase observations of GPS dual-frequency (L1 and L2) obtained by the dual-receiver system. This phase combination is geometry free and is not affected by ionospheric delays. A theoretical model is established to describe the relationship between the snow depth and the spectral peak frequency of the combined phase. In the actual snow depth estimation process, the carrier phase observation data recorded by GNSS receivers are processed to obtain the spectral peak frequency which is then used to calculate the snow depth based on the developed model.

This paper first proposes a new clustering algorithm for selection of reference points (RPs) based on virtual positions of access points for indoor localization in area without linear constraints, which can not only cluster automatically but also guarantee the consistency of methods between the offline phase clustering and the online phase positioning. A new weighted algorithm based on physical distance is then presented for position determination. With angle velocity measurement provided such as by gyroscope, the weighted algorithm is particularly suited for scenarios where the mobile moves along a trajectory. The number of clusters in traditional RP clustering algorithms needs to be predefined, which means an unsuitable number of clusters would lead to poor estimation accuracy. Traditional weighted K-nearest neighbor (WKNN) algorithm weights the RPs' coordinates by the inverse of the received signal strength indication (RSSI) difference, which is not accurate enough because of the exponential relationship between RSSI and physical distance. Furthermore, methods based on probabilistic model or data fusion do not consider the uneven spatial resolution of Wi-Fi RSSI. Experimental results show that the proposed weighted algorithm considerably outperforms the K-nearest neighbor (KNN), Euclidean-WKNN, ManhattanWKNN, EWKNN, LiFS, and GPR in terms of positioning accuracy which is defined as the cumulative distribution function of position error. The results also demonstrate that RPs in indoor area without linear constraints can be clustered automatically by the proposed clustering algorithm, and cumulative distribution function of the proposed clustering algorithm outperforms KNN, WKNN, RP location clustered, and signal distance clustered.

As a cost-effective remote sensing technique, global navigation satellite system reflectometry (GNSS-R) has recently drawn significant attention from both academia and industry. However, research on GNSS-R has mainly been focused on the global positioning system which consists of only medium earth orbit satellites, while the use of geostationary earth orbit (GEO) satellites, such as those in BeiDou navigation satellite system, has received little attention. This paper investigates the GEO-satellite-based GNSS-R with a focus on the application of soil moisture retrieval. Because GEO satellites remain static with the earth, the models of the reflected GNSS signals can be considerably simplified and their signals can be used to estimate soil moisture with a high update rate such as once per hour. Two new soil moisture estimation approaches using GEO signals are proposed, which are termed GEO interferometric reflectometry (GEO-IR) and GEO reflectometry (GEO-R). Two theoretical models (linear and second order) are developed for signal-to-noise ratio (SNR)-based GEO-IR as well as for phase-based GEO-IR. Meanwhile, two empirical models (linear and second order) are developed for signal amplitude-based GEO-R as well as for SNR ratio-based GEO-R. Experimental data sets collected from three different geographical regions were used to evaluate the proposed methods. The results demonstrate that the proposed GEO-IR and GEO-R are able to monitor soil moisture reliably under bare soil condition, augmenting GNSS-R through significantly reduced processing complexity and increased temporal coverage.

Terrestrial laser scanning (TLS) is a widely used remote sensing technique which can produce very dense point cloud data very promptly and is particularly suited for surface deformation monitoring. Deformation magnitude is typically estimated by comparing TLS scans over the same area but at different time epochs of interest. However, there is an issue related to such a method, which is not clear that whether the difference between two successive surveys results from the surface deformation. Hence, it is vital to determine the minimum detectable deformation (MDD) by a TLS device with a given registration and point cloud error level. In this paper, the MDD is determined based on the computation of the point cloud error entropy. The performance of the proposed method is extensively evaluated numerically using simulated plane board deformation point clouds under a range of distances and incidence angles. This proposed method was also successfully applied to deformation monitoring of one landslide test site located in the Wuhan University of Technology. The experimental results demonstrate that the theoretical MDD has a good match with the actual deformation, and the deformation greater than MDD can be accurately detected by the TLS device.

In this paper, an approach based on Global Navigation Satellite System-Reflectometry (GNSS-R) is proposed for distinguishing sea ice and water from each other. The Delay-Doppler Map (DDM) of a GNSS signal reflected from sea ice and water show different spreading characteristics. The difference between two adjacent normalized DDMs is a differential DDM observable which provides information about the difference of two DDMs. Through studying whether the pixel number is above or below a predefined threshold, it is possible to determine the type of the reflected surface. The feasibility of the proposed differential DDM based method is validated using the ground-truth sea ice extent map provided by the National Snow and Ice Data Center, USA. The results demonstrate that the probability of detection can be up to 99.88%.

Deformation is typically estimated by comparing scans of terrestrial laser scanner (TLS) over the same area but at different time instants. However, such a method can only estimate the difference between two successive surveys, which may be caused by instrumental error and registration error instead of the real deformation, affecting the reliability of deformation monitoring. In order to improve the reliability of TLS-based deformation monitoring, it is necessary to estimate the errors in TLS measurements and determine the precursory displacements that can be detected. In this paper, the error entropy model is exploited for inspecting the threshold value in deformation monitoring, i.e. the minimum detectable deformation magnitude of a TLS. The experimental results demonstrate that deformation greater than the threshold calculated by the proposed error entropy based method can be reliably detected.

This paper presents an approach for the detection of early weak Tsunami in presence of large noise in sea surface height (SSH) measurements obtained such as using a satellite-carried global navigation satellite system (GNSS) receiver and the GNSS reflectometry (GNSS-R) technique. A sliding window moving average (SWMA) technique is proposed for detecting a Tsunami lead wave and a hypothesis testing method is developed to decide whether or not a Tsunami is present by examining the SWMA outputs against a predefined threshold. The proposed approach is evaluated using the 2011 Japan's Tsunami data collected by altimetry satellite Jason-1. Simulation results demonstrate that the proposed detect method considerably outperforms the existing methods.

This paper focuses on WiFi indoor positioning based on received signal strength, a common local positioning approach with a number of prominent advantages such as low cost and ease of deployment. Weighted k nearest neighbor (WKNN) approach and Naive Bayes Classifier (NBC) method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of them need to handle carefully the issue of access point (AP) selection and inappropriate selection of APs may degrade positioning performance considerably. To avoid the issue of AP selection and hence improve positioning accuracy, a new WiFi indoor position estimation strategy via domain clustering (DC) is proposed in this paper. Extensive experiments are carried out and performance comparison based on experimental results demonstrates that the proposed method has a better position estimation performance than the existing approaches.

This paper investigates weak tsunami detection using noisy sea surface height (SSH) measurement data such as that recorded by a satellite-borne receiver and produced by the Global Navigation Satellite System (GNSS) reflectometry (GNSS-R) technique. By studying the patterns of many real tsunamis, a triangle function is proposed to model the shape of tsunami lead waves for theoretical studies. Very similar simulation results are produced when the modeled and real tsunami data are used separately, indicating good modeling accuracy. A bin averaging (BA) technique is proposed for detecting a tsunami, and a hypothesis testing method is developed to decide whether a tsunami is present by examining the BA outputs against a predefined threshold. Mathematical formulas are derived for the probability of detection (PD) and the probability of false alarm (PFA), which can be employed as a guideline in parameter selection and performance evaluation. Simulation results using both modeled and real tsunami data demonstrate that, given a PFA of 10%, the PD can be around 60% when the wave height is 45 cm, and the SSH measurement error standard deviation (STD) is 76 cm. The results also show that, when a suitable bin length is selected, a two-stage hypothesis testing scheme and a one stage one produce very similar results. Based on the methods developed here and elsewhere, the GNSS-R technique has the potential for future tsunami detection.

This paper focuses on the estimation of tsunami-wave parameters (propagation direction, propagation speed, and wavelength) using the Global Navigation Satellite System (GNSS) reflectometry (GNSS-R)-based sea surface height (SSH) measurements. By exploiting multiple surface specular reflection tracks of GNSS signals as well as the geometry of wave propagation direction and the multiple tracks, concise mathematical expressions are derived to determine the propagation direction and speed and wavelength of a tsunami wave. Real tsunami-wave data measured by buoy sensors are employed to model GNSS-R-based SSH measurements by adding Gaussian measurement noise. The simulation results demonstrate that the proposed method can achieve a propagation direction estimation accuracy of about 4.4° and 5.9° when the SSH error standard deviations are 10 and 20 cm, respectively. The propagation speed estimation accuracies are about 12.7 and 17.7 m/s, respectively, under the same conditions when the speed ground truth is 200 m/s. The results also show that the wavelength estimation error can be as large as 100 km when the wavelength ground truth is about 400 km. Better filtering methods are needed to improve the wavelength estimation accuracy by mitigating the effect of the SSH estimation error particularly on the wave trailing edge of small negative magnitudes.

Snow is important to the ecological and climate systems; however, current snowfall and snow depth in situ observations are only available sparsely on the globe. By making use of the networks of Global Positioning System (GPS) stations established for geodetic applications, it is possible to monitor snow distribution on a global scale in an inexpensive way. In this paper, we propose a new snow depth estimation approach using a geodetic GPS station, multipath reflectometry and a linear combination of phase measurements of GPS triple-frequency (L1, L2, and L5) signals. This phase combination is geometry free and is not affected by ionospheric delays. Analytical linear models are first established to describe the relationship between antenna height and spectral peak frequency of combined phase time series, which are calculated based on theoretical formulas. When estimating snow depth in real time, the spectral peak frequency of the phase measurements is obtained, and then the model is used to determine snow depth. Two experimental data sets recorded in two different environments were used to test the proposed method. The results demonstrate that the proposed method shows an improvement with respect to existing methods on average.

With the recent advances in the theory of fractional Brownian motion (fBm), this model is used to describe the position coordinate estimates of Global Navigation Satellite System (GNSS) receivers that have long-range dependencies. The Modified Leaky Least Mean Squares (ML-LMS) algorithms are proposed to filter the long time series of the position coordinate estimates, which uses the Hurst parameter estimates to update the filter tap weights. Simulation results using field measurements demonstrate that these proposed modified leaky least mean squares algorithms can outperform the classical LMS filter considerably in terms of accuracy (mean squared error) and convergence. We also deal with the case study where our proposed algorithms outperform the leaky LMS. The algorithms are tested on simulated and real measurements.

This study investigates identifying forest condition changes using Global Navigation Satellite System (GNSS) signal measurements obtained in a recently conducted airborne experiment. Received signal strength (i.e. the peak correlation power) of the reflected signal is used to distinguish ground surfaces or forest conditions from each other. A simple threshold based method is developed to perform the identification of forest condition change. Through processing the logged airborne experimental data, it is observed that GNSS signal power can be used to reliably and accurately identify abnormal conditions in a forest.

The noise in GPS coordinate time series is known to follow a power-law noise model with different components (white noise, flicker noise, and random walk). This work proposes an algorithm to estimate the white noise statistics, through the decomposition of the GPS coordinate time series into a sequence of sub time series using the empirical mode decomposition algorithm. The proposed algorithm estimates the Hurst parameter for each sub time series and then selects the sub time series related to the white noise based on the Hurst parameter criterion. Both simulated GPS coordinate time series and real data are employed to test this new method; the results are compared to those of the standard (CATS software) maximum-likelihood (ML) estimator approach. The results demonstrate that this proposed algorithm has very low computational complexity and can be more than 100 times faster than the CATS ML method, at the cost of a moderate increase of the uncertainty (~5%) of the white noise amplitude. Reliable white noise statistics are useful for a range of applications including improving the filtering of GPS time series, checking the validity of estimated coseismic offsets, and estimating unbiased uncertainties of site velocities. The low complexity and computational efficiency of the algorithm can greatly speed up the processing of geodetic time series.

In the analysis of some specific time series (e.g., Global Positioning System coordinate time series, chaotic time series, human brain imaging), the noise is generally modeled as a sum of a power-law noise and white noise. Some existing softwares estimate the amplitude of the noise components using convex optimization (e.g., Levenberg-Marquadt) applied to a log-likelihood cost function. This work studies a novel cost function based on an approximation of the negentropy. Restricting the study to simulated time series with flicker noise plus white noise, we demonstrate that this cost function is convex. Then, we show thanks to numerical approximations that it is possible to obtain an accurate estimate of the amplitude of the colored noise for various lengths of the time series as long as the ratio between the colored noise amplitude and the white noise is smaller than 0.6. The results demonstrate that with our proposed cost function we can improve the accuracy by around 5% when compared with the log-likelihood ones with simulated time series shorter than 1400 samples.

This paper presents an enhanced least-squares positioning algorithm for locating and tracking within indoor environments where multipath and nonline-of-sight propagation conditions predominate. The ranging errors are modeled as a zero-mean random component plus a bias component that is assumed to be a linear function of the range. Through minimizing the mean-square error of the position estimation, an expression for the optimal estimate of the bias parameter is obtained. Both range and pseudo-range-based positioning are considered. Simulations and experimentation are conducted which show that a significant accuracy gain can be achieved for range-based positioning using the enhanced least-squares algorithm. It is also observed that the pseudo-range-based least-squares algorithm is little affected by the choice of the bias parameter. The results demonstrate that the experimental 5.8-GHz ISM band positioning system can achieve positional accuracy of around half a meter when using the proposed algorithm.

In this paper we investigate near sea surface wind speed estimation using GNSS (Global Navigation Satellite System) signals. A low-altitude airborne experiment was conducted recently using a UNSW-owned light aircraft over the coast of Sydney. Both direct and reflected signals were captured via a zenith-looking antenna and a nadir-looking antenna respectively. The logged IF data bits were processed to generate delay waveforms and delay-Doppler waveforms. Using the measured waveforms and the theoretical ones, the wind speed can be estimated through waveform fitting. The processed waveforms associated with eight satellites were employed to estimate the wind speed. A four-step method is proposed to perform the waveform fitting. The results show that similar estimation accuracy can be achieved using signals transmitted from satellites with low or high elevation angles. It is demonstrated that the estimation accuracy of the wind speed is around 1 m/s. Further the incorrect encoding of some quantized data bits in a software receiver is investigated.

This paper presents an improved positioning approach for cellular-network based mobile tracking in severe non-line-of-sight (NLOS) propagation environments. The proposed approach consists of two stages: the smoothing stage to suppress the NLOS errors in the distance measurements; and the position tracking stage. An improved distance smoothing method is proposed to significantly reduce the NLOS errors. It applies online distance mean and variance estimates to identify LOS and NLOS propagations. The online LOS and NLOS identification results, the distance mean and variance estimates are employed to update the Kalman filter (KF) for smoothing distance measurements. A data fusion technique is developed to combine distance measurements, mobile velocity and heading angle estimates provided by motion sensors through the extended KF. Simulation results demonstrate that the proposed two-stage approach significantly improves position accuracy compared to the existing NLOS mitigation algorithms, at the cost of increased computational complexity.

This paper presents a method of determining the statistical positional accuracy of a moving object being tracked by any 2D (but particularly radiolocation) positioning system without requiring a more accurate reference system. Commonly for testing performance only static positional errors are measured, but typically for radiolocation systems the positional performance is significantly different for moving objects compared with stationary objects. When only the overall statistical performance is required, the paper describes a measurement technique based on determining 1D cross-track errors from a nominal path, and then using this data set to determine the overall 2D positional error statistics. Comparison with simulated data shows that the method has good accuracy. The method is also tested with vehicle tracking in a city and people tracking within a building. For the indoor case, static and dynamic measurements allowed the degrading effect of body-worn devices due to signal blockage to be determined. Error modeling is also performed and a Rayleigh-Gamma model is proposed to describe the radial positional errors. It is shown that this model has a good match with both indoor and outdoor field measurements.

This paper presents positioning algorithms for cellular network-based vehicle tracking in severe non-line-of-sight (NLOS) propagation scenarios. The aim of the algorithms is to enhance positional accuracy of network-based positioning systems when the GPS receiver does not perform well due to the complex propagation environment. A one-step position estimation method and another two-step method are proposed and developed. Constrained optimization is utilized to minimize the cost function which takes account of the NLOS error so that the NLOS effect is significantly reduced. Vehicle velocity and heading direction measurements are exploited in the algorithm development, which may be obtained using a speedometer and a heading sensor, respectively. The developed algorithms are practical so that they are suitable for implementation in practice for vehicle applications. It is observed through simulation that in severe NLOS propagation scenarios, the proposed positioning methods outperform the existing cellular network-based positioning algorithms significantly. Further, when the distance measurement error is modeled as the sum of an exponential bias variable and a Gaussian noise variable, the exact expressions of the CRLB are derived to benchmark the performance of the positioning algorithms.

In the above-cited article, which appeared in the IEEE Transactions on Mobile Computing, vol. 11, no. 2, pp. 254-263, February 2012, the authors wish to clarify that the authors listed in reference [10] are incorrect.

This paper presents a new approach for smoothing long time series of position estimates of ground GNSS (global navigation satellite system) receivers. The fractional Brownian motion (fBm) model is employed to describe the position coordinate estimates that have long-range dependencies. A new and low-complexity method is proposed to estimate the Hurst parameter and the simulation results show that the new method achieves good accuracy and low complexity. A modified leaky least mean squares (ML-LMS) estimator is proposed to filter the long time series of the position coordinate estimates, which uses the Hurst parameter estimates to update the filter tap weights. Simulation results demonstrate that this ML-LMS estimator outperforms the classic LMS estimator considerably in terms of both accuracy and convergence.

In this paper we consider the inter-network interference problem in Wireless Body Area Networks (WBANs). We propose a distributed inter-network interference aware power control algorithm motivated by game theory. A power control game is formulated considering both interference between nearby networks and energy efficiency of WBANs. We derive a distributed power control algorithm called ProActive Power Update (PAPU), which can efficiently find the Nash Equilibrium representing the best tradeoff between energy and network utility. A realistic power control procedure is proposed assuming limited cooperation between WBANs. We compare our algorithm with the ADP algorithm where users are punished for interfering with others and we show that our solution can utilize energy much more efficiently by only sacrificing a small amount of network utility. In addition, we show that by adjusting the energy price, PAPU provides a methodology for application scenarios where WBANs have different energy constraints and quality of service requirements.

This paper presents positioning algorithms for cellular network-based mobile tracking in severe non-line-of-sight (NLOS) propagation scenarios. The aim of the algorithms is to enhance positional accuracy of network-based positioning systems when the GPS receiver does not perform well due to the hostile environment. Two positioning methods with NLOS mitigation are proposed. Constrained optimization is utilized to minimize the cost function which takes account of the NLOS error. Mobile velocity and heading angle information is exploited to greatly enhance position accuracy. It is observed through simulation that the proposed methods significantly outperform other cellular network based positioning algorithms. Further, the exact expressions of the CRLB are derived when the distance measurement error is the sum of an exponential and a Gaussian variable.

This paper focuses on wireless capsule endoscope magnetic field based localization by using a linear algorithm, an unconstrained optimization method and a constrained optimization method. Eight sensor populations are employed for performance evaluation. For each of five sensor populations, four different sensor configurations are investigated, which represent potential sensor placements in practice. Accuracy is evaluated over a range of noise standard deviations and the position area is set on a solid cylinder which well represents the realistic scenario of the human body. It is observed that the optimization method greatly outperforms the linear algorithm that should not be used alone in general. The constrained optimization approach outperforms the unconstrained optimization method in presence of large noise. Simulation results show that best position accuracy is achieved when the sensors are uniformly deployed on a 2D plane with some sensors on the boundary of the position area. For the sensor populations considered, when increasing sensor population by one, the accuracy improves by about 0.45 divided by the sensor population. The results provide useful information for the design of wireless endoscope localization systems.

The performance of the peak and leading edge detection methods for time-of-arrival (TOA) estimation in band-limited systems is examined. Analytical expressions for the detection performance in the presence of both random noise and multipath interference are derived. A dimensionless performance factor is presented that allows simple comparisons of the TOA estimation algorithms. These equations allow the performance tradeoff analysis to be undertaken without the need for simulations. It is shown that the leading edge detection method has significantly better multipath mitigation characteristics than the peak detection one, but at the expense of inferior noise performance.

A hybrid anchor-free localisation scheme for multihop wireless sensor networks is presented. First, a relatively dense group of nodes is selected as a base, which are localised by using the multidimensional scaling method. Secondly, the robust quads (RQ) method is employed to localise other nodes, following which the robust triangle and radio range (RTRR) approach is used to perform the localisation task. The RQ and the RTRR methods are used alternately until no more nodes can be localised by the two approaches. Simulation results demonstrate that the proposed hybrid localisation algorithm performs well in terms of both accuracy and the success rate of localisation. To evaluate the accuracy of anchor-free localisation algorithms, the authors derive two different accuracy measures: the Cramer–Rao lower bound (CRLB) to benchmark the coordinate estimation errors and the approximate lower bound to benchmark the distance errors. Simulation results demonstrate that both the CRLB and the distance error lower bound provide references for the accuracy of the location algorithms.

Accuracy of ordinary sensor node localization in wireless sensor networks mainly depends on the signal parameter such as time-of-arrival and signal strength estimation errors and the accuracy of the anchor node locations. In this paper a low- complexity but efficient algorithm is derived to improve anchor location accuracy in the presence of both anchor-to-anchor distance and AOA estimates and GPS measurements. Also, a Lenvenberg-Marquardt (LM) optimization based algorithm is developed for accuracy improvement when anchor-to-anchor distance estimates and GPS measurements are provided. Further, we derive the Cramer-Rao lower bound (CRLB) to benchmark the anchor position accuracy. To our knowledge, improving anchor node location accuracy and deriving the CRLB in the presence of both GPS and anchor-to-anchor measurements in 3-D scenarios are not reported in the literature. Simulation results demonstrate that the proposed approaches can improve the anchor position accuracy substantially and that the accuracy of the two developed algorithms approaches the corresponding CRLB.

This paper addresses the problem of identifying NLOS propagation by applying the statistical decision theory. A time-of-arrival (TOA) based method is developed under idealized conditions to provide a performance reference. In the presence of both TOA and received signal strength (RSS) measurements, a joint identification method is derived to efficiently exploit both the TOA and RSS measurements. Analytical expressions for the probability of detection (POD) and the probability of false alarm (PFA) are derived. Simulation results demonstrate that the proposed methods perform well and the joint TOA and RSS based method outperforms the TOA based methods considerably. It is also shown that the analytical results agree with the simulated ones.

Thin paper presents the performance analysis of time-of-arrival (TOA) measurements by employing bandlimited radio signals. We choose one of the practical TOA estimation methods, peak tracking for study. First, two simplified models, i.e. the hyperbolic and Gaussian models are introduced to approximate the correlation diagram (correlogram) for ease of performance analysis. It is shown that the two models accurately approximate the true bandlimited correlogram especially around the peak. Concise expressions of the TOA estimation errors are derived for either Gaussian measurement noise or multipath interference when using bandlimited signals. The analytical results can be readily exploited to assist the design of TOA based positioning systems using peak tracking algorithm under bandwidth constraints.

In this paper a hybrid localization scheme for multihop wireless sensor networks is presented. At first a relatively dense group of nodes is selected as a base. Next, the multidimensional scaling (MDS) method is applied to localize the group of nodes. Then, the robust quads (RQ) method is employed to localize other nodes, following which we make use of the robust triangle and radio range (RTRR) approach to perform the localization task. The RQ and the RTRR methods are used alternately until no more nodes can be localized by the two approaches. Simulation results demonstrate that the proposed hybrid localization algorithm performs well in terms of both accuracy and success rate of localization.

In this paper, we propose a modified two stage Taylor series (TS) method for position estimation in a 3-D environment when either the time-difference-of-arrival (TDOA) or the distance measurements are available. It is aimed to improve the convergence performance of the traditional Taylor series method. Simulation results demonstrate that the modified TS method can improve the position estimation convergence considerably.

This work deals with the development of pre-filtering techniques for low-cost devices using high data rate communications. Many positioning algorithms have been recently revisited in a centralized architecture scenario, where a cheap mobile sensor is surrounded by N base stations. The overview of the system is composed of two blocks: a smoothing filters and a positioning block. In the smoothing filters block, different algorithms such as smoothing filter, recursive least squares and maximum-likelihood are developed to process multiple time-of-arrival measurements before triangulating the position of the mobile sensor. The positioning algorithms are the Taylor series, direct method and spherical interpolation. All in all, it is shown that the recursive processing of multiple measurements at the input of the positioning algorithm improves not only the accuracy of the triangulated position, but also the robustness of the positioning algorithms. We also explain why the very good results given by the maximum- likelihood should only be seen as a lower-bound of the system.

In the paper we consider location estimation in an non-line-of- sight (NLOS) environment. A constrained optimization based location algorithm is proposed to jointly estimate the unknown location and bias by using the sequential quadratic programming (SQP) algorithm. This method does not rely on any prior statistics information, and simulation results show that the proposed method outperforms the existing related methods considerably. To reduce the complexity of the SQP based algorithm, we further propose a Taylor-series expansion based linear quadratic programming (TS-LQP) algorithm. Simulation results demonstrate that the computational complexity of the TS-LQP algorithm is only a fraction of that of the SQP algorithm while the accuracy loss is marginal.

Most radio positioning methods are based on the measurements of distance between different wireless nodes. Owing to the existence of non-line-of-sight (NLOS) radio propagation, unfortunately, not all the measured distances are reliable. One way to tackle the problem of positioning is therefore to take two-steps: (i) identifying the NLOS measurements; (ii) smart signal processing of the mixed LOS and NLOS measurements. This paper is focused on the second issue. Under the assumption that the NLOS measurements have been identified, we first propose a simple method to suppress the effect of the NLOS error. Simulation results demonstrate that the proposed method achieves similar or better accuracy than several other known methods and the computational complexity is reduced considerably. We also present an optimal location estimator under the assumption of Gaussian distributed measurement noise and Rayleigh distributed NLOS error. Although it is difficult to achieve the optimal performance in practice due to modeling uncertainties, the optimal estimator provides a performance benchmark.

In the paper we investigate 3-D positioning by making use of distance/range and angle-of-arrival (AOA) measurements. Two estimation methods (linear least squares (LS) estimator and optimization) are developed for node positioning in wireless networks. We derive the Cramer-Rao lower bound (CRLB) for positioning with both range and AOA measurements in a 3-D environment, which is not seen in the literature. Also we derive compact approximate expressions of the variances of the LS algorithms. In the literature, positioning accuracy is usually studied by assuming perfect anchor location information. To evaluate positioning accuracy under realistic conditions, we also analyze the impact of anchor position error. Numerical results demonstrate that the derived analytical results have a good match with the simulated results.

Mobile positioning has drawn significant attention in recent years. In dealing with the non-line-of-sight (NLOS) propagation error, the dominant error source in the mobile positioning, most previous research in this area has focused on the NLOS identification and mitigation. In this paper, we investigate new positioning algorithms to take advantage of the NLOS propagation paths rather than cancelling them. Based on the prior information about the NLOS path, a least squares based position estimation algorithm is developed and its performance in terms of root mean square error (RMSE) is also analyzed. Furthermore, the maximum likelihood based algorithm is presented to jointly estimate the mobile's and scatterers' positions. The Cramer-Rao lower bound on the RMSE is derived for the benchmark of the performance comparison. Finally, the performances of the proposed algorithms are evaluated analytically and via computer simulations. Numerical results demonstrate that the simulated results closely match the derived analytical results.

This paper presents a subspace-based algorithm for the simultaneous estimation of angle of arrivals (AOAs) and angle of departures (AODs) from an estimated spatial signature at the receive antenna array. The algorithm exploits a 2-D unitary ESPRIT-like technique to separate and estimate the phase shifts due to the AOAs and AODs with automatic pairing of the two parameter sets. The Cramer-Rao lower bound (CRLB) on the AOAs and AODs estimates is provided. The performance of the algorithm is illustrated based on the computer simulation. It shows that the proposed algorithm is an efficient estimator which is able to achieve the CRLB

This letter focuses on the performance analysis of the decorrelating receiver in multipath Rician faded CDMA channels. M-ary QAM scheme is employed to improve the spectral efficiency. Approximate expressions are first derived for the two performance indexes: the average symbol error rate (SER) and the average bit error rate (BER) when the decorrelating-first receiver perfectly knows the channel information of the user of interest. To achieve desirable closed-form expressions of the SER and the BER, we exploit results in large system analysis and make assumptions of a high signal-to-interference ratio (SIR) and/or a small Rician K-factor. To measure the receiver performance in the practical scenario, we further derive expressions to approximate the average SER and BER of the decorrelating-first scheme with channel uncertainty. Simulation results demonstrate that the analytical results can also be employed to evaluate the performance of the combining-first receiver.

This paper focuses on the performance analysis of the decorrelating multipath combining receiver in multipath Rician faded CDMA channels. M-ary QAM scheme is employed to improve the spectral efficiency. Approximate expressions are first derived for the two performance indexes: the average symbol error rate (SER) and the average bit error rate (BER) when the receiver perfectly knows the channel information of the user of interest. To achieve desirable closed-form expressions of the SER and the BER, we exploit results in large system analysis and make assumptions of a high signal-to-interference ratio (SIR) and/or a small Rician K-factor. To measure the receiver performance in the practical scenario, we further derive expressions to approximate the average SER and BER with channel uncertainty. The pilot-symbol aided linear minimum mean-square channel estimator is considered to obtain an accurate channel estimate and to obtain an expression for the variance of the channel estimation error required for calculating the error rate. All analytical results are compared with simulation results

This letter investigates the performance analysis of a linear minimum mean square error receiver for M-ary quadratic-amplitude modulation in multipath fading channels. Both channel gain variances and instantaneous channel gains of the interferers are considered for receiver implementation. Approximate expressions for symbol and bit error rates are derived only when the receiver knows the channel gain variances of the interferers. In deriving the analytical expressions, we exploit large system analysis and results in single-user multipath combining. The receiver performance and the accuracy of the theoretical results are examined via simulations.

We derive upper bounds on the symbol error probability for a communication system that sends quadrature amplitude modulated data over a frequency-flat Rayleigh fading channel. We first derive simple error bounds in terms of a key parameter, namely, the channel estimation error variance. We move on to derive expressions for the this parameter for a pilot symbol assisted channel estimation scheme. The estimation error variance, and thus the symbol error probability, are expressed succinctly in terms of the statistics of the channel fading process, the frequency of insertion of pilot symbols, and the average signal-to-noise ratio.

Recently a number of modified MMSE receivers have been efficiently applied to code-division multiple-access (CDMA) communications with dynamic fading channels. These receivers can successfully cope with multiple access interference (MAI) but are limited to BPSK signals. This paper presents new adaptive implementations of MMSE receivers for higher order signals in multi-user environments. High-order signal constellations, e.g. MQAM, have been extensively investigated in single-user fading channels due to their high spectral efficiency. This paper provides performance evaluations and analysis for the proposed adaptive multiuser receiver. It also presents a new computationally efficient adaptive algorithm for these high-order signal constellations.