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As an increasing number of people suffer from sleep disorders, such as insomnia or sleep apnea, sleep monitoring and management using consumer devices have gained increasing attention from research communities. As sleep quality is closely related to sleep structure based on hypnograms, the classification of sleep stages over the course of the night is important for accurate sleep monitoring. We present sleep stage classification using a smartphone equipped with ultra-wideband (UWB) radar. We focused on the development of easily accessible sleep monitoring system for the general population by placing the smartphone on a table near a bed, which is commonly used during sleep. We collected 509 nights of UWB radar and nocturnal in-laboratory polysomnography (PSG) data from various participants, including patients with apnea, using a customized Samsung Galaxy smartphone with a UWB radar chip placed on a table near the bed. A combination of 1D convolutional neural network and transformer architecture was proposed in this study, and a domain adaptation technique was applied to train the model with both large-scale respiratory signals from open database PSGs and UWB radar data to boost the performance by overcoming the lack of UWB radar data. With 5-fold validation, an epoch-by-epoch comparison between the predicted and expert-annotated four sleep stages (Wake, REM sleep, light sleep, and deep sleep) resulted in 0.76 of accuracy and 0.64 of Cohen’s kappa. This study demonstrated that sleep stages can be monitored with substantial accuracy by simply placing a smartphone on a bedtable, making it highly usable and reliable in real use cases.

In recent years, the Poisson multi-Bernoulli mixture (PMBM) filter has been established among the state-of-the art methods in target tracking. We present a method for including target-provided measurements in said filter, both when using it to track extended objects and point targets. We use messages from the Automatic identification system as an example of target-provided measurements, and radar and LiDAR as examples of exteroceptive sensors. In the point target case, we utilize several different kinematic models in parallel through the interacting multiple models framework, and compare the presented method to several common trackers and other PMBM filter configurations. The results show that our method outperforms similar methods when target-provided measurements are available. The point target variant is also shown to work in a closed-loop collision avoidance experiment in a maritime environment, demonstrating its feasibility for use in real-world applications. For the extended object tracking case, we expand upon the Gaussian process PMBM filter. The extended object method is evaluated on both simulated and experimental data, and is shown to improve the tracking performance when including target-provided measurements in comparison to when it only uses exteroceptive measurements.

For the problem of power allocation between a distributed multistatic radar network and a smart jammer, the application of non-cooperative game theory is employed to address the issue in this paper. Consequently, three scenarios of power allocation games are examined. The first two game scenarios, characterized by information asymmetry, are categorized under the Stackelberg game framework, while the final scenario, with information symmetry, is classified as a non-cooperative game. Through the power allocation analyses of the three game scenarios, it is observed that both the radar system and the jammer possess a first-mover advantage. Additionally, the existence and uniqueness of the Nash equilibrium in the games are demonstrated. Based on the best response strategies within the games, three corresponding power allocation game algorithms are proposed. Ultimately, the convergence and performance comparison of the three power allocation game algorithms are validated through simulation experiments.

This paper develops a novel stochastic geometry model to investigate design tradeoffs in a large-scale radar-aided millimeter-wave cellular network to eliminate the beam training overhead. In the proposed system, each base station comprises two sub-systems: the sensing sub-system, where radar localizes users, and the communication sub-system, where directional antennas serve the detected users. Both sub-systems operate simultaneously to eliminate the beam training overhead and reduce misalignment errors. The system is modeled under realistic fading conditions with scatterers and interferers in the environment. System parameters, such as the density of mobile users and undesired clutter, radar cross-section fluctuations, radar search duration, antenna directivity, and bandwidth are considered in the analytical model. Two scenarios are studied; the first considers antenna misalignment for the communication sub-system, where error magnitude depends on the radar sub-system accuracy. The other scenario considers perfect alignment for benchmarking. It is demonstrated that the radar search duration, which controls the radar antenna beamwidth, is a crucial parameter for optimizing the system throughput. Moreover, the beamwidth of the communication antenna exhibits a tradeoff in which a narrow beam yields higher gain and reduces interference but increases the misalignment error impact. The results demonstrate that if the beamwidths of both sub-systems are carefully chosen, the proposed system can eradicate the beam training overhead without compromising the system’s performance, in addition to significantly enhancing the average total system throughput.

In this paper, we propose an S/X-band shared-aperture array antenna with a mutual complementary design to improve the electromagnetic (EM) transparent characteristics. A unit-cell of the proposed antenna includes one dual-loop element for the S-band and $3\times 3$ dual-loop elements for the X-band. To configure the shared-aperture structure in a limited space, the S-band element is stacked on top of the X-band elements. To solve the practical engineering problems of the shared-aperture antennas, novel design techniques such as using a mutual complementary structure, a coupling compensation array, an interface layer, and an antenna modularization are employed. To verify the antenna feasibility, the fabricated unit-cell extends into a $4\times 4$ unit-cell array. The fractional bandwidth of the reflection coefficients for the proposed array are 14.7% and 15% in the S- and X-bands, respectively. In the S-band, as the steering direction of the main beam increases from 0° to 45°, the maximum gain decreases from 14.6 dBi to 11.8 dBi. In the X-band under the same conditions, the maximum gain varies from 26.6 dBi to 25.3 dBi.

This article aimed to monitor vegetation using C-band radar data at a subdaily time step. To this end, radar measurements using tower-mounted antennas with a 15-min time step, along with physiology-related information (sapflow and micrometric dendrometry), were acquired quasi-continuously from March 2020 to December 2021 in an olive orchard located near Marrakech, Morocco. The article focused on temporal coherence, whose clear diurnal cycle (highest at night and lowest at the end of the afternoon) had been highlighted over tropical and boreal forests in previous studies. The results showed that coherence was highly sensitive to: wind-induced movement of scatterers, since coherence was lowest when wind speed was highest in late afternoon, and vegetation activity, especially its water dynamics, since the morning coherence drop coincided with the onset of sapflow and the daily evapotranspiration cycle, as well as the good agreement between the temporal drop rate of coherence and the daily residual variation in trunk circumference (i.e., deviation from long-term trend). Finally, coherence remained high for temporal baselines of several days, showing that sentinel-1 data (when both satellites are operational) may be well suited for such studies, especially with acquisitions made during morning passes, when wind speed is low. These results open perspectives for monitoring tree crop physiology using high-revisit-time radar observations.

Incoherent backscattering of mm-waves from natural rough surfaces is considered. A novel method is proposed to determine the statistical properties of surface scattering from range profile measurements. The method is based on modeling the road surface as a grid of uncorrelated scattering elements, described by normalized scattering matrices. Using this model, expressions are derived to estimate the mean value and covariance matrix of surface scattering from measurement data. This procedure is then applied to measurement data of four road surface types, namely: 1) dry asphalt, 2) wet asphalt, 3) asphalt covered by basalt gravel, and 4) old asphalt. Using the derived statistical normalized radar cross-section models, two novel applications are proposed. First, a procedure for synthesizing/simulating surface clutter is proposed. This procedure is subsequently used to simulate received power from surfaces comprising patches of one or multiple road surface conditions. Excellent agreement between simulation and measurement results is demonstrated. Second, a method for determining the optimal polarization of the electromagnetic sensing waves used in a single-polarized radar system is proposed. This method is based on factorizing the antenna polarization vector into two bounded parameters, allowing for numerical evaluation of the minima and maxima for targets with a specified scattering matrix. This method is further extended to work with statistical descriptions of scattering matrices by means of Monte Carlo simulations.

A promising waveform candidate for future joint sensing and communication systems is orthogonal frequency-division multiplexing (OFDM). For such systems, supporting multiple transmit antennas requires multiplexing methods for the generation of orthogonal transmit signals, where equidistant subcarrier interleaving (ESI) is the most popular multiplexing method. In this work, we analyze a multiplexing method called Doppler-division multiplexing (DDM). This method applies a phase shift from OFDM symbol to OFDM symbol to separate signals transmitted by different Tx antennas along the velocity axis of the range-Doppler map. The main focus of this work lies on the implications of DDM on the communication task. It will be shown that for DDM, the channels observed in the communication receiver are heavily time-varying, preventing any meaningful transmission of data when not taken into account. In this work, a communication system designed to combat these time-varying channels is proposed, which includes methods for data estimation, synchronization, and channel estimation. Bit error ratio (BER) simulations demonstrate the superiority of this communications system compared to ESI-based systems.

Non-contact vital sign monitoring has been an important research topic recently due to the ability to monitor patients for an extended period especially during sleep without requiring uncomfortable attachments. Radar is a popular sensor for vital sign monitoring research. Various algorithms have been proposed for estimating respiration rate and heart rate from the radar data. But many algorithms rely on Fast Fourier Transform (FFT) to convert time domain signal to the frequency domain and estimate vital signs, despite FFT having limitation of frequency resolution being inverse of the time interval of data sample. However, there are other spectral estimation algorithms, which have not been much researched into the suitability of vital sign estimation using radar signals. In this paper, we compared eight different types of spectral estimation algorithms, including FFT, for respiration rate and heart rate estimation of stationary subjects in a controlled environment. The evaluation is based on extensive data consisting of different stationary subject positions. Considering the results, the eligibility of algorithms other than FFT for respiration rate and heart rate estimation is demonstrated. Using this work, researchers can get an overview on which algorithm is suitable for their work without the need to review individual algorithms separately. [ABSTRACT FROM AUTHOR]

The work presented in this article aims to detect the cardiac movement of a person in a noninvasive way and correlate it with a reference signal in the medical field: the electrocardiogram. To achieve this goal, a measurement campaign was carried out on 20 consenting individuals. On the one hand, the mechanical signal, the movement of the subject’s chest induced by the heartbeat, is recorded via an FMCW radar, and on the other hand, the electrical signal of the heart is recorded via an ECG acquisition board. Signal processing functions and different filtering will allow the correlation of the radar and ECG signals. This study is conducted on apnea recordings in order to remove the impact of breathing on the movement of the chest. When the subject holds his or her breath, the two important phases of cardiac movement via radar capture can be detected: 1) the systole and 2) the diastole. The delay between the mechanical signal of the heart and the electrical signal of the heart, already explained by medicine, is well noted. The accuracy of motion detection provided by the radar allowed us to highlight the reproducibility of the chest movements detected during a capture. Their correlation with ECG data validates the proposed hypotheses.

This work presents a high angular resolution radar system operating at 94 GHz, utilizing a 20-channel receiver (RXs) and a single-channel transmitter (TX) in 65-nm CMOS technology. The receiver consists of five subarray chips and each chip has four receiving channels. Among these five receiving chips, the 4 channels of the first chip are continuously sampled for all chirp intervals and the 4 channels of other four receiver chips are sampled in a time interleaved way at their allocated chirp times. The fully sampled channels process the range-Doppler information with coarse angular information of the targets and provide the phase reference to the time-interleaved RX channels. The time-interleaved massive channels are used to obtain range and high-resolution angular information. This approach allows for processing the information from all 20 RX channels using only eight-channel analog-to-digital converters (ADCs), thereby significantly reducing the cost and complexity associated with a radar system comprising massive RX channels with high scalability. The challenges associated with packaging, antenna integration, and LO distribution in W-band for a massive channel radar system are overcome by a high ratio multiplier design to be driven by a low reference frequency-modulated continuous wave (FMCW) waveform and using an on-chip antenna feeder, facilitating low-loss and compact integration. Experimental results demonstrate that the implemented radar system achieves an angular resolution of 1.74°. These results suggest that a sub-1° super-resolution radar can be designed by incorporating multiple-input multiple-output (MIMO) techniques.

To help meet the growing demand for wireless data capacity, the United States Federal Communications Commission (FCC) and National Telecommunications and Information Administration (NTIA) define and regulate a spectrum-sharing approach in several radio frequency (RF) bands, with sharing in additional bands expected in the near future. This paper examines the case in which some of the users in the shared bands are government radars, as is the case in the 3.5 GHz Citizens Broadband Radio Service (CBRS) band. This paper surveys topics related to dynamic spectrum sharing (DSS) of RF bands between government radars and communication systems, including spectrum policy, dynamic spectrum access (DSA), dynamic frequency selection (DFS) and frequency assignment, Primary User (PU) protection and privacy, radar-communications interference mitigation, and joint radar-communications system design. The purpose of this paper is to provide readers with a summary of current knowledge of these aforementioned concepts while also providing references so that topics of interest may be studied deeper. The structure of this paper is broken into several topics, where within each topic table(s) provide a survey of the relevant topic. The contribution made includes over 200 references. Tables include summaries of approaches described in multiple references, as well as descriptions and summaries of individual references used throughout the survey. A taxonomy of approaches applicable to spectrum sharing between radar and communication systems is provided, as are conclusions and recommendations. Findings include existing technology, systems, and regulations utilizing the CBRS in its current structure. Therefore, all of the advancements will most likely be derived using a combination, derivative, or adapted methodology defined in the different sections throughout this survey.

Studies have emphasized the pivotal role of remote laboratories in delivering high-quality engineering education. Specifically, within the domain of wireless communication, remote laboratories offer students the opportunity to actively engage in practical Radio Frequency (RF) experiments, much like their traditional in-person counterparts. Through online platforms, students can construct RF prototypes, gaining hands-on experience in communication theories. However, the lack of a standardized model for designing and evaluating remote labs in RF education limits their consistency and effectiveness. This paper introduces MELODY, a model and classification framework tailored explicitly for Software Defined Radio (SDR) remote laboratories. This model is characterized by its technology-agnostic and open-source approach. The paper evaluates existing SDR remote lab projects, analyzing their architecture and design choices. MELODY is presented in details, encompassing process, services, platform, and infrastructure layers. Additionally, MELODY provides a classification framework, assigning ratings to SDR remote labs on a 1 to 5-star scale aligned with engineering standards. The classification framework’s rubric assesses isolation challenges, calibration, scalability, and remote SDR lab availability in order to compare with other SDR remote labs. Practical applications developed by the Remote Hub Lab (RHL), such as the remote labs RHL-RELIA and RHL-RADAR are explored, showcasing how MELODY can be effectively applied. MELODY aims to establish standardization, ensuring consistency and quality assurance in remote SDR labs, fostering innovation, skill development, and collaboration within engineering education.

Thanks to the complementary nature of millimeter wave radar and camera, deep learning-based radar-camera 3D object detection methods may reliably produce accurate detections even in low-visibility conditions. This makes them preferable to use in autonomous vehicles’ perception systems, especially as the combined cost of both sensors is cheaper than the cost of a lidar. Recent radar-camera methods commonly perform feature-level fusion which often involves projecting the radar points onto the same plane as the image features and fusing the extracted features from both modalities. While performing fusion on the image plane is generally simpler and faster, projecting radar points onto the image plane flattens the depth dimension of the point cloud which might lead to information loss and makes extracting the spatial features of the point cloud harder. We proposed ClusterFusion, an architecture that leverages the local spatial features of the radar point cloud by clustering the point cloud and performing feature extraction directly on the point cloud clusters before projecting the features onto the image plane. ClusterFusion achieved the state-of-the-art performance among all radar-monocular camera methods on the test slice of the nuScenes dataset with 48.7% nuScenes detection score (NDS). We also investigated the performance of different radar feature extraction strategies on point cloud clusters: a handcrafted strategy, a learning-based strategy, and a combination of both, and found that the handcrafted strategy yielded the best performance. The main goal of this work is to explore the use of radar’s local spatial and point-wise features by extracting them directly from radar point cloud clusters for a radar-monocular camera 3D object detection method that performs cross-modal feature fusion on the image plane.

It is well-known that the availability of transmit and receive arrays in colocated multiple-input multiple-output radar systems can be exploited to detect multiple targets, and estimate their range and angular coordinates. Unluckily, the accuracy achieved in the estimation of target parameters can be severely affected by hardware nonidealities. The impact of these nonidealities can be mitigated through the adoption of specific methods explicitly developed to estimate and compensate for them, i.e., briefly, of calibration methods. As far as we know, until now, most of the calibration techniques proposed in the technical literature for colocated multiple-input multiple-output radars are based on the idea of placing one or more reference targets in front of the considered radar device at perfectly known and fixed locations, and quantifying the difference between the expected radar image and the measured one. In this manuscript, two novel calibration methods are proposed. Both exploit the measurements captured by a colocated multiple-input multiple-output radar device at multiple positions; for this reason, the acquisition of such measurements involves the movement of the radar. The first one does not require any knowledge about the nature, number, position or radar cross section of the reference targets, provided that all these are in far-field; the second one, instead, needs a single reference target in near-field conditions, with the only constraints for it to be pointwise and isolated from stray targets in the range domain. This drastically simplifies the experimental setup to be employed for radar calibration. The proposed methods are tested on a commercial radar device and compared with a traditional method for radar calibration.

While automotive radars are widely used in ADAS and autonomous driving, extrinsic and temporal calibration of automotive radars with other sensors is still daunting due to the sparsity, uncertainty, and missing elevation angles of automotive radar measurements. We propose a target-based calibration approach of 3D automotive radar and 3D LiDAR that performs extrinsic and temporal calibration in both factory and on-road settings. In factory calibration settings, a map is built with precise target poses; target trajectories are estimated based on map-based target localization in which the accuracy of both nearby and faraway target pose estimates can be ensured. The spatial and temporal relationships between radar and LiDAR measurements are established with target trajectories to accomplish extrinsic and temporal calibration. The proposed data collection procedure provides sufficient motion for analyzing time delay between sensors and can significantly reduce the data collection effort and time. There is 52.3% distance error reduction after time delay compensation in the experiment, which shows the improvements of temporal calibration. In on-road calibration settings, the metal objects with semantic labels, such as traffic signs, are selected as calibration targets. Although there could be insufficient correspondences to infer the missing dimension of planar radar for six DoF extrinsic calibration as demonstrated in factory calibration settings, the three extrinsic parameters and the time delay are shown still to be accurate. We validated the proposed method using the nuScenes datasets, which provide sensor measurements, poses, and HD map. With twenty-two data logs, each has over 1000 correspondences, the result of extrinsic parameters reaches centimeter-level accuracy compared with the offered benchmark. The time delay compensation improves 1 meter error for radar tracking in a 20 m/s vehicle case and improves mapping quality in real world data.

In this article a general first-order statistical framework is established for passive multi-channel detection and localization of an unknown radiated signal. This radiated signal is written in terms of a finite basis expansion, and the map between these basis coefficients and the measured data on a sensor is a channel that might be known, partially known, or unknown except for the dimension of the signal subspace. The noise at each sensor is assumed to be Gaussian and white; the noise variances at each sensor may be known, unknown and equal, or unknown and possibly unequal. This article develops detectors for all nine of these cases. These detectors are each generalized likelihood ratios, and typically decompose into locally computed detector statistics plus pairwise coherence, or cross-validation, statistics. Of particular note are the scale-invariant detectors that preserve a constant false alarm rate (CFAR) property with respect to noise power.

Radar is a key component of the suite of perception sensors used for safe and reliable navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity imaging, detection of agents in occlusion and over long ranges, and robust performance in adverse weather conditions. However, the usage of radar data presents some challenges: it is characterized by low resolution, sparsity, clutter, high uncertainty, and lack of good datasets. These challenges have limited radar deep learning research. As a result, current radar models are often influenced by lidar and vision models, which are focused on optical features that are relatively weak in radar data, thus resulting in under-utilization of radar’s capabilities and diminishing its contribution to autonomous perception. This review seeks to encourage further deep learning research on autonomous radar data by 1) identifying key research themes, and 2) offering a comprehensive overview of current opportunities and challenges in the field. Topics covered include early and late fusion, occupancy flow estimation, uncertainty modeling, and multipath detection. The paper also discusses radar fundamentals and data representation, presents a curated list of recent radar datasets, and reviews state-of-the-art lidar and vision models relevant for radar research.

This paper examines the separation of wireless communication and radar signals, thereby guaranteeing cohabitation and acting as a panacea to spectrum sensing. First, considering that the channel impulse responses were known by legitimate receivers (communication and radar), we showed that the optimizing beamforming weights mitigate the interference caused by signals and improve the physical layer security (PLS) of the system. Furthermore, when the channel responses were unknown, we designed an interference filter as a low-complex noise and interference cancellation autoencoder. By mitigating the interference on the legitimate users, the PLS was guaranteed. Results showed that even for a low signal-to-noise ratio, the autoencoder produces low root-mean-square error (RMSE) values.

The Global Flood Monitoring (GFM) system of the Copernicus Emergency Management Service addresses the challenges and impacts that are caused by flooding. The GFM system provides global, near-real-time flood extent masks for each newly acquired Sentinel-1 Interferometric wide swath synthetic aperture radar (SAR) image, as well as flood information from the whole Sentinel-1 archive from 2015 on. The GFM flood extent is an ensemble product based on a combination of three independently developed flood mapping algorithms that individually derive the flood information from Sentinel-1 data. Each flood algorithm also provides classification uncertainty information that is aggregated into the GFM ensemble likelihood product as the mean of the individual classification likelihoods. As the flood detection algorithms derive uncertainty information with different methods, the value range of the three input likelihoods must be harmonized to a range from low [0] to high [100] flood likelihood. The ensemble likelihood is evaluated on two test sites in Myanmar and Somalia, showcasing the performance during an actual flood event and an area with challenging conditions for SAR-based flood detection. The Myanmar use case demonstrates the robustness if flood detections in the ensemble step disagree and how that information is communicated to the end-user. The Somalia use case demonstrates a setting where misclassifications are likely, how the ensemble process mitigates false detections and how the flood likelihoods can be interpreted to use such results with adequate caution.

For the feature-based detector of small targets in sea clutter, on the one hand, the three-dimensional convex hull-based detector deviates from the distribution of sea clutter vectors in the feature space and only combines the information of low-dimensional features. On the other hand, the redundancy and correlation between high-dimensional features are high. Consequently, we propose a detector for detecting small floating targets in sea clutter in high-dimensional feature space (HDFS) in this article. First, the maximum relevance-minimum redundancy (mRMR) algorithm to choose the low-relatedness features from the HDFS is proposed. For the mRMR algorithm, we choose the target features and sea clutter features from the eight-dimensional feature space (8-DFS), where the target features and sea clutter features have the highest degree of discrimination in the 3-DFS. Second, the distribution of sea clutter in the 3-DFS is concave or convex, which depends on the selection of features. In most cases, the distribution is concave. Using the traditional convex hull to match the concave distribution of sea clutter inevitably enlarges the judgment area and considerably decreases the detection probability. Due to the concave distribution of sea clutter in the 3-DFS, we propose a new false alarm controllable three-dimensional concave hull detector based on the mRMR (mRMR-Tri-ConcaveHull detector). In the mRMR-Tri-ConcaveHull detector, the feature vectors in the 3-DFS, which are selected by mRMR, form a concave area that is more suitable for the Concave Hull detector. Through the experimental analysis of the measured data, we find that the proposed mRMR-Tri-ConcaveHull in this article can significantly enhance the detection performance compared with the three-feature convex hull detector.

Precipitation and in particular rain present a random medium with constituent particles having dimensions comparable to the wavelength at millimeter-wave frequencies. Due to considerable scattering of waves from such particles, significant signal attenuation and phase front aberration can take place which are important factors that must be considered in modern communication system or radar design. This paper presents a fast and accurate numerical method for phase coherent modeling of wave propagation and scattering in random media, like rain. In this approach, the random medium is divided into sufficiently large finite slabs and each slab is modeled as a network with multiple input/output ports where ports represent rays with different polarizations propagating in different directions entering and leaving the slabs. The cascaded networks of slabs can be used to find the response of the random medium to any arbitrary source. The proposed method considers multiple scattering among all scatterers and thus the entire physics of wave-particle interactions is accounted for. In addition, the method can model both the mean and the fluctuating parts of waves in the random medium. To demonstrate the applicability of the proposed method, it is used for the calculation of the specific attenuation in rain media as a function of rain rates and frequency within the millimeter-wave band. The results show a good match with the available rain attenuation data in the literature.

An increasing number of connected vehicles (CVs) driving together with regular vehicles (RVs) on the road is an inevitable stage of future traffic development. As accurate traffic flow state detection is essential for ensuring safe and efficient traffic, the level of road intelligence is being enhanced by the mass deployment of roadside perception devices, which is capable of sensing the mixed traffic flow consisting of RVs and CVs. In this background, we propose a roadside radar and camera data fusion framework to improve the accuracy of traffic flow state detection, which utilizes relatively more accurate traffic parameters obtained from real-time communication between CVs and roadside unit (RSU) as calibration values for training the back propagation (BP) neural network. Then, with the perception data collected by roadside sensors, the BP neural network-based data fusion model is applied to all vehicles including RVs. Furthermore, considering the changes of road environments, a dynamic BP fusion method is proposed, which adopts dynamic training by updating samples conditionally, and are applied to fuse traffic flow, occupancy and RVs speed data. Simulation results demonstrate that for CVs data and all vehicles (including RVs) data, the proposed dynamic BP fusion method is more accurate than single sensor detection, entropy based Bayesian fusion method and traditional BP fusion without training by CVs. It can achieve smaller error, and the accuracies of vehicle speed, traffic flow, and occupancy are all above 95%.

Applications for human sensing, also known as (human) occupancy detection, include energy management systems for intelligent buildings, intruder detection, e-health systems, the identification of everyday activity, and the monitoring of vital signs. These applications require intelligent decision-making that relies on human sensing. Multiple technologies based on vision, sensors, or radio signals can be used to detect occupancy. Vision-based systems use a multitude of cameras to recognize the human presence, but they are restricted by light availability, line-of-sight coverage, expensive equipment, and privacy concerns. Sensor-based techniques refer to a prospective method that employs various combinations of sensors. These solutions are static and necessitate costly equipment installation and maintenance. Due to technical advancements, radio-based signals, such as WiFi, have been integrated into various forms of infrastructure, including homes, offices, and constructions. Due to how human body movements affect wireless signal propagation, it is possible to detect human motions by analyzing the received wireless signals (such as reflection, diffraction, and scattering). Due to its low cost and non-intrusive nature, wireless-based human activity detection has received substantial attention and become a key topic of study. This article reviews the underlying principles, methodologies, and system architectures of radio-frequency-based occupancy detection systems. We classify the reviewed research studies based on the technical measures and applications they employ. In addition to focusing on the security aspects of occupancy detection and discussing future trends and difficulties, we also discuss practical considerations.

NASA's Soil Moisture Active Passive (SMAP) mission originally included both passive and active L-band measurement capabilities. It was the first satellite instrument to provide global L-band radar observations of normalized radar cross section ($\sigma ^{0}$) at multiple resolutions. The SMAP radar collected high-resolution ($\sim$1–3 km) synthetic aperture radar (SAR) measurements over most of the earth's land mass. It simultaneously collected low-resolution 6 × 30 km “slice” and full-footprint 29 × 35 km measurements. The SMAP radar operated for 83 days, from day of the year 103 to 186 in 2015, before the transmitter failed. The SMAP radar was designed to make vegetation roughness measurements in support of the SMAP primary mission to measure soil moisture, but the radar data are useful for a variety of applications, particularly in the polar regions. Unfortunately, limitations in the data download volume precluded the downlink of high-resolution data over Antarctica, sea ice in the polar regions, and various islands. Nonetheless, low-resolution slice and footprint data were collected and downlinked over these areas. To better exploit these low-resolution data, this article employs image reconstruction techniques to create twice-daily enhanced resolution SMAP radar images from the slice and footprint measurements. To validate the resolution enhancement, the enhanced resolution data are compared to SAR results over Greenland and South America. The new dataset is provided to the science community to support cryosphere and climate studies.

In radar imaging, any lateral resolution is either attained using antenna arrays or by synthetic aperture radar (SAR) principles. Recently, the combination of both approaches to a multi-channel SAR (MCSAR) has gained increased attention. However, antenna arrays are impaired by unknown calibration errors, particularly phase errors, amplification deviations, mutual coupling, and phase center position deviation of the antenna element. Since, first, established calibration procedures are often non-ideal and, second, the calibration parameters usually vary over time due to aging and changes in temperature, calibration errors distort the measurements during the radar operation. Therefore, current research focuses on finding new practical calibration procedures. However, hardly any research has been conducted to analyze the basic effects which erroneous calibration parameters have on the reconstructed image in MCSAR or MIMO radar. In this work, we analyze the effects of multiple calibration error types on MCSAR and MIMO radar imaging. Both measurement principles lead to periodic error patterns, resulting in deterministic distortions. The derived relations can be used to precisely predict the position and number of ghost targets caused by calibration errors for known measurement setups and targets. For this, no information about the actual calibration errors is required. Furthermore, worst-case distortion levels are provided for different bounded errors allowing rough estimates of the impact of different error types on the image's distortion level. The findings are validated via measurements using a 76$\,$GHz FMCW MIMO radar.

Synthetic aperture radar (SAR) is an established technique for high-resolution radar imaging relying on the ego-motion of the radar. The long coherent integration time of the radar in conjunction with a linear or circular trajectory enables the illumination of targets under different observation angles leading to robustness against short-time interference signals. The use of a bistatic setup to simultaneously acquire multiple observation angles further increases the aforementioned benefits. In this work, a bistatic SAR system using an active repeater is proposed, in the following called Bistatic Repeater-SAR (BR-SAR). In BR-SAR the repeater acts as a second observer providing a bistatic path with low system cost and hardware effort. This article discusses the BR-SAR approach with all signal processing and hardware considerations necessary to build the bistatic radar system. This includes a description of the full system and signal chain with a focus on the signal separation and evaluation of both monostatic and bistatic signals and the subsequent SAR processing. The approach is verified via simulations and measurements, in which conventional SAR is compared to the proposed BR-SAR system. Hardware effects leading to a deteriorated system performance of the bistatic system are analyzed and countermeasures are discussed and implemented. Finally, the benefits of the bistatic system are demonstrated based on a realistic measurement scenario.

In this article, we investigated the semantic segmentation of Polarimetric Synthetic Aperture Radar (PolSAR) using Complex-Valued Neural Network (CVNN). Although the coherency matrix is more widely used as the input of CVNN, the Pauli vector has recently been shown to be a valid alternative. We exhaustively compare both methods for six model architectures, three complex-valued, and their respective real-equivalent models. We are comparing, therefore, not only the input representation impact but also the complex- against the real-valued models. We then argue that the dataset splitting produces a high correlation between training and validation sets, saturating the task and thus achieving very high performance. We, therefore, use a different data pre-processing technique designed to reduce this effect and reproduce the results with the same configurations as before (input representation and model architectures). After seeing that the performance per class is highly different according to class occurrences, we propose two methods for reducing this gap and performing the results for all input representations, models, and dataset pre-processing.

Subsurface electromagnetic sensing techniques that can measure material properties of hidden layers are useful for applications such as security screening. A system combining active (radar) and passive (radiometer) sensing into one unit, or a pactive sensor, can address drawbacks of extant single mode sensors by reducing measurement ambiguity and resolving features better, thereby leading to improved identification of the hidden layer. This work investigates a technique to noninvasively extract the complex permittivity and thickness of hidden dielectrics using a pactive sensor. A proof-of-concept demonstration of an optimization-based inversion technique is used to extract the properties of an unknown, hidden layer in a multi-layered dielectric structure. The technique uses active data to estimate dielectric constant and thickness, and passive data to estimate loss tangent. The experimental setup is selected to represent a simplified single-voxel security scenario consisting of multiple dielectric layers backed by a human phantom layer (heated water). Tests using K-band prototype radar and radiometer systems yielded results with less than 5% error in the extracted dielectric constant, loss tangent and thickness of the unknown layer.

Potholes are one of the most important issues in African road-networks. They pose a major threat to mobility and, with time, cause accelerated degradation of the underlying road infrastructure as well as extensive vehicle damage. To address the need for improved infrastructure management, an advanced data gathering solution is required. This paper presents one such solution. The pothole detection, classification and logging (PDCL) system is under active development by Sensorit (Pty) Ltd in collaboration with the University of Cape Town (UCT) Radar Remote Sensing Group (RRSG). This system represents the initial step in Sensorit’s modular approach to producing fully autonomous vehicles for African markets. In this paper, an overview of the PDCL system is presented and early results are shown. The efficacy of the system’s unique combination of active infrared stereo vision and mmWave frequency-modulated continuous-wave (FMCW) radar sensors is explored. Under various experimental conditions, range-Doppler maps (RDMs) produced by the radar were unable to provide meaningful pothole detections. In contrast, processed depth maps produced by the stereo vision system are demonstrated to successfully detect even shallow potholes.

In this article, an asynchronous information fusion issue is investigated for a camera and a radar in an intelligent driving system. Local camera and radar estimators with missed detections are developed independently at synchronized state update time for target tracking. A Kuhn–Munkers algorithm is used to match local tracking results of camera and radar for fusion estimation of a same target. A fusion estimator is obtained by a matrix-weighted fusion algorithm with wide detection range and reliable fused estimates. Effectiveness of the proposed asynchronous fusion estimator is displayed by experimental results on road vehicle tracking. [ABSTRACT FROM AUTHOR]

High-resolution range profile (HRRP) is essential for high-throughput radar automatic target recognition. Due to the aspect sensitivity of HRRP, the recognition performance degrades when the training dataset misses some aspects. Inspired by disentangled representation learning (DRL), this letter proposes a type-aspect disentanglement network (TADN) to alleviate the above aspects missing problem. The proposed method disentangles HRRP into uncorrelated type and aspect representations, so that the type representation can be used to accurately recognize target without being affected by target aspects. Specifically, the proposed network acquires initial type and aspect representations by residual factorization. On this basis, mutual information (MI) minimization is used to constrain the independence of two representations from each other, and a reconstruction loss is employed to avoid information reduction during the decorrelation process. Additionally, target type and aspect labels are exploited to constrain their semantic meanings. Experimental results demonstrate that the proposed method achieves improved robustness in varying degrees of aspects missing cases. [ABSTRACT FROM AUTHOR]

A multistatic inverse synthetic aperture radar (ISAR) system can observe a target from multiple observation angles. Compared with the monostatic ISAR system, the multistatic ISAR system can obtain more spatial sampling data, which provides the ability for high-resolution ISAR imaging. In some cases, the locations of radars are close. There are overlaps among observing angles, which brings little cross-range resolution improvement. However, such scenes are less considered indicates the literature. In the scene with overlaps, the image obtained by each radar may be similar due to the similar observation angles. In this letter, a novel multistatic ISAR imaging model is proposed by applying the similarity prior as a constraint. And an efficient image reconstruction algorithm is derived based on the orthogonality of observation matrix. Compared with existing CS-based methods, the proposed method can be directly applied on multistatic ISAR echoes without rearranging, which is more convenient in practical applications. Experiment results of simulated and measured data show that the proposed method achieves better performance especially under low signal-to-noise ratio (SNR) conditions. [ABSTRACT FROM AUTHOR]

Tropospheric refractivity, which is closely related to temperature, pressure, and relative humidity, is a valuable parameter for weather forecasting and climate analysis. It has already been demonstrated that refractivity estimates can be derived using the phase measurements corresponding to radar signals backscattered from stationary targets over any terrain orography, with high temporal resolution. However, the random distribution of stationary targets affects the spatial resolution provided by the computed refractivity estimates. It is of interest to obtain reliable radar-based refractivity maps to assist final users with data interpretation and analysis, so the use of a suitable geostatistical interpolation technique to obtain refractivity maps is studied in this letter. Refractivity estimates obtained from C-band radar data gathered during 2019 by the United Kingdom’s national weather service (Met Office) are used to evaluate the accuracy of the method by comparing the results to ground-based weather stations and the European Center for Medium-Range Weather Forecasts (ECMWF’s) ERA5 reanalysis dataset. [ABSTRACT FROM AUTHOR]

The traditional convolutional neural network (CNN)-based methods usually use the spatial information in the amplitude of complex synthetic aperture radar (SAR) images. Several studies have started to concentrate on merging the unique physical properties of SAR images, such as DSN-v1, extracting the backscattering characteristic from the frequency domain. Although DSN-v1 has obtained impressive classification ability, there is some room for improvement. In this letter, DSN-v2 is proposed to boost the classification ability of man-made and natural objects in SAR images. The improvement is reflected in two aspects. First, a multiscale subband feature extraction (MSFE) component is designed for natural objects. Since we observe their multiscale subband spectrum is significantly different, multiple encoders are used to extract effective features. Second, the additive angular margin (AAM) loss is introduced to distinguish man-made objects more clearly by manually adding a margin to the decision boundary. The experimental results on the Sentinel-1 (S1) dataset show that DSN-v2 achieves superior classification performance and model training speed compared with DSN-v1. [ABSTRACT FROM AUTHOR]

High-fidelity deception jamming can seriously mislead synthetic aperture radar (SAR) image interpretation and target detection, which is difficult to identify or eliminate through traditional anti-jamming methods. Based on the range–Doppler algorithm (RDA), an anti-jamming method for SAR using joint waveform modulation and azimuth mismatched filtering is proposed in this letter. The signal model of SAR echo after pulse compression with deception jamming is derived from the radar detection and jamming characteristics. A multiobjective cost function is introduced to suppress the jamming level and keep the real target information, parameterized with the waveform initial phase and the azimuth mismatched filter coefficients, which is optimized using the second-order Taylor expansion approximation and the alternating direction multiplier method (ADMM). The performance of this proposed method is evaluated through the convergence analysis and anti-jamming experiments on the point target and the distributed target scenarios. [ABSTRACT FROM AUTHOR]

To compensate for the repositioning error (RE) introduced by the radar position offset in discontinuous ground-based synthetic aperture radar (GBSAR) monitoring, a new mathematical framework for modeling the baseline error based on the Taylor expansion is developed in this letter. And then, a novel 3-D model called multiparameter nonlinear trigonometric model (MNTM) is proposed to accurately compensate for the RE. Furthermore, to improve the compensating efficiency, we further develop an efficient 2-D model called linear trigonometric model (LTM). Both simulation and field experiments verify the superiority and feasibility of the proposed methods, which measure the displacement with sub-millimeter accuracy. [ABSTRACT FROM AUTHOR]

The Doppler frequency of the echo signal exhibits time-varying and high-order characteristics due to the high-speed relative motion between the satellites in spaceborne inverse synthetic aperture radar (ISAR) imaging of space target, even during a short coherent processing interval (such as 1 s). Therefore, the selection of the optimal imaging time interval (OITI) is crucial to obtain a well-focused image. The existing methods do not consider the fact that the OITIs of scatterers on the target can also vary significantly. In this letter, an OITI selection method for spaceborne ISAR imaging of space targets based on the time–frequency analysis is proposed. First, the differences in optimal imaging instants (OIIs) of scatterers are analyzed. Then, based on the combination of the Margenau–Hill–spectrogram (MHS) and the S-method, the time–frequency curves can be obtained after extracting the time–frequency ridge and polynomial fitting. The OII of the entire target can be estimated after eliminating the differences in the OIIs by multiplying the constructed third-order phase terms with the signal. Finally, a more suitable OITI can be obtained because of the elimination of the differences in the OIIs. The effectiveness and advancement of the proposed method are verified by simulation and real data results. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is a powerful tool for all-weather, all-day observation, but it is susceptible to interference in the echo phase, particularly from the nonlinear phase caused by terrain relief. This can cause defocus and target position movement, which can impact the accuracy and recognition ability of SAR imaging. While current research focuses on using digital elevation models (DEMs) to correct the phase, obtaining high-precision DEM data requires significant measurement and processing work. To address this, we propose a new SAR filtering algorithm based on the terrain echo phase. By studying the phase of a SAR echo signal containing elevation information, we divide the nonlinear phase caused by terrain echo into two components: one that varies with azimuth and one that does not. We filter out the nonlinear phase component dependent on the azimuth angle from the echo phase and eliminate the nonlinear phase component invariant with the azimuth angle by filtering the specific pitch angle. The effectiveness of this filtering algorithm is verified through theoretical analysis and simulation. [ABSTRACT FROM AUTHOR]

Special attention has been devoted to joint waveforms based on radar signals to mitigate the tension of spectrum resources and simultaneously play a multifunctional role. Aiming at the shortcomings of conventional detection and jamming dual-functional systems, where wide beams are difficult to detect with high accuracy, while narrow beams are difficult to cover a wide range of jamming, this letter designs a multiple-input multiple-output (MIMO)-synthetic aperture radar (SAR) system with jamming capabilities. The characteristic of this signal is to use MIMO system multiple antennas to transmit frequency diversity wideband code division jamming signals to effectively generate wideband suppression jamming for conventional radars, including frequency-agile radars. At the same time, the wide beam characteristics of MIMO antennas can ensure the physical range of jamming. In addition, for the imaging part, digital beamforming (DBF) technology can be used to form the beam in a specific direction at the receiving end, resulting in high resolution. Simulation experiments confirm the system’s ability to achieve high-resolution imaging of specific target areas while maintaining robust jamming capabilities. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) using radar has been realized commonly using deep neural networks (DNNs). A change in radar operating frequency significantly changes the spectrogram characteristics compared to any other radar parameter. In this work, we consider three different approaches, viz., transfer learning, metric learning, and meta-learning (Reptile algorithm) for adapting the network developed for one radar operating frequency (source domain) to another operating frequency (target domain). Results and discussions on the performance of these algorithms on an openly available dataset are presented. [ABSTRACT FROM AUTHOR]

In the dynamic game between jammers and radars, it is easily affected by the complex electromagnetic environment, resulting in the generation of asymmetric radar information. In this letter, a jamming effect evaluation method for asymmetric radar information is proposed from the perspective of a jammer. An online jamming effect evaluation model is constructed. An improved class-attribute contingency coefficient (ICACC) method is proposed. The feature attribute weight and inconsistency rate are used to constrain the division process of breakpoints, so that the discretization of radar data is more reasonable. At the same time, a basic belief assignment (BBA) correction method that combines probability similarity for asymmetric information is proposed. The output results of the support vector machine (SVM) are corrected to reduce the influence of incomplete radar sample data on the discriminant and evaluation results. Experiments show that the proposed method can achieve efficient jamming effect evaluation under asymmetric radar information conditions. [ABSTRACT FROM AUTHOR]

Vortex electromagnetic (EM) waves, which carry orbital angular momentum (OAM), can provide more information of the target in radar imaging and target detection. Pulsed radar is often used in current OAM radar design. However, compared with the pulsed radar, the frequency-modulated continuous-wave (FMCW) radar is small in size, light in weight, and low in power consumption, which can be applied into many practical scenarios. Therefore, this letter investigates into the OAM radar based on the FMCW system. It can be found that, apart from the inherent characteristics of small blind area and high measurement accuracy in FMCW system, the OAM-FMCW radar also owns an additional degree of freedom, which is independent of the frequency, finally leading to the result that the FMCW radar will not be affected by the range and velocity of the target when performing azimuth detection. Simulation results are given to validate the corresponding analysis of the proposed system. [ABSTRACT FROM AUTHOR]

Accurate recognition of the types of mainlobe active deception jamming is essential for radar systems to take antijamming countermeasures. A bunch of deep-learning (DL)-based recognition methods that require large-scale datasets for training have shown promising results. However, capturing a sufficient number of diverse deception jamming samples is particularly intricate in actual dynamic and complex battlefields, the yielding limited or unbalanced datasets presents a significant challenge in training and generalizing DL models. This letter proposes a deep generative model, called the label-guided denoising diffusion probabilistic model (LG-DDPM), to address the issue of limited or class-imbalanced active deception jamming samples through data augmentation. By embedding label information into the diffusion process, the proposed model can generate and expand the active deception jamming samples specific to a predefined class even under low jamming-to-noise ratio (JNR) scenarios. The proposed method demonstrates superior performance in terms of both the fidelity and diversity of the generated jamming samples as well as the recognition accuracy of the DL recognizer when compared to state-of-the-art methods. Experimental results demonstrate the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Effective oil-spill monitoring is critical for timely response to minimize the impact on the environment. In response to the difficulty in extracting suspected oil films from marine radar images, a semantic segmentation method was proposed. In the preprocessing of training samples in similar scene, a slicing solution was used to compensate for the small sample of original data. The U-Net semantic segmentation network was used to classify oil film into two categories: real and suspected. Existing mainstream marine radar oil-spill identification methods were compared. Experimental results demonstrate that the proposed method achieves more reliability in oil-spill semantic segmentation. It can provide real-time information for oil-spill emergency response and disaster assessment. [ABSTRACT FROM AUTHOR]

In this letter, a constant false alarm rate (CFAR) detector is proposed for skywave over-the-horizon radar (OTHR) under Weibull distribution. First, an objective function, composed of a data fidelity term and regularization terms, is formulated in the Bayesian framework. The low rank prior of the distribution parameter matrix, smoothness of the parameters along the Doppler/range dimension, and sparsity of the target in the range-Doppler (RD) matrix are used to regularize the objective function. Then, the distribution parameters of all cells under test (CUTs) in the RD matrix are estimated by optimizing the objective function, and the abnormal CUTs with targets are identified and separated. Finally, target detection is performed by calculating the detection threshold through the threshold coefficient and estimated distribution parameters. Experiments on OTHR real data demonstrate that the proposed detector has a good CFAR property and significantly improved the probability of detection, especially in the case of multitarget and clutter edge. [ABSTRACT FROM AUTHOR]

In this study, we introduce MobileViTX, an enhanced MobileViT architecture for human activity recognition (HAR) in impulse radio ultrawideband (IR-UWB) radar applications. MobileViT is a lightweight Vision Transformer mainly consisting of MobileViT blocks and MobileNetv2 blocks. Modifications to the MobileNetv2 block include adding a drop path and a squeeze-and-excitation (SE) module and altering activation functions to hard-sigmoid and hard-swish. Additionally, the self-attention in the MobileViT block is transformed to possess linear complexity. These adjustments aim to accelerate inference while preserving high accuracy. We experiment with a dataset from 20 individuals performing 20 distinct actions, using fivefold cross-validation to assess our model’s performance. Results show that MobileViTX outperforms the original MobileViT and other models in both recognition rate and efficiency. [ABSTRACT FROM AUTHOR]

Conventional monostatic SAR or DBS technology cannot realize forward-looking imaging due to Doppler symmetry ambiguity. Although the ambiguity can be resolved using multiple channels in azimuth, its azimuth resolution in the vicinity of flight path is usually poor due to the small angle variation. To solve the above problems, a hybrid real/synthetic aperture scheme for multichannel radar forward-looking imaging is proposed in this letter. In the scheme, the synthetic aperture imaging result with left/right ambiguity is obtained first using the information of platform motion. Then the real aperture superresolution imaging with regularized iterative adaptive approach (RIAA) is achieved using the instantaneous data of multiple channels. Finally, the two imaging results are fused to obtain the forward-looking superresolution image without left/right ambiguity. Simulation results are given to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this letter, we designed a hemispherical refractive lens using ice for millimeter wave (MMW) imaging in the environment of −15 °C. Guided by geometrical optics theory, we calculated a suitable thickness of the lens to compensate for the phase of aperture field emitted by the antenna. Then, the 3 dB beamwidth was reduced from 17.6° to 2.1°. Meanwhile, the antenna gain is increased from 17 to 24 dB, which raises the maximum radar detection distance by 18%. Finally, we conducted experiments for MMW imaging to test the ice lens under different road conditions. Measuring results demonstrated that the image error rate has been significantly reduced and the peak signal-to-noise ratio (PSNR) has increased by more than 5 dB, which are consistent with simulated results. It should be noted that the ice lens can improve imaging quality and increase imaging resolution, making it widely applicable in the fields of Pole expedition and snow disaster rescue. [ABSTRACT FROM AUTHOR]

Along-track synthetic aperture radar interferometry (ATI) systems are used to detect a moving target and to estimate its radial velocity. A dual-channel (single-baseline) ATI system estimates the velocity ambiguously because there are many candidate velocities corresponding to the interferometric phase. To resolve the ambiguity of the estimates, a multichannel (multibaseline) ATI system is required. This letter proposes an efficient implementation technique of a multichannel ATI system by using a frequency-modulated continuous wave (FMCW) radar and an antenna arrangement method considering the inherent bi-static characteristic of FMCW radars. Also, a simple and robust radial velocity estimation algorithm suitable for the proposed system model is presented. The validity of the algorithm is confirmed with simulated ATI results. [ABSTRACT FROM AUTHOR]