The Global Navigation Satellite System (GNSS) provides continuous high-precision positioning, enabling many applications such as vehicle navigation and pedestrian monitoring. However, in challenging environments such as urban canyons, positioning accuracy is significantly degraded due to multi-path and non-line-of-sight (NLOS) issues. To tackle this issue, we introduce a single point positioning (SPP) framework based on pseudorange correction, including three modules: feature extraction, pseudorange correction, and positioning model. Heavy pseudorange error is the primary cause of inaccurate localization, and in particular, we introduce GNSSFormer, a transformer-based model to obtain the pseudorange correction values. After preprocessing raw observations of GNSS, we designed a pseudorange correction model GNSSFormer containing a temporal transformer block and a multi-satellite joint spatial transformer block. GNSSFormer extracts multiple features related to both satellites and receivers, learning the complex global relationships between these features and the pseudorange errors to derive correction values. Subsequently, an extended Kalman filter (EKF)-based rauch tung striebel (RTS) smoothing SPP algorithm is employed to determine the location. Validation demonstrates that GNSSFormer significantly enhances SPP performance compared to state-of-the-art algorithms, improving positioning accuracy at least by 29.42% and 28.40% on two open source datasets and 18.91% on real-world dataset.

The utilisation of global navigation satellite system reflectometry (GNSS-R) signals in remote sensing of land surface parameters has undergone significant advancements over the years. However, a paucity of analysis exists regarding the vegetation-transmitted GNSS signal, which represents an avenue for further research. In this study, an empirical investigation was conducted to ascertain the statistical characteristics of the power associated with GNSS signals emitted from vegetation, and the most appropriate distribution function model was obtained by a combinatorial test. The experimental results indicate that the vegetation-transmitted GNSS signal continues to conform to the characteristics of a Normal (right-hand circular polarization, RHCP) and Weibull (left-hand circular polarization, LHCP) distribution; however, significant variations are observed in the distribution parameters and the parameter value ranges. Furthermore, the results suggest a positive correlation between the k-order (k= 1, 2, 3, 4) moment order and the discrepancy in signals obtained by disparate GNSS antennas. Both antenna elevation angle and vegetation type exert an influence on moments of all orders, and the influence of the latter is more pronounced, thereby enabling the differentiation of vegetation types.

Global Navigation Satellite System-Reflectometry (GNSS-R) provides an effective remote sensing technique for accurate retrieval of sea surface height (SSH) measurements. However, accuracy is severely affected by environmental disturbances such as wind-induced sea clutter and wave interference, degrading Delay-Doppler Map (DDM)-derived measurements. In this study, we propose an advanced trajectory-based deep learning model, Crossformer, explicitly designed to capture temporal dependencies inherent in GNSS-R sequential data. The method leverages five distinct DDM features: Peak Power Point (PPP), Maximum Slope Point (MSP), Center Pixel Intensity (CPI), Average Power Point (APP), and Kurtosis (KUR). A dimension-segment-wise embedding technique combined with a two-stage attention mechanism effectively models both temporal and cross-dimensional correlations. Evaluation using CYGNSS data validated against Jason-3 Level 2 measurements demonstrates the superior performance of our approach, yielding a Root Mean Square Error (RMSE) of 0.93 m, Mean Absolute Error (MAE) of 0.65 m, and a coefficient of determination (R2) of 0.9901. Comparative analyses with baseline methods confirm significant improvements in robustness and predictive accuracy, particularly across varying sea states. This research underscores the potential of advanced temporal modeling techniques in GNSS-R altimetry applications.

A novel basic observable, termed the alternating-current texture (ACT), is defined from the original linear-polarization carrier-to-noise ratio (CNR) of Global Navigation Satellite System (GNSS) for monitoring wind vector. The amplitude distribution (AD), autocorrelation function (ACF), power spectral density (PSD), and fractal dimension (FD) of the ACT are explored. The results show that these statistical characteristics derived from the GNSS-IR CNR resemble those directly extracted from the reflected signal. Twelve statistics from the AD, ACF, PSD, and FD, as observables sensitive to wind speed, are analyzed. The findings suggest that these statistics exhibit geometric dependence, especially at the low elevation angle. Furthermore, the skewness of the AD, the correlation time of the ACF, PSD peak, PSD width, and fractal dimension respond more effectively to wind speed than other statistics, and are used to assess the capability of retrieving wind speed. Kernel principal component analysis (KPCA) is employed to fuse these statistics to produce a new sensitive observable to wind speed. When elevation and azimuth angles are confined to optimal regions with minimal interference, a root mean square error (RMSE) of 1.57 m/s is obtained with a minute-level temporal resolution. In contrast, a geodetic GNSS receiver provides an RMSE of 2.55 m/s. Additionally, based on the anisotropy of sea surface, the feasibility of these statistics in retrieving wind direction is investigated. Novel sensitive observables to wind direction, derived from the best-fit ellipse to the spatial distributions of the statistics, are defined. The fusion using KPCA achieves the best determination coefficient (DC) of 0.50 with wind direction, while the geodetic receiver yields a DC of only 0.16. These results conclude that low-cost GNSS sensors can be utilized for retrieving wind vector, and the linear-polarization GNSS-Interferometric Reflectometry (GNSS-IR) outperforms its right-handed circular polarization (RHCP) counterpart.

This paper revisits the Interferometric Complex Field (ICF) concept in coastal Global Navigation Satellite System-Reflectometry (GNSS-R), and proposes a novel baseband signal processing scheme for monitoring sea states. The scheme utilizes incoherent averaging to reduce hardware complexity while maintaining accuracy comparable to correlation-based methods. This eliminates requirements for the RHCP antenna, RF front-end, and baseband processor of the direct signal. Compared to the existing schemes, it has lower computational complexity, power, and cost. The primary observable, termed the alternating-current incoherent average power (aIAP), is derived by detrending a stable baseline component from the output of the proposed scheme. The coherence time and spectral width from the aIAP time series are defined as the observables of retrieving sea state, specifically wind speed and significant wave height (SWH). Three experimental data sets are used to demonstrate and assess the proposed scheme. Results indicate that the coherence time and spectral width of aIAP exhibit sea-state dependencies similar to those of alternating ICF within a wind speed range of 0 ∼ 15 m/s, and thus can be used to retrieve sea state. Spectral width retrieves sea state more effectively than coherence time. Coherence time and spectral width weakly depend on elevation angle so that an elevation correlation is unneeded. The proposed scheme, as a cost-efficient alternative, has the potential for operational sea state monitoring.

The potential of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) is explored for detecting and estimating global mesoscale oceanic eddies. A fractional Fourier transform-residual network (F-ResNet) model is used to achieve centimeter-level absolute dynamic topography (ADT), demonstrating a root mean square error (RMSE) of 5.05 cm, which is subsequently used for detecting oceanic eddies based on their height characteristics. Using CYGNSS data from 2020, comprising over 750 million samples, the research demonstrates that GNSS-R technology effectively supports daily oceanic eddy detection, achieving precision rates of 50.63% and 47.80%, recall rates of 89.95% and 90.48%,$F1$scores of 64.79% and 62.56%, and accuracy rates of 47.92% and 45.52% for cyclonic and anticyclonic eddies, respectively. Although the method proves both effective and feasible, its performance remains suboptimal. To improve detection accuracy, the study explores high-bandwidth signals, whose sharper autocorrelation function enhances ADT retrieval and improves oceanic eddy detection accuracy. Simulation results from the ACE-BOC (15, 10) signal suggest precision rates of 85.51% and 84.14%, recall rates of 91.93% and 91.11%,$F1$scores of 88.76% and 87.48%, and accuracy rates 79.80% and 77.75% for cyclonic and anticyclonic eddies, respectively. Moreover, supplementary metrics for eddy features indicate significant improvements with high-bandwidth signals. The application of these two signals for sustained, long-term monitoring of oceanic eddies in a specific region is also demonstrated, providing a foundation for eddy tracking. The tracking results show that the ACE-BOC (15, 10) signal can effectively track eddy trajectories, achieving an RMSE of 1.11 km, whereas CYGNSS yields an RMSE of 8.4 km. Finally, this article discusses the characteristics of ACE-BOC and carrier-altimeter signals, highlighting the potential of advanced spaceborne GNSS-R altimeters and discussing future directions in this field.

This study provides statistical characteristics of the power of the reflected GNSS signals and establishes associations between probability density function (PDF) characteristics and different land cover situations. The feasibility of utilizing the PDF of signal power for determining the presence of vegetation cover and inverting associated parameters has been demonstrated. The simulation demonstrates that the signal-to-noise ratio (SNR) of land surface-reflected signals from various reflectors follows a Weibull distribution for the dual-antenna model, and an F distribution for the single-antenna model. The moment-generating function is used for the calculation of 1st-4thorder moments to study the characteristics of the PDF. Furthermore, the distribution parameters and moments are influenced by both the land-cover type and the reflector’s physical parameters. Experiments were conducted on mud flats and farmland for four months to validate the simulation model utilized in this study. Moreover, the simulation and experimental results demonstrate a mathematical correlation between the moments of the PDF and land surface parameters such as soil moisture content (SMC), soil roughness, and vegetation density.

This article presents the statistical analysis of the reflected global navigation satellite system-reflectometry (GNSS-R) signal from a coastal experiment, including the non-Gaussianity, probability distribution functions, autocorrelations, and fractal dimensions of the speckle and texture components. The results clearly show that the amplitude distribution is modeled well by a Weibull model. The texture component of the reflected GNSS signal has a log-normal distribution. Due to the presence of the coherent and non-coherent components, the phase of the reflected signal is not uniformly distributed with$\left [{{-\pi, \pi }}\right]$. The autocorrelation functions (ACFs) of the speckle and texture components both are Gaussian-shaped, with the correlation times on the order of hundreds of milliseconds and a few seconds, respectively. Some statistical properties of the reflected GNSS signal depend on GNSS-R geometry and sea state; therefore, once the influence of GNSS-R geometry is corrected, they can be used to determine sea state. The speckle and texture correlation times of the reflected GNSS signal, as an example, are used to retrieve wind speed. The speckle and texture correlation times provide retrieved wind speeds with root mean square errors (RMSEs) of 1.66 and 1.75 m/s. When a minimum variance estimator is used to fuse two retrieved wind speeds, the RMSE is reduced to 1.46 m/s. The work is helpful for developing a GNSS signal scattering model over the sea surface and further studies on coastal GNSS-R to monitor sea state and maritime target.

Spaceborne global navigation satellite system reflectometry has been used to retrieve wind speed, but few studies have directly detected tropical cyclones from spaceborne GNSS-R observations. Moreover, it is challenging to solve the multivalued dependence of the spaceborne observable on wind speed to unambiguously and accurately retrieve wind speed in cyclone conditions. This paper presents a single-pass cyclone detector and coarse estimation approach of the tropical cyclone position using a spaceborne GNSS-R full delay-Doppler map (FDDM). When the specular point passes through a tropical cyclone, the FDDM asymmetry experiences an abnormal change. From the FDDM asymmetry sequence along the specular point trajectory, two subsequence features, including the slope and extremum difference, are defined, from which the tropical cyclone detector is proposed. The results from the simulation and the Cyclone GNSS (CYGNSS) data both indicate a good detection performance for tropical cyclones. The location corresponding to the peak detector is considered as a coarse estimation of the tropical cyclone position. The test result from the CYGNSS data shows that the mean error between the detected cyclone center and the International Best Track Archive for Climate Stewardship (IBTrACS) cyclone center is approximately 124.50 km. From the detector of tropical cyclones, a scene-classified wind speed retrieval model is proposed. The simulated and experimental results show that a better retrieval performance can be obtained at high wind speed (> 30 m/s) using the scene-classified model. Reductions of 10 m/s and 4 m/s in the root mean square errors (RMSEs) are obtained for the simulation and CYGNSS data, respectively. This work is meaningful for directly detecting tropical cyclones and retrieving high wind speed data using spaceborne GNSS-R in real time.

The Global Navigation Satellite System Pseudo Interference Reflectometry (GNSS-PIR) is proposed for ground-based soil moisture remote sensing in this letter. It combines the signal received by the up-looking right-hand circularly polarized (RHCP) antenna with that received by the down-looking left-hand circularly polarized (LHCP) antenna to form an oscillatory carrier to noise ratio (CNR) waveform. The analyses of waveform quality and parameters’ sensitivities to soil moisture in comparison with GNSS Interferometric Reflectometry (GNSS-IR) indicate that both the amplitude and the phase of GNSS-PIR waveform at high elevation angles are available for soil moisture determination. Soil moisture inversion based on Monte Carlo simulations shows that GNSS-PIR obtains better inversion accuracy than GNSS-IR.

The signals at higher elevation angles could be received by a left-handed circularly polarized (LHCP) antenna because LHCP components dominate the reflected signals at higher elevation angles. This letter explores a four-channel interference method of dual-antenna global navigation satellite system (GNSS) reflectometry capable and its application in retrieving the water level. Compared to single-antenna observation using a oscillating signal-to-noise ratio (SNR), due to the capability of receiving the reflected signals at high elevation angles, the proposed method has higher temporal resolution. The criterion based on the effective value of the time series is proposed to evaluate the quality of oscillating carrier phase difference. The Lomb–Scargle method and cosine fitting are used to estimate the oscillated frequency of carrier phase difference. Through normalizing cosine oscillation and time scale by the GNSS signal amplitude and wavelength, the time-series from multisatellites could be combined to retrieve the height. At last, the experiment is conducted to demonstrate the proposed method, and the results show that when the criterion threshold is under 0.2, the centimeter-level precision of the retrieved height could be achieved. Two-satellite observation combining satellite pseudorandom noise (PRN) 15 and 24 could decrease the time span of the observation to 320 s from 480 s for only using single satellite, and the root mean square error (RMSE) reduces to 6.2 cm from 9.5 and 18.9 cm of the satellite PRN 15 and 24.

The potential mymargin for improving storm surge simulation is demonstrated by using winds derived from ground-based Global Navigation Satellite System Reflectometry (GNSS-R) that uses BeiDou geostationary Earth orbit (GEO) satellite signals. We reconstruct wind fields by blending GNSS-R coastal winds with the European Center for Median Weather Forecasts (ECMWF) reanalysis product. The reconstructed winds agree well with the weather station data collected at Yangjiang in Guangdong, China. The ECMWF winds and the reconstructed winds are used to force a storm surge model off the Chinese coast during typhoon Utor 2013, respectively. The model storm surges forced by the reconstructed winds agree substantially better with tide-gauge observations than those forced by the ECMWF winds. The average error has been reduced by 30.5% from 24.3 cm with the ECMWF winds to 16.9 cm with the reconstructed winds. This letter suggests that GNSS-R coastal winds can have a positive impact on the accuracy of storm surge hindcasting directly and forecasting indirectly by improving the initial conditions.

The interferometric Global Navigation Satellite System Reflectometry (iGNSS-R) exploits the full spectrum of GNSS signal to improve the ranging performance for sea surface height applications. However, simultaneous reception of multiple satellite signals introduces the intersatellite interference degrading the altimetry accuracy, which occurs in both ground-based and spaceborne scenarios. By simulating situations of ground-based stations in Beijing and Hainan, the feasibility of ground-based iGNSS-R altimetry measurement without the intersatellite interference is verified, for more than half of the total observation areas are available and up to 34.73% and 74.64% of a day is suitable in Beijing and Hainan. Some antenna directions are recommended in the northern (southern) hemisphere: for long-time measurement, the lower (upper) parabola formed by GLONASS and the area of specular reflection points of GEO satellites are preferred; For continuous measurement, GEO satellites are recommended; For all-day measurement, the range of elevation and azimuth is 30°-60° and ±300(150°-210°).

Aiming at the problems of low resolution and poor imaging quality in global satellite system reflectometry based synthetic aperture radar (GNSS-R SAR), this paper proposes a multi-period SAR imaging method with a GPS satellite and a fixed receiver. Coherent fusion will be used in point target simulation. The result achieves the improvement of imaging resolution. In the actual scene experiment, with the larger interval of the data acquisition time, finally, the more abundant image feature information is obtained compared with the traditional bistatic SAR imaging. This method proves the vast potential in realizing local area monitoring by GNSS-R SAR.

In recent years, Global Navigation Satellite System Reflectometry (GNSS-R) has been studied as a new technique for ocean remote sensing, and the simulation of GNSS-R signals has played a very important role. In this letter, a random model for ocean reflected signal simulation of GNSS-R is presented to provide a useful method for generating reflected signals. The study is conducted by means of a superimposed result of multiple random reflected signals based on a previous fixed model. Reflected signals are randomly selected with different delay bins and different random amplitudes. To generate GNSS-R signals, a software GNSS-R simulator based on the proposed random model is developed. A receiver is also used to process the simulated signals to demonstrate the availability of the proposed model and developed simulator. The analysis shows good agreement between the produced and theoretical delay waveforms with the correlation coefficient of 0.998. To further verify the significance of the random model, the simulated reflected signals are compared with the airborne experimental data collected in Yantai, China. The comparative results show that the random model has better statistical and stochastic characteristics.

For spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R), the delay difference of direct and reflected GNSS signals among successive snapshots changes rapidly because of the high dynamics of low earth orbital and GNSS satellites. This change has to be compensated to avoid the distortion of incoherently averaged delay-Doppler map (DDM). The method to refresh the correlation window on each coherent integration time period may require too many instrument resources or too much data to be uploaded from the ground station. This letter proposes a new postprocessing approach based on the motion degradation model of DDM and the reconstruction to replace real-time compensation. Raw sampled data from UK TechDemoSat-1 are used to verify the availability of proposed approach. The results show that after reconstruction for the distorted DDM, the DDM accuracies relative to that compensated in real time are significantly improved.

In recent years, the air target detection applications by using GNSS signal have been exploited. In [1-2], the theory of target detection using GPS forward scatter radar was proposed. When the target is near GPS satellite and terrestrial receiver baseline, the detection mode is forward scattering radar, and the experimental results verify the theory[3]. In [4], the target detection had been implemented based on the shadow, which was formed by the aircraft passing through the GPS satellite and receiver baseline. In [5], different targets are detected by GPS L1 reflected signal power and signal-to-noise ratio, and a signal processing model was proposed which analyzed the target RCS characteristics. The experiment was designed in [7], which was using GPS L5 signal to detect the target successfully. In [8-9], the target detection and the signal processing had been completed by using GPS forward scattering radar. The classification of objects from their radio shadows of GPS signal was done in [10], which was based on different RCS characteristics of target.

Aiming at the problem of traffic flow detection, a method of detecting traffic flow by GNSS-R is proposed for the first time. Using the dielectric constant difference between the vehicle and the ground, it is possible to judge whether the vehicle is present in the detection area. After receiving the reflection signal in the detection area, and calculating the signal-to-noise ratio and the related power, the change of dielectric constant is analyzed by threshold truncation and region search method, and then the characteristics of vehicle can be extracted. In order to verify the technology, the relevant experiment is designed. The results show that the trend of the dielectric constant is in agreement with the traffic flow, and the GNSS-R technology can effectively detect the traffic flow density and speed.

For preventing the effects of sea ice bring the serious negative impact on the maritime transport, a full-scale detection of sea ice should be performed. The BeiDou GEO satellites could provide stable geometry and better coverage in mid- and low-latitude region where most of the sea ice occur. Based on this consideration, this paper evaluates the usage of BeiDou GEO Satellites reflected signals for accurate real-time Earth observation to study the changes in the sea surface state through remote sensing. BeiDou GEO signals received after reflection from Bohai Bay were analyzed for their sea ice content. The results are in good agreement with the cluster center of the sea ice reflected correlation power and the sea water reflected correlation power. The average of the cluster center on the sea ice surface is much smaller than that on the sea water surface.

In the application of GNSS-R technology, the reflection signal source is needed to test the receiver in order to reduce costs. The signal model is the core of GNSS-R signal source. In this paper, GPS reflection signal from sea surface is taken as the research object. Firstly, the appropriate signal phases are selected according to the correlation power distribution of the reflection signal in the ZV model. Then the equations are solved. And the result curve of the equations is approximated by numerical fitting method, so as to obtain the normalized amplitude corresponding to different phases. Finally, the time delay and doppler frequency offset of the specular point signal, with respect to the direct signal, are calculated. Thereby, three-dimensional parameters of the 100 reflection signals, namely the pseudo-code delay, Doppler frequency and amplitude, could be obtained. And it is verified by the correlation processing, which show the effectiveness of the method.

In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on soil moisture monitoring by Global Navigation Satellite System Reflected signals(GNSS-R) at the Valencia Anchor Station is introduced.

This letter focuses on exploiting parameters, including peak power spectral density (PSD), integrated power, mean frequency, and spectrum width of reflected BeiDou signals in the frequency domain, to retrieve wind speed. The PSD of reflected signals is estimated after choosing proper parameters of the Welch method. Then, the features of PSD are illustrated, and a method is proposed based on fitting estimated PSD with a Gaussian function to evaluate the aforementioned PSD parameters. The estimated parameters of collected BeiDou medium earth orbit (MEO)/inclined geosynchronous satellite orbit (IGSO) and geostationary orbit (GEO) data are fitted with in situ wind speed using polynomial. Fitting results show that the peak PSD, mean frequency, and spectrum width of reflected signals from GEO have more evident dependence on wind speed, compared with MEO/IGSO. To obtain the most accurate results, the impact of delay used in lagging direct replica to align reflected signals is analyzed. Results show that regardless of delay locating within the interval of [τ0- τc, τ0+ τc], the better root-mean-square error (rmse) of less than 1.7 m/s and the larger coefficient of determination over 0.8 can be obtained by retrieving from the spectrum width, compared with peak PSD and mean frequency, whose optimal results, with rmse values of 2.03 and 1.70 m/s and coefficient of determination values of 0.73 and 0.81, are obtained as delay is τ0- 0.85τcand τ0+ 0.9τc, respectively, where τ0is the delay of specular reflection, and τcis the length of the B1 code.

Typhoon motoring using Global Navigation Satellite System (GNSS) signals is a new application of the GNSS-R technique, with increasing interest in the last years. Examples of that can be the CYGNSS (Cyclone Global Navigation Satellite System) spaceborne mission proposed by NASA, or the TIGRIS (Typhoon Investigation using GNSS-R Interferometric Signals) experiment, conducted in the framework of ESA-China cooperation. This paper focuses on the TIGRIS experiment, presenting the preliminary results obtained using the interferometric GNSS-R (iGNSS-R) technique.

An indoor positioning system based on Receive Signal Strength Indication(RSSI) from wireless access equipment has become very popular in recent years. This system is very useful in many applications such as tracking service for older people or customer inside living communities, mobile robot localization, logistics systems etc. While outdoor environment using Global Navigation Satellite System(GNSS) and cellular network work well and are widespread used for navigation. However, there is a problem with signal propagation from satellites or cell site. They cannot be used effectively inside complex building areas or even in an urban environment. In general, the widely used method for indoor environment positioning based on Wi-Fi consists with two main categories, which are trilateration technique and location fingerprint technique(LF). It is already known that the explicit positioning performance of trilateration technique is more sensitive to noise effect than LF technique. Nevertheless, the accuracy of LF technique depends on training data set and it does not work well when environment changes. In this article, we propose the hybrid algorithm, the combination of the advantages of both systems, which is able to improve the accuracy stability and robustness. The performance of this algorithm is evaluated by the experimental results, which shows that our proposed scheme can achieve a certain level of positioning system accuracy.

The bistatically reflected global navigation satellite systems (GNSS) signals have become an attractive tool for spaceborne ocean altimetry. The interferometric processing that can exploit the full bandwidth of the available GNSS signals without the knowledge of the actual ranging codes was proposed for the PARIS IoD mission to improve the ranging precision. This letter presents a novel on-board processing method which utilizes the interferometry of partial GNSS signal components to explore a further improvement of the altimetry precision. The scheme of extracting partial signal components and interferometry procedure, for GPS L1 band as an example, are illustrated. The assessment and comparison of achievable altimetric performance of the proposed method including the altimetric sensitivity, the resolution per pulse, the signal-to-noise ratio, and the overall altimetric precision are also introduced. The extension of the proposed method to other existing and planned GNSS systems and signals would guarantee an improvement in the overall performance of the PARIS concept.

Inter-cell interference coordination (ICIC) is one of the key technologies in the multi-cell radio resource management of mobile systems. ICIC must be effectively used to reduce the mutual interference of edge users in heterogeneous network (Het-net), where the interference scenario becomes more complicated. ICIC plays a significant role in improving cell edge spectrum efficiency. In this paper, the limitation of traditional ICIC method is analyzed and a novel ICIC method based on User' position is proposed for the improvement of the downlink ICIC efficiency. By the system simulation, it is approved that the edge user throughput and system average throughput may be improved in different degree respectively.

Introducing spatial information technology into logistics for Intelligent Transportation System (ITS) can improve the logistics management and running efficiency. On the analysis of spatial information and GIS GPS RS integration technology. The paper discusses the application mode of spatial information integration technology in logistic management and freight vehicle monitoring. Furthermore, their system compositions and application frameworks are also given.

Gravity gradient based passive navigation for autonomous underwater vehicle (AUV) is proposed in This work. Its basic principle and two working modes are analyzed in detail. Simulation test has been done and the results show that the proposed method can provide high precision positioning information for AUV.

The data preprocessing, algorithm analysis and performance evaluation were done making full use of various information related to the ocean geographical features, then intelligent data fusion was used to design the underwater passive navigation system simulation module. Simulation results show that the underwater terrain matching assisted navigation can modify the error from inertial navigation system and provide precise navigation for underwater navigation system.