Remote photoplethysmography (rPPG) enables non-contact heart rate monitoring through facial videos, offering significant potential for health monitoring and telemedicine applications. Existing methods typically learn direct mappings from facial videos to rPPG signals. However, they often treat the rPPG signal holistically, overlooking the different contributions of different frequency bands, which may miss potential band-specific features and lead to inaccurate estimation and reduced robustness. To overcome this limitation, we propose PhysFlow, a novel Flow Matching framework for robust rPPG measurement. PhysFlow introduces a frequency-selective wavelet loss to emphasize physiologically important frequency bands while suppressing noise. The framework employs a highly extensible Dual-Stream Velocity Estimator to predict velocity fields from spatiotemporal and signal perspectives, integrated via an Expert Fusion mechanism. Additionally, a Wavelet Enhancement module is introduced to transform the intermediate flow state into multi-scale spectral-temporal features to facilitate velocity field prediction. Besides, classifier-free guidance is incorporated to enhance conditional control. We validate PhysFlow on four datasets, showing that our method significantly outperforms existing approaches in both intra-dataset and cross-dataset evaluations. The code is available at https://github.com/reimu996/PhysFlow/.

Remote physiological measurement enables the capture of vital signals in a non-contact way, which offers significant potential for various applications. Monitoring these signals is achieved through video cameras or radio frequency (RF) sensors, with recent few methods attempting to fuse both sources to leverage complementary patterns for enhanced accuracy. However, these two modalities operate on distinct principles, where video-based methods detect subtle facial color changes from blood volume variations, while RF-based methods capture subtle body vibration due to heartbeats. In practical applications, they may encounter interference at different occasions. Treating these modalities as equally reliable in all situations can lead to suboptimal fusion. To address this issue, we propose an evidential video-RF fusion framework for robust remote physiological signal measurement. We design an uncertainty regression head for each uni-modality, which estimates uncertainty features together with the corresponding physiological signal in each branch. Then an evidential multi-modal fusion module is employed to dynamically fuse the two modalities according to their uncertainty. Extensive experiments carried on public and self-collected datasets show that the proposed method not only achieves superior fusion performance on easy data collected under well-controlled environment, it also generalizes well to unseen data which represents challenging practical conditions that one or both sensors are disturbed.

We present CAGE (Continuity-Aware edGE) network, a robust framework for reconstructing vector floorplans directly from point-cloud density maps. Traditional corner-based polygon representations are highly sensitive to noise and incomplete observations, often resulting in fragmented or implausible layouts. Recent line grouping methods leverage structural cues to improve robustness but still struggle to recover fine geometric details. To address these limitations, we propose a native edge-centric formulation, modeling each wall segment as a directed, geometrically continuous edge. This representation enables inference of coherent floorplan structures, ensuring watertight, topologically valid room boundaries while improving robustness and reducing artifacts. Towards this design, we develop a dual-query transformer decoder that integrates perturbed and latent queries within a denoising framework, which not only stabilizes optimization but also accelerates convergence. Extensive experiments on Structured3D and SceneCAD show that CAGE achieves state-of-the-art performance, with F1 scores of 99.1% (rooms), 91.7% (corners), and 89.3% (angles). The method also demonstrates strong cross-dataset generalization, underscoring the efficacy of our architectural innovations. Code and pretrained models are available on our project page: https://github.com/ee-Liu/CAGE.git.

This paper demonstrates an automatic beam alignment system for a laser diode (LD)-photomultiplier tube (PMT) based underwater optical wireless communication (UOWC) system. Initially, the imaging characteristics of the underwater laser spot are described. Subsequently, a laser outlet estimation algorithm based on principal component analysis (PCA) is proposed. The direction of the emitted light beam is rapidly determined by reflecting it with a micro-electro-mechanical system (MEMS) mirror to align with the receiver. Experimental results validate the feasibility of the proposed beam alignment method, indicating an acceptable alignment error. Moreover, the proposed system effectively enhances the bit error rate (BER) performance, meeting the BER requirements of the forward error correction (FEC) limit when the receiver moving speed is below 0.45 m/s.

Pedestrian dead reckoning (PDR) and global navigation satellite system are two popular solutions for pedestrian navigation with a smartphone. pedestrian dead reckoning (PDR) estimates the user’s position by analyzing their walking gaits, including step length and heading angle. However, PDR position errors can accumulate over time due to measurement noise. In contrast, GNSS generates position information by processing radio signals. However, these signals can be affected by blockage and interference. GNSS and PDR are often integrated using a Kalman filter (KF) to provide a more reliable solution. While current integration methods rely on position and velocity measurements, pseudo-range measurements for PDR and GNSS integration still need to be explored. To improve the accuracy of pedestrian position estimation in urban areas, we propose a walking-gaits-aided smartphone GNSS approach. This approach involves employing a factor graph optimization (FGO)-based GNSS/ PDR tight integration method. The FGO- GNSS/ PDR tight integration considers the pseudo-range measurements from each satellite, pedestrian position, and step length to optimize the position estimation. We introduce a fuzzy adaptive FGO (A-FGO) to enhance the accuracy further to suppress pseudo-range outliers. We conducted two experiments using a Samsung Galaxy A40 and Huawei Mate 40 Pro smartphones to evaluate the accuracy of the proposed methods. Our experimental results demonstrate that the proposed methods effectively improve the PDR/ GNSS position accuracy.

The canopy cover is a fundamental parameter in forest inventory. The launch of Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) that carries the Advanced Topographic Laser Altimeter System (ATLAS) photon-counting lidar provides an astonishing opportunity to assess canopy covers at a large scale. Currently, the canopy covers were calculated as the proportion of vegetation photons to total signal photons using ICESat-2/ATLAS data without considering the radiometric distortion caused by photon-counting detectors and the surface reflectance of vegetation and ground. The overall goal of this study is to investigate a method to derive more accurate canopy covers considering the radiometric correction and surface reflectance correction with ICESat-2 photon data. With focusing on two study areas, Slaughter (SLAU) and Lenoir Landing (LENO) in USA, the specific purposes are to: 1) propose a radiometric correction model based on the lidar equation and response mechanism of photon-counting detectors to recover accurate vegetation and ground photons; 2) estimate the reflectance ratio between vegetation and ground (RVG) according to the vegetation radiative transfer model and the density of spatial cluster method; 3) derive original and compensated canopy covers with ICESat-2 classified photons; 4) evaluate the accuracy of derived canopy covers relative to local airborne reference canopy covers; and 5) explore the effects of undergrowth vegetation and land cover types on the canopy covers. The coefficients of correlation (${R}$) and root-mean-square errors (RMSEs) of the compensated canopy covers are 0.86 and 0.15 at SLAU and 0.59 and 0.16 at LENO, compared with those for original canopy covers with 0.71 and 0.18, and 0.45 and 0.21, respectively. As the undergrowth vegetation and diverse land cover types have an impact on the retrieval accuracy of canopy covers, we can employ the photons of different species to obtain their specific reflectance ratios to achieve a higher precision.

Hyperspectral LIDAR (HSL) is an innovative active remote sensing technology that allows for the simultaneous collection of spectral and spatial information. In this study, we primarily focus on the radiation correction method of the incident angle and distance effects for the backscatter intensity of HSL. We have developed a comprehensive radiometric correction model that addresses these effects. Additionally, we have applied the correction model to point cloud classification using the random forest method. Comparing the accuracy of point cloud classification before and after correction, we observed a 9.6% improvement in overall accuracy (OA) and a 10.8% improvement in the kappa coefficient. These results indicate that the radiometric correction model significantly enhances the classification accuracy.

In remote sensing domains, it is difficult to evaluate the lateral structures using the current remote sensing techniques. The mathematical peak intensity formula of the echo waveform modulated by the lateral structures establishes a quantitative yet concise relationship between the peak intensity and the lateral structures, enabling the retrieval of lateral structural details in terms of inverting the formula. The process of the retrieval includes: 1) mathematical formula derivation; 2) target shape discrimination; and 3) mathematical formula inversion. Using the sizes estimated from the simulated echo waveforms, this study demonstrates how the estimated lateral structures are affected by the number of lateral structural parameters to be solved, instrument noise, movement direction, target shape, and target size. The results reveal that for unknown target size and lateral structures, the averaged size errors are 0.56% and 4.30%, respectively. When the instrument noise is absent and only the target size is unknown, the size error averaged over four shapes is 0.3%, and the size error averaged over the square, circle, and triangle is 0.04%. When only the size is unknown, the size errors of the rectangle, square, circle, and triangle estimated by fitting the experimental peak intensity with the formula are 2.41%, 3.47%, 0.89%, and 1.42%, respectively. The small size errors prove the possibility of retrieving the lateral sizes at a centimeter-level resolution and a distance of hundreds of kilometers, which is of great practical significance in precisely mapping the lateral structures of 3-D targets using full-waveform light detection and ranging (FW-LiDAR).

Waveform decomposition is needed as a first step in the extraction of various types of geometric and spectral information from hyperspectral full-waveform LiDAR echoes. We present a new approach to deal with the ”Pseudo-monopulse” waveform formed by the overlapped waveforms from multi-targets when they are very close. We use one single skew-normal distribution (SND) model to fit waveforms of all spectral channels first and count the geometric center position distribution of the echoes to decide whether it contains multi-targets. The geometric center position distribution of the ”Pseudo-monopulse” presents aggregation and asymmetry with the change of wavelength, while such an asymmetric phenomenon cannot be found from the echoes of the single target. Both theoretical and experimental data verify the point. Based on such observation, we further propose a hyperspectral waveform decomposition method utilizing the SND mixture model with: 1) initializing new waveform component parameters and their ranges based on the distinction of the three characteristics (geometric center position, pulse width, and skew-coefficient) between the echo and fitted SND waveform and 2) conducting single-channel waveform decomposition for all channels and 3) setting thresholds to find outlier channels based on statistical parameters of all single-channel decomposition results (the standard deviation and the means of geometric center position) and 4) re-conducting single-channel waveform decomposition for these outlier channels. The proposed method significantly improves the range resolution from 60cm to 5cm at most for a 4ns width laser pulse and represents the state-of-the-art in ”Pseudo-monopulse” waveform decomposition.

The signal strength is a fundamental parameter in radiometric applications for satellite lidars. Different from full-waveform lidars, photon-counting lidars cannot record the returned signal strength but only respond to the presence of the photon event and may miss some returned photons due to the dead time effect, i.e., introduce radiometric distortion. Based on the lidar equation and the response mechanism of photon-counting detectors, we propose a radiometric correction model to remove the impact of the nonlinear response and dead time of detectors for the photon-counting lidar borne on ICESat-2. The returned signal photon number is corrected by the proposed model with respect to the photon event number per shot (PNPS) and surface slope derived from ATL03/ATL08 products. Then, the optical throughput calibration factor of ICESat-2 is obtained from ATL06 products over high Antarctic plateau where has given reflectance and clear atmosphere, which is generally equal to 0.52. The atmospheric attenuation induced by the molecular, cloud, and aerosol is calculated from ATL09 products. In addition, the corrected radiometric parameters including the calculated surface reflectance and apparent surface reflectance (ASR) are applied to classify land cover types along laser tracks over snow-covered terrains. The results indicate that the signal strength and calibration constant are reliable after corrections, but the atmospheric attenuation is sometimes inaccurate, which further influences the derived surface reflectance. In classifications, the overall accuracy and Kappa coefficient based on the corrected ASR can achieve the best classification results with 88.80% and 0.69. The proposed radiometric correction model is very essential to radiometric applications for photon-counting lidars such as ICESat-2, especially for data captured on ice and bare land with relatively high reflectance.

Convolutional neural networks (CNN) are capable of extracting features with high accuracy, which is dominant in visual-based classification. Previous researches demonstrate that CNN can extract essential features of the target in the plant feature extraction and classification. Hyperspectral LIDAR (HSL) is a novel active remote sensing technology that can simultaneously collect spectral and spatial information. This paper proposed a novel classification method named VI-CNN for hyperspectral LiDAR, which combines the spectral features with the vegetable index(VI). As far as we know, we are the first to apply CNN to HSL data classification. The VI -CNN is divided into two parts. Firstly, spectral CNN focuses on intra-spectral correlations; secondly, the vegetation indices supplement the biological parameters. The evaluation shows that the concatenation has stronger identification and robustness than standalone methods. The experimental results demonstrate that the VI-CNN significantly improves the classification accuracy against other traditional machine-learning methods.

Position and location constitute critical context for Internet of Things (IoT) devices. Global navigation satellite systems (GNSSs) are the primary apparatus providing precise position and location information for IoT devices in outdoor environments. However, in dense urban areas, non-line-of-sight (NLOS) signals will induce large errors in GNSS pseudorange measurements due to the additional signal transmission paths. The vector tracking (VT) technique utilizing a Kalman filter (KF) to estimate navigation solutions has been investigated in NLOS detection, and its advantages have been demonstrated. However, the estimation of NLOS-induced bias has not been thoroughly investigated in the VT framework. In this article, we focus on the estimation and correction of NLOS-induced errors within the VT framework. First, graph optimization (GO) instead of a KF is incorporated with VT to optimize the estimation of navigation solutions. The NLOS-induced bias is then added to the VT state vector as the variable for real-time estimation. Compared with the KF-VT method, in GO-VT, the state transformation and the measurement model are regarded as constraints to optimize the state vector estimation. Hence, the GO-VT framework is more flexible than the KF approach in dealing with state vector changes. An iterative process is conducted to solve for the optimization results; a multiple-correlator scheme is employed in GO-VT to provide the initial values of the NLOS-induced bias. Three collected GPS L1 data sets (static and dynamic) are used to evaluate the proposed method. The statistical results support the conclusion that GO-VT with state augmentation achieves superior position estimation in urban areas.

Airborne hyperspectral images are used for crop identification with a high classification accuracy because of their high spectral resolution, spatial resolution, and signal-to-noise ratio (SNR). However, the tradeoffs between the three core parameters of a hyperspectral imager (SNR, spatial resolution, and spectral resolution) should be considered for designing an efficient imaging system. Only a few reported studies on the analysis of the impact of SNR on identification accuracy are available. Further, the tradeoffs and mutual interactions among these parameters are rarely considered. In this empirical study, our aim was to understand the relationship among the core parameters and their effects on crop identification accuracy by analyzing the tradeoffs and mutual interactions among these parameters. We analyzed the hyperspectral images of a typical plain agricultural area in Xiongan, China, acquired by the newly developed sensor airborne multimodular imaging spectrometer (AMMIS). The fundamental images were transformed to form datasets with different ranges of spectral resolution, spatial resolution, and SNR using data reconstruction methods. We adopted the classification and regression tree (CART), random forest (RF), and k-nearest neighbor (kNN) classifiers, and observed the overall accuracy (OA) across the degraded hyperspectral datasets. The experimental results indicated that the OA decreased with a decreasing SNR. As the spectral resolution became coarser, the OA first increased, plateaued, and then decreased. However, the OA increased with decreasing spatial resolution. This study was performed with the goal of bridging the knowledge gap between the back-end hyperspectral sensor designing and its front-end applications.

Full-waveform satellite LiDAR can be used to retrieve the terrestrial surface information by decomposing its received waveforms. However, it is challenging to accurately extract the parameters of each component from a non-Gaussian overlapped waveform, which happens in steep mountain or urban areas. Therefore, a synthetic algorithm with the boosted Richardson–Lucy (RL) deconvolution, layered extraction, and gradient descent is proposed to implement the skew-normal decomposition for the received waveforms. To validate the performance of the proposed algorithm, we developed waveform decomposition experiments for three types of data, including known-parameter waveforms, simulated waveforms, and Global Ecosystem Dynamics Investigation (GEDI) satellite LiDAR waveforms. Meanwhile, we figured out the evaluation metrics involving correlation coefficients (CCs); root mean square errors (RMSEs); extracted parameter errors; and successful, missing, and unwanted rates for the decomposed waveforms. Through comparing the decomposed results of the proposed algorithm and the classical direct Gaussian decomposition (DGD) algorithms, we discovered that 1) the average CC has a growth of 4% and the average RMSE has a reduction of 60%; 2) the average errors of extracted amplitude, peak position, and pulsewidth have mitigated with 3.9%, 2.2%, and 5.1%, respectively; and 3) the successful detection rate increases by 40% and the unwanted and the missing rate decrease by 5% and 35% for the 2000 groups of known-parameter waveforms. In addition, the average CCs have slight growth of 3% and 1.2%, and the average RMSEs have significant reductions of 43% and 49% for the simulated and GEDI LiDAR waveforms, respectively. This research provides a preferable waveform decomposing approach conductive to characterizing the terrestrial information from the overlapping skew-normal full waveforms.

In this paper, we present an improved framework for the instance-aware semantic segmentation of road furniture in mobile laser scanning data. In our framework, we first detect road furniture from mobile laser scanning point clouds. Then we decompose the detected pieces of road furniture into poles and their attached components, and extract the instance information of the components with different features. Most importantly, we classify the components into different categories by combining a classifier and a probabilistic graphic model named DenseCRF, which is the major contribution of this paper. For the classification of the components using DenseCRF, the unary potentials and the pairwise potentials are first obtained. The unary potentials are obtained from the classifier which takes the instance information of components as the input. The pairwise potentials are calculated considering contextual relations between components. By utilising DenseCRF, the contextual consistency of components is preserved, and the performance is significantly improved compared to our previous work. We collect three datasets to test our framework, and compare the classification performances of six different classifiers with and without DenseCRF. The combination of random forest with DenseCRF outperforms the other methods and achieves high overall accuracies of 83.7%, 96.4% and 95.3% in these three datasets. Experimental results demonstrate that our framework reliably assigns both semantic information and instance information for mobile laser scanning point clouds of road furniture.

During the mining operation, it is a critical task in coal mines to significantly improve the safety by precision coal mining sorting and rock classification from different layers. It implies that a technique for rapidly and accurately classifying coal/rock in-site needs to be investigated and established, which is of significance for improving the coal mining efficiency and safety. In this letter, a 91-channel hyperspectral LiDAR (HSL) using an acousto-optic tunable filter (AOTF) as the spectroscopic device is designed, which operates based on the wide-spectrum emission laser source with a 5-nm spectral resolution to tackle this issue. The spectra of four-type coal/rock specimens collected by HSL are used to classify with three multi-label classifiers: naive Bayes (NB), logistic regression (LR), and support vector machine (SVM). Furthermore, we discuss and explore whether Gaussian fitting (GF) method and calibration with the reference whiteboard (RB) can enhance the classification accuracy. The experimental results show that the GF technique not only improves the accuracy of range measurement but also optimizes the classification performance using the spectra collected by the HSL. In addition, calibration with RB can improve classification accuracy as well. In addition, we also discuss methods to improve the calibration-free classification accuracy preliminarily.

In this letter, a hyperspectral light detection and ranging (HSL) with 10-nm spectral resolution was designed and tested using a supercontinuum laser source. The major difference between the prototyped HSL and similar instruments was that a liquid crystal tunable filter (LCTF) was installed before the avalanche photodiode detector and utilized as a spectroscopic device. The design allowed continuous wavelength selection of the backscattered echoes in the time dimension. Moreover, for general accuracy evaluation of range measurement and spectral measurement, laboratory experiments for vegetation red edge detection were performed using the prototyped HSL to assess its feasibility on agriculture application. Yellow and green leaves from aloe and dracaena plants were measured by the LCTF-HSL for detecting the corresponding “red edge” position. Spectral profiles measured by an SVC-HR-1024 spectrometer which is designed by SVC company were used as a reference to evaluate the measurements of HSL. The comparison results showed that the red edge positions extracted from the two individual measurements were similar, thus indicating that the LCTF-based high-resolution HSL was effective for this application.

A fully polarimetric Airborne Wind Vector Scatterometer (AWVS) is developed to provide independent airborne wind vector measurements for validation of space-borne GNSS-R measurements. The scatterometer is designed to meet 1 m/s wind speed accuracy requirement. In this ad hoc and low-budget project, the instrument development exploited on some already existing subsystems. The development started from scientific system requirement definition and ended to two experiment flights for wind vector retrieval from three areas at the Gulf of Finland, the Baltic Sea. One of the flights was carried out with a simultaneous overpass of the TDS-l satellite, conducting the GNSS-R measurements. The results confirm the measurement capabilities of the GNSS-R technology and the desired 1 m/s wind speed accuracy of the developed scatterometer. Moreover, the fully polarimetric backscattering results support well other recent measurements and models of cross-polarized sea surface scattering.

Hyperspectral LiDAR (HSL) possesses the advantages of the LiDAR and the hyperspectral detection, and detects ranging and spectrum information synchronously, by one HSL system. The data fusion is also avoided. At present, the spectrum range of reported HSLs usually covers only 500 nm-1000 nm (from visual (VIS) to near infrared (NIR) band). However, there is requirement to extend the spectrum range to short wave infrared (SWIR) band, which often contains more useful spectral information. In this paper, a HSL covering the spectrum from VIS to SWIR is reported. In the HSL, the echoes are divided into two sections and are detected by the different optoelectronic devices, of which the spectral response ranges are respectively compatible to the corresponding echoes. The HSL detection experiment in the laboratory was carried out. The waveforms of the echoes were analyzed, and the spectra of different targets were measured by the HSL. The experiment results demonstrate the capability of the prototyped HSL that obtaining the ranging information and the spectrum information of the targets in VIS-SWIR bands synchronously.

Recently, a major effort has been made to develop methods or tools for rock characterization and mineral content mapping. Light detection and ranging (LiDAR) is an efficient active remote sensing technique for collecting geometry information about rock surfaces. However, traditional LiDAR sensors work with a single-wavelength laser source, and it is unfeasible to obtain spectral information using one LiDAR sensor. The combination of hyperspectral imaging and LiDAR techniques is an emerging method for acquiring spatial and spectral information simultaneously that allows remote mapping of high-resolution mineral content and distributions and identifies subtle chemical variations. Unfortunately, spatial and spectral data registration, which introduces additional complicated data processing, is an inevitable and essential issue for this method. In this letter, first, we investigate the feasibility of ore classification applications with hyperspectral LiDAR (HSL). HSL consists of 17 spectral channels covering the visible–shortwave infrared (SWIR) spectral range. Spatial and spectral information about seven different ore samples is obtained under a controlled laboratory environment using HSL. The standard deviation of the distance measurements is less than 1.1 cm for different spectral channels, and the classification accuracy can reach 100% if all 17 spectral measurements are used. To optimize the system design with lower cost and system complexity, a spectral band selection criterion is built based on the feature contribution degree (FCD), which is calculated using the normalized variance of the reflectance values for different ore samples at each wavelength. Two different strategies of FCD selection are tested to generate vectors: ascending sequences and descending sequences. Feature vectors with descending sequences have better classification accuracy. In addition, the results show that the classification accuracy can reach 100% with the feature vector of the seven largest FCD values compared to 59.57% for the feature vector with the seven smallest FCD values. Moreover, we find that the channels with high FCD values are primarily centered in SWIR bands. This result could be a reference for optimizing the hardware design of HSL for ore classification or mineral identification.

This paper presents the estimation of the ground elevation and canopy top elevation from the data collected by an airborne frequency-modulated continuous waveform profiling radar, Tomoradar. The estimated ground and canopy top elevations are critical for the derivation of reference information for the satellite-borne microwave radar data and the modeling of interaction between microwave radar signal and foliage. The methods of estimating the ground elevation and canopy top elevation from profiling radar are introduced, and the accuracy was evaluated via digital terrain model and Velodyne VLP-16 LiDAR integrated with the Tomoradar. To our knowledge, the ranging radar and the LiDAR data were simultaneously collected for the first time. The evaluation proved that the root-mean-square error (RMSE) of ground level estimation of the developed profiling radar can reach up to 0.33 m. When comparing the estimated canopy top peak elevation between the profile radar data and the LiDAR data, it was found that the side lobes of Tomoradar antenna system may produce undesired canopy backscatters when the size of the canopy gap is comparable to the footprint size of the main lobe, resulting in a higher canopy top elevation measurement from Tomoradar than that from LiDAR. The RMSE of the estimated canopy top peak elevation between two data sets was 0.32 m in the best case and 0.852 m on average. Moreover, the RMSE of point-to-point comparing the entire canopy tops elevation estimated from the data of two active remote sensing systems is 0.799 m after excluding the outliers.

In this paper, a range calibration and modulation sweep linearity verification method of a helicopter-borne FM-CW (Frequency-Modulated Continuous Waveform) profiling radar are presented. The radar is designed to collect backscatter signal waveforms to build target stand profiles. Various forest elements e.g. tree height; density; species, etc. can be evaluated through this profiling radar. This paper investigates the relation between target range and the received corresponding backscatter signal frequency and verifies the linearity of modulated RF (radio frequency) signal. The calibration result can be achieved based on a ground calibration test with restricted distance rather than the full measurement range. And the calibrated output can be directly adopted for airborne forestry inventory. The field test proves that the proposed calibration method is applicable for FMCW radar calibration using a centimeter level accurate fly trajectory with the help of Global Navigation Satellite System (GNSS) and IMU (Inertial Measurement Units) and a DEM (Digital Terrain Model) of the test field.

Microphone arrays, also known as acoustic antennas, have been extensively used for sound localization. Small-scale microphone arrays have especially been used in teleconferences and game consoles due to their small dimension and easy deployment. In this article, we present an approach to locating a sound source using a small linear microphone array. We describe the fundamentals of linear microphone arrays and analyze the impact of geometry in terms of positioning accuracy using the dilution of precision (DOP) concept. The generalized cross-correlation (GCC) based on the phase transform (PHAT) weighting function is used to estimate the time difference of arrivals in a microphone array. Given the time differences, we use both closed-form and iterative optimization solutions to calculate the coordinates of the sound source. In order to evaluate the performances of the solutions applied in this paper, simulations and field tests were conducted. Simulation results show that the closed-form algorithm gives a positioning error of less than 5 cm in a 10-by-10 meter room when the geometry of a microphone array is good and the signal to noise ratio (SNR) is high. Linear small microphone arrays have lower performances compared to a non-linear distributed array. When the scale of a linear array is reduced, the positioning accuracy decreases dramatically. With a small linear array, the iterative optimization algorithm gives much better performance compared to the closed-form algorithm. Field tests were conducted in an 11-by-5.6 meter room using a linear array with a length of 0.23 meters. Positioning results show an average error of 0.25 meters along the axis parallel to the linear array and 0.53 meters error along the axis which is perpendicular to the linear array.

Personal positioning has been playing an important role in context awareness and navigation. Pedestrian dead reckoning (PDR) solution is a positioning technology used where the global positioning system (GPS) signal is not available or its signal is mightily attenuated or reflected by constructions nearby, such as inside the buildings or in GPS degraded areas such as urban city, basement. A traditional PDR solution employs a multisensor unit (integrating accelerometer, gyroscope, digital compass, barometer, etc.) to detect step occurrences, as well as to estimate the stride length. In our pilot research, we proposed a novel electromyography (EMG)-based method to fulfill that task and obtained satisfying PDR results. In this paper, a further attempt is made to investigate the feasibility of using EMG sensors in sensing muscle activities to detect the corresponding locomotion patterns, and as a result, a new approach, which recognizes different locomotion patterns using EMG signals and constructs stride length models according to the recognition results, is then proposed to improve the positioning accuracy and robustness of the EMG-based PDR solution by adapting the stride length model into different locomotion patterns. The experimental results demonstrate that EMG-based pattern recognition of four motions (walking, running, walking upstairs, walking downstairs) achieve an error rate of less than 2%. Combined with locomotion pattern recognition, the proposed EMG-based PDR solution yield a position deviation of less than 5 m within the whole distance of 404 m in a simulated indoor/outdoor field test. The proposed method is proven to be effective and practical in sensing context information, including both the user's activities and locations.

Navigation and position applications are now becoming standard built-in features in a smart phone. However, locating a mobile user in GNSS unfriendly and denied environments such as urban canyons and indoor environments ubiquitously is still a challenging task. Several self-contained sensors, such as accelerometer, digital compass, gyroscope and barometer, have been adapted as assistance augmentation technologies to a GPS receiver to make a seamless outdoor-indoor pedestrian navigation system. Since the indoor environment is more complex than an open-sky environment, such GNSS signal-degraded areas are typically also contaminated with disturbance sources that affect sensor measurements, a digital compass can be disturbed significantly by e.g. an elevator that bears magnetic perturbance. And a ventilation facility may cause inconsistencies in the barometer's measurements; not to mention that the indoor surrounding attenuates or blocks the GNSS signal. In this paper, a novel outdoor-indoor seamless solution for pedestrian navigation is introduced, which is based on Electromyography (EMG) sensors. The EMG sensor measures the electrical potentials generated by muscle contractions of human body. Therefore it is immune against the environment disturbance; moreover, it has potential capability to exploit the health situation of the pedestrian, since the EMG sensor has been applied on the biomedical field for decades. In the paper, five different motions are classified to estimate the stride length, including: walking horizontally, walking up along a slope, stepping upstairs/downstairs and standing still. The stride length estimation is based on a simple empirical module where fix stride length is donated to each classified motion. In order to evaluate the EMG-based pedestrian dead reckoning (PDR) solution developed in this study, an outdoor-indoor field test had been carried out in the Finnish Geodetic Institute. The test results demonstrated that the EMG-based PDR solutions are comparable to the commercial GPS stand-alone solutions for a period of 9 minutes outdoors, which is equivalent to a walking distance of 667 meters and also demonstrates its robustness for indoor navigation for a period of 3 minutes.