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Compared with an extensive list of automotive radar datasets that support autonomous driving, indoor radar datasets are scarce at a smaller scale in the format of low-resolution radar point clouds and usually under an open-space single-room setting. In this paper, we scale up indoor radar data collection using multi-view high-resolution radar heatmap in a multi-day, multi-room, and multi-subject setting, with an emphasis on the diversity of environment and subjects. Referred to as the millimeter-wave multi-view radar (MMVR) dataset, it consists of $345$K multi-view radar frames collected from $25$ human subjects over $6$ different rooms, $446$K annotated bounding boxes/segmentation instances, and $7.59$ million annotated keypoints to support three major perception tasks of object detection, pose estimation, and instance segmentation, respectively. For each task, we report performance benchmarks under two protocols: a single subject in an open space and multiple subjects in several cluttered rooms with two data splits: random split and cross-environment split over $395$ 1-min data segments. We anticipate that MMVR facilitates indoor radar perception development for indoor vehicle (robot/humanoid) navigation, building energy management, and elderly care for better efficiency, user experience, and safety. The MMVR dataset is available at https://doi.org/10.5281/zenodo.12611978., Comment: 26 pages, 25 figures, 10 tables; See https://doi.org/10.5281/zenodo.12611978 to access the MMVR dataset

Radar-based perception has gained increasing attention in autonomous driving, yet the inherent sparsity of radars poses challenges. Radar raw data often contains excessive noise, whereas radar point clouds retain only limited information. In this work, we holistically treat the sparse nature of radar data by introducing an adaptive subsampling method together with a tailored network architecture that exploits the sparsity patterns to discover global and local dependencies in the radar signal. Our subsampling module selects a subset of pixels from range-doppler (RD) spectra that contribute most to the downstream perception tasks. To improve the feature extraction on sparse subsampled data, we propose a new way of applying graph neural networks on radar data and design a novel two-branch backbone to capture both global and local neighbor information. An attentive fusion module is applied to combine features from both branches. Experiments on the RADIal dataset show that our SparseRadNet exceeds state-of-the-art (SOTA) performance in object detection and achieves close to SOTA accuracy in freespace segmentation, meanwhile using sparse subsampled input data., Comment: 18 pages, 4 figures, 5 tables, with supplement

In this paper, for the first time to our knowledge, an explicit expression for the maximum detection range of an entangled quantum two-mode squeezed (QTMS) radar, in which a two-mode squeezed vacuum state of microwave electromagnetic fields is used, has been derived by considering both the quantum properties of the entangled microwave fields and radar parameters. By comparing this equation with that of traditional radars, we show that one can view a QTMS radar as a traditional radar with a reduced threshold signal-to-noise ratio. By discussing the current limitations, it has been shown that the critical parameter to achieve both simultaneous quantum advantage and substantial radar range is increasing the bandwidth of the generated output signal in the quantum entangled source. It has been demonstrated that, by considering the current feasible system parameters, it is possible to implement a QTMS radar with a maximum detection range of up to 2km, which is suitable for recognizing small unmanned aerial vehicles at urban distances. Moreover, based on the false alarm rate, we introduce two classes of early alarm and track QTMS radars. This approach can be generalized to other quantum radars with different types of quantum sources, such as electro-opto-mechanical sources, and may shed new light on investigating quantum radar systems for practical applications, such as far-distance ultrasensitive contactless vital sign detection and counter-Drone technology. Finally, we discuss potential outlooks to improve and develop quantum entangled radar systems to be practical from an engineering point of view.

This work presents a large-scale dataset for radar adaptive signal processing (RASP) applications, aimed at supporting the development of data-driven models within the radar community. The dataset, called RASPNet, consists of 100 realistic scenarios compiled over a variety of topographies and land types from across the contiguous United States, designed to reflect a diverse array of real-world environments. Within each scenario, RASPNet consists of 10,000 clutter realizations from an airborne radar setting, which can be utilized for radar algorithm development and evaluation. RASPNet intends to fill a prominent gap in the availability of a large-scale, realistic dataset that standardizes the evaluation of adaptive radar processing techniques. We describe its construction, organization, and several potential applications, which includes a transfer learning example to demonstrate how RASPNet can be leveraged for realistic adaptive radar processing scenarios.

Millimeter-wave (mmWave) radars are indispensable for perception tasks of autonomous vehicles, thanks to their resilience in challenging weather conditions. Yet, their deployment is often limited by insufficient spatial resolution for precise semantic scene interpretation. Classical super-resolution techniques adapted from optical imaging inadequately address the distinct characteristics of radar signal data. In response, our study redefines radar imaging super-resolution as a one-dimensional (1D) signal super-resolution spectra estimation problem by harnessing the radar signal processing domain knowledge, introducing innovative data normalization and a domain-informed signal-to-noise ratio (SNR)-guided loss function. Our tailored deep learning network for automotive radar imaging exhibits remarkable scalability, parameter efficiency and fast inference speed, alongside enhanced performance in terms of radar imaging quality and resolution. Extensive testing confirms that our SR-SPECNet sets a new benchmark in producing high-resolution radar range-azimuth images, outperforming existing methods across varied antenna configurations and dataset sizes. Source code and new radar dataset will be made publicly available online.

Tree defect detection is crucial for the structural health screening of trees. Existing nondestructive testing (NDT) techniques for tree defect detection require time-consuming and labor-intensive measurement campaigns. This discourages their application for the routine structural health screening of whole populations of managed urban trees. To address this issue, this study proposes a deep-learning augmented stand-off radar scheme for contactless scanning of tree trunks and rapid detection of tree defects. In this scheme, the antenna is moved along a straight trajectory at a distance from the tree trunk to obtain the trunk's B-scan. The obtained raw B-scan is then processed by a signal-processing framework specifically developed for revealing the scattering signatures of defects in B-scan, which achieves a 30 dB and 22 dB increase in the signal-to-clutter and noise ratio of the measurement data of tree trunk samples and living trees, respectively. Finally, the processed B-scan is input into a multilevel feature fusion neural network particularly designed for extracting the signature of the defect in the processed B-scan in real time. The developed scheme's applications to the detection of defects in real fresh-cut tree trunks show that the stand-off radar scheme can detect tree defects with 96% accuracy. This stand-off radar scheme is the first contactless NDT technique for tree defect detection while operated on a straight trajectory and potentially can be integrated into the routine tree inspection workflow which is part of urban tree management., Comment: Accepted and to be published in IEEE Transactions on Geoscience and Remote Sensing

The detection of multiple extended targets in complex environments using high-resolution automotive radar is considered. A data-driven approach is proposed where unlabeled synchronized lidar data is used as ground truth to train a neural network with only radar data as input. To this end, the novel, large-scale, real-life, and multi-sensor RaDelft dataset has been recorded using a demonstrator vehicle in different locations in the city of Delft. The dataset, as well as the documentation and example code, is publicly available for those researchers in the field of automotive radar or machine perception. The proposed data-driven detector is able to generate lidar-like point clouds using only radar data from a high-resolution system, which preserves the shape and size of extended targets. The results are compared against conventional CFAR detectors as well as variations of the method to emulate the available approaches in the literature, using the probability of detection, the probability of false alarm, and the Chamfer distance as performance metrics. Moreover, an ablation study was carried out to assess the impact of Doppler and temporal information on detection performance. The proposed method outperforms the different baselines in terms of Chamfer distance, achieving a reduction of 75% against conventional CFAR detectors and 10% against the modified state-of-the-art deep learning-based approaches., Comment: Under review at IEEE Transaction on Radar Systems

This paper proposes a short-range FMCW radar-based facial authentication and out-of-distribution (OOD) detection framework. Our pipeline jointly estimates the correct classes for the in-distribution (ID) samples and detects the OOD samples to prevent their inaccurate prediction. Our reconstruction-based architecture consists of a main convolutional block with one encoder and multi-decoder configuration, and intermediate linear encoder-decoder parts. Together, these elements form an accurate human face classifier and a robust OOD detector. For our dataset, gathered using a 60 GHz short-range FMCW radar, our network achieves an average classification accuracy of 98.07% in identifying in-distribution human faces. As an OOD detector, it achieves an average Area Under the Receiver Operating Characteristic (AUROC) curve of 98.50% and an average False Positive Rate at 95% True Positive Rate (FPR95) of 6.20%. Also, our extensive experiments show that the proposed approach outperforms previous OOD detectors in terms of common OOD detection metrics., Comment: Accepted at ICIP 2024

Radar sensors are low cost, long-range, and weather-resilient. Therefore, they are widely used for driver assistance functions, and are expected to be crucial for the success of autonomous driving in the future. In many perception tasks only pre-processed radar point clouds are considered. In contrast, radar spectra are a raw form of radar measurements and contain more information than radar point clouds. However, radar spectra are rather difficult to interpret. In this work, we aim to explore the semantic information contained in spectra in the context of automated driving, thereby moving towards better interpretability of radar spectra. To this end, we create a radar spectra-language model, allowing us to query radar spectra measurements for the presence of scene elements using free text. We overcome the scarcity of radar spectra data by matching the embedding space of an existing vision-language model. Finally, we explore the benefit of the learned representation for scene retrieval using radar spectra only, and obtain improvements in free space segmentation and object detection merely by injecting the spectra embedding into a baseline model.

Effective tracking of surrounding traffic participants allows for an accurate state estimation as a necessary ingredient for prediction of future behavior and therefore adequate planning of the ego vehicle trajectory. One approach for detecting and tracking surrounding traffic participants is the combination of a learning based object detector with a classical tracking algorithm. Learning based object detectors have been shown to work adequately on lidar and camera data, while learning based object detectors using standard radar data input have proven to be inferior. Recently, with the improvements to radar sensor technology in the form of imaging radars, the object detection performance on radar was greatly improved but is still limited compared to lidar sensors due to the sparsity of the radar point cloud. This presents a unique challenge for the task of multi-object tracking. The tracking algorithm must overcome the limited detection quality while generating consistent tracks. To this end, a comparison between different multi-object tracking methods on imaging radar data is required to investigate its potential for downstream tasks. The work at hand compares multiple approaches and analyzes their limitations when applied to imaging radar data. Furthermore, enhancements to the presented approaches in the form of probabilistic association algorithms are considered for this task., Comment: Presented at: 9. International ATZ-Live Automated Driving 2024

In data mining, density-based clustering, which entails classifying datapoints according to their distributions in some space, is an essential method to extract information from large datasets. With the advent of software-based radio, ionospheric radars are capable of producing unprecedentedly large datasets of plasma turbulence backscatter observations, and new automatic techniques are needed to sift through them. We present an algorithm to automatically identify and track clusters of radar echoes through time, using dbscan, a celebrated density-based clustering method for noisy point-clouds. We demonstrate its efficiency by tracking turbulent structures in the E-region ionosphere, the so-called radar aurora. Through conjugate auroral imagery, as well as in-situ satellite observations, we demonstrate that the observed turbulent structures generally track the motion of the aurora. We discuss instances when this prediction is brought to fruition and when it ostensibly is not. Through case studies, we highlight the important instances when the radar echo bulk motions vary considerably around discrete auroral arcs, an effect we argue is produced by strong electric field modulations caused by energetic particle precipitation., Comment: 11 pages, 6 figures

With the rapid advancement of autonomous driving technology, there is a growing need for enhanced safety and efficiency in the automatic environmental perception of vehicles during their operation. In modern vehicle setups, cameras and mmWave radar (radar), being the most extensively employed sensors, demonstrate complementary characteristics, inherently rendering them conducive to fusion and facilitating the achievement of both robust performance and cost-effectiveness. This paper focuses on a comprehensive survey of radar-vision (RV) fusion based on deep learning methods for 3D object detection in autonomous driving. We offer a comprehensive overview of each RV fusion category, specifically those employing region of interest (ROI) fusion and end-to-end fusion strategies. As the most promising fusion strategy at present, we provide a deeper classification of end-to-end fusion methods, including those 3D bounding box prediction based and BEV based approaches. Moreover, aligning with recent advancements, we delineate the latest information on 4D radar and its cutting-edge applications in autonomous vehicles (AVs). Finally, we present the possible future trends of RV fusion and summarize this paper.

In this paper, we present a comprehensive study on the application of YOLOv8, a state-of-the-art computer vision (CV) model, to the challenging problem of joint detection and classification of signals in a highly dynamic and congested RF environment. Using our synthetic RF datasets, we explored three different scenarios with congested communication and radar signals. In the first study, we applied YOLOv8 to detect and classify multiple digital modulation signals coexisting within a highly congested and dynamic spectral environment with significant overlap in both frequency and time domains. The trained model was able to achieve an impressive mean average precision (mAP) of 0.888 at an IoU threshold of 50%, signifying its robustness against spectral congestion. The second part of our research focuses on the detection and classification of multiple polyphase pulse radar signals, including Frank code and P1 through P4 codes. We were able to successfully train YOLOv8 to deliver a nearly perfect mAP50 score of 0.995 in a densely populated signal environment, further showcasing its capability in radar signal processing. In the last scenario, we demonstrated that the model can also be applied to the multi-target detection problem for continuous-wave radar. The synthetic datasets used in these experiments reflect a realistic mix of communication and radar signals with varying degrees of interference and congestion - a setup that has been overlooked by many past research efforts, which have primarily focused on ML-based classification of digital communication signal modulation schemes. Our study demonstrated the potential of advanced CV models in addressing spectrum sensing challenges in congested and dynamic RF environments involving both communication and radar signals. We hope our findings will spur further collaborative efforts to tackle the complexities of congested RF spectrum environments., Comment: Submitted to IEEE MILCOM 2024

This paper investigates the application of millimeter-wave (mmWave) radar for the estimation of human vital signs. Aiming to obtain more accurate frequency estimation for periodic signals of respiration and heartbeat, we propose the harmonic MUSIC (HMUSIC) algorithm to consider harmonic components for frequency estimation of vital sign signals. In the experiments, we tested different subjects' vital signs. Experimental results demonstrate that the 89-th percentile errors in respiration rate and the 88-th percentile errors in heartbeat rate are less than 3 respirations per minute and 5 beats per minute.

A strong increase of the reflection loss (from 25 up to 35 dBs) and an extension of the absorption bandwidth up to 20% under far-field real radar conditions are experimentally reported in this work by creating a functional bilayer system. Each layer consists of a composite material, typically a dielectric matrix filled with magnetic entities (powder or wires). Combining the two types of materials into a bilayer structure has shown unprecedented improvements in microwave absorption capacities compared to the former absorption of each layer. The potential of random magnets to create thin broadband absorbers is also demonstrated. The capacity to improve the shielding behavior is strongly related to each layer's permittivity, permeability, and thickness. This provides strong control over the design of novel materials for stealth applications.

High Resolution Range Profiles (HRRP) have become a key area of focus in the domain of Radar Automatic Target Recognition (RATR). Despite the success of data-driven neural network-based HRRP recognition, challenges such as insufficient training samples persist in its real-world application. This letter introduces HRRPGraphNet, a novel Graph Neural Network (GNN) model designed specifically for HRRP target recognition that leverages new insights to address these challenges. A pivotal innovation is the transformation of HRRP data into a graph structure, utilizing a range cell amplitude-based node vector and a range-relative adjacency matrix. This graph-based approach facilitates both local feature extraction via one-dimensional convolution layers and global feature extraction through a graph convolution layer, capitalizing on the intrinsic relationships between range cells which is a distinct advantage over existing sequence-based methods. Experiments on the aircraft electromagnetic simulation dataset and the measured dataset have confirmed HRRPGraphNet's superior accuracy and robustness, particularly in fewer training sample environments, underscoring the potential of graph-driven innovations in HRRP-based RATR., Comment: 5 pages, 4 figures

The electromagnetic properties and microwave absorption capabilities are studied under real radar conditions for layer-type composite samples consisting of a polymeric matrix and a magnetic powder filler, either metallic or ceramic. The effect of the filler type and the filling factor is investigated. The obtained results show remarkable broadband microwave absorption, which make these materials suitable for stealth technology. The experimentally measured absorptions reach -40 dB, while model-based predictions suggest that these systems could overpass the barrier of -50 dB. The experimental results are supported by models, both for single and bilayer systems.

Two beam broadening methods for active electronically scanned array (AESA) antennas with uniform amplitude excitation are proposed and compared: phase tapering optimization (PTO) and a novel time-varying phase tapering (TPT). The PTO is a simple and efficient approach assuming continuous polynomial phase distribution and requiring optimization of only few parameters. The TPT is valid mainly for radar applications, taking advantage of the fact that radars typically transmit pulse trains for coherent integration. By varying the array elements' phases from pulse to pulse, the TPT achieves effective amplitude tapering, thus providing a method of beam shaping, occasionally with a simple analytic form. The TPT also makes it possible to produce beam shaping with very low side lobe levels in comparison to the PTO. As a preliminary step, the dimensionality of the radiation pattern characterization for all scan directions is reduced from five to only two variables. This is crucial for efficient optimization of the radiation pattern which needs to be evaluated over a judiciously specified two-dimensional domain.

Multiple Signal Classification (MUSIC) is a widely used Direction of Arrival (DoA)/Angle of Arrival (AoA) estimation algorithm applied to various application domains such as autonomous driving, medical imaging, and astronomy. However, MUSIC is computationally expensive and challenging to implement in low-power hardware, requiring exploration of trade-offs between accuracy, cost, and power. We present MUSIC-lite, which exploits approximate computing to generate a design space exploring accuracy-area-power trade-offs. This is specifically applied to the computationally intensive singular value decomposition (SVD) component of the MUSIC algorithm in an orthogonal frequency-division multiplexing (OFDM) radar use case. MUSIC-lite incorporates approximate adders into the iterative CORDIC algorithm that is used for hardware implementation of MUSIC, generating interesting accuracy-area-power trade-offs. Our experiments demonstrate MUSIC-lite's ability to save an average of 17.25% on-chip area and 19.4% power with a minimal 0.14% error for efficient MUSIC implementations., Comment: Paper accepted at ESWEEK-CASES 2024 as a Late Breaking (LB) Result paper. The definitive version of the work will appear in IEEE Embedded Systems Letters

Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a principled architecture which employs Variational Auto-Encoders for estimating a latent distribution responsible for generating the data, and Evidential Deep Learning for its ability to sense out-of-distribution activities. We verify that the fused data processed by different antennas of the same Wi-Fi receiver results in increased accuracy of human activity recognition compared with the most recent benchmarks, while still being informative when facing out-of-distribution samples and enabling semantic interpretation of latent variables in terms of physical phenomena. The results of this paper are a first contribution toward the ultimate goal of providing a flexible, semantic characterisation of black-swan events, i.e., events for which we have limited to no training data., Comment: 8 pages, 10 figures, accepted at 26th International Conference on Information Fusion (FUSION 2023)

We introduce a transformer-based neural network to generate high-resolution (3km) synthetic radar reflectivity fields at scale from geostationary satellite imagery. This work aims to enhance short-term convective-scale forecasts of high-impact weather events and aid in data assimilation for numerical weather prediction over the United States. Compared to convolutional approaches, which have limited receptive fields, our results show improved sharpness and higher accuracy across various composite reflectivity thresholds. Additional case studies over specific atmospheric phenomena support our quantitative findings, while a novel attribution method is introduced to guide domain experts in understanding model outputs., Comment: Published as a workshop paper at "Machine Learning for Earth System Modeling", ICML 2024; added acknowledgements and github link

This paper presents two new, simple yet effective approaches to measure the vibration of a swaying millimeter-wave radar (mmRadar) utilizing geometrical information. Specifically, for the planar vibrations, we firstly establish an equation based on the area difference between the swaying mmRadar and the reference objects at different moments, which enables the quantification of planar displacement. Secondly, volume differences are also utilized with the same idea, achieving the self-vibration measurement of a swaying mmRadar for spatial vibrations. Experimental results confirm the effectiveness of our methods, demonstrating its capability to estimate both the amplitude and a crude direction of the mmRadar's self-vibration., Comment: 5 pages, 4 figures,submitted to the IEEE for publication

In this work, we consider a backscatter communication system wherein multiple asynchronous sources (tags) exploit the reverberation generated by a nearby radar transmitter as an ambient carrier to deliver a message to a common destination (reader) through a number of available subchannels. We propose a new encoding strategy wherein each tag transmits both pilot and data symbols on each subchannel and repeats some of the data symbols on multiple subchannels. We then exploit this signal structure to derive two semi-blind iterative algorithms for joint estimation of the data symbols and the subchannel responses that are also able to handle some missing measurements. The proposed encoding/decoding strategies are scalable with the number of tags and their payload and can achieve different tradeoffs in terms of transmission and error rates. Some numerical examples are provided to illustrate the merits of the proposed solutions., Comment: Submitted to the IEEE Transactions on Wireless Communications

Rydberg atomic sensors and receivers have enabled sensitive and traceable measurements of RF fields at a wide range of frequencies. Here we demonstrate the detection of electric field amplitude in the extremely high frequency (EHF) band, at $131\ \mathrm{GHz}$. In our approach we propagate the EHF field in a beam, with control over its direction and polarization at the detector using photonic waveplates. This way, we take advantage of the highest detection sensitivity, registered for collinear propagation and circular polarization. To exhibit the potential for applications in this kind of Rydberg-atom based detection, we perform test measurements on the EHF field emitted from an on-chip radar, planned to be used in automotive industry as a vital sign detector. Our work elucidates practical applications of Rydberg-atom media as well as photonic metamaterial elements., Comment: 6 pages, 6 figures

The impact of the expansion of a high-frequency radar (HFR) system in a dynamic coastal area (the Ibiza Channel in the Western Mediterranean Sea) is evaluated through an Observing System Simulation Experiment (OSSE). The installation of two new antennas in the Iberian Peninsula would complement the existing ones in the islands of Ibiza and Formentera, providing surface currents observations of the full channel. Two different configurations of the same model, validated to give realistic simulations, are used: i) a Nature Run (NR) which is considered as the real ocean state and that is used to generate pseudo-observations, and ii) a Control Run (CR) in which the pseudo-observations are assimilated. The OSSE is first validated by comparison against a previous Observing System Experiment (OSE). The impact of the new antennas for forecasting surface currents is evaluated in two different periods with different levels of agreement between NR and CR. The HFR expansion is found to contribute to significantly correct the circulation patterns in the Channel, leading to surface merdional velocity error reductions up to 19%. The effects on the transport in the area are also analyzed from a Lagrangian perspective, showing that DA can help to better represent the Lagrangian Coherent Structures present in the NR and constrain the ocean dynamics.

Neural fields have been broadly investigated as scene representations for the reproduction and novel generation of diverse outdoor scenes, including those autonomous vehicles and robots must handle. While successful approaches for RGB and LiDAR data exist, neural reconstruction methods for radar as a sensing modality have been largely unexplored. Operating at millimeter wavelengths, radar sensors are robust to scattering in fog and rain, and, as such, offer a complementary modality to active and passive optical sensing techniques. Moreover, existing radar sensors are highly cost-effective and deployed broadly in robots and vehicles that operate outdoors. We introduce Radar Fields - a neural scene reconstruction method designed for active radar imagers. Our approach unites an explicit, physics-informed sensor model with an implicit neural geometry and reflectance model to directly synthesize raw radar measurements and extract scene occupancy. The proposed method does not rely on volume rendering. Instead, we learn fields in Fourier frequency space, supervised with raw radar data. We validate the effectiveness of the method across diverse outdoor scenarios, including urban scenes with dense vehicles and infrastructure, and in harsh weather scenarios, where mm-wavelength sensing is especially favorable., Comment: 8 pages, 6 figures, to be published in SIGGRAPH 2024

Radar sensors have a long tradition in advanced driver assistance systems (ADAS) and also play a major role in current concepts for autonomous vehicles. Their importance is reasoned by their high robustness against meteorological effects, such as rain, snow, or fog, and the radar's ability to measure relative radial velocity differences via the Doppler effect. The cause for these advantages, namely the large wavelength, is also one of the drawbacks of radar sensors. Compared to camera or lidar sensor, a lot more surfaces in a typical traffic scenario appear flat relative to the radar's emitted signal. This results in multi-path reflections or so called ghost detections in the radar signal. Ghost objects pose a major source for potential false positive detections in a vehicle's perception pipeline. Therefore, it is important to be able to segregate multi-path reflections from direct ones. In this article, we present a dataset with detailed manual annotations for different kinds of ghost detections. Moreover, two different approaches for identifying these kinds of objects are evaluated. We hope that our dataset encourages more researchers to engage in the fields of multi-path object suppression or exploitation.

We address the challenge of spectral sharing between a statistical multiple-input multiple-output (MIMO) radar and an in-band full-duplex (IBFD) multi-user MIMO (MU-MIMO) communications system operating simultaneously in the same frequency band. Existing research on joint MIMO-radar-MIMO-communications (MRMC) systems has limitations, such as focusing on colocated MIMO radars, half-duplex MIMO communications, single-user scenarios, neglecting practical constraints, or employing separate transmit/receive units for MRMC coexistence. This paper, along with companion papers (Part I and III), proposes a comprehensive MRMC framework that addresses all these challenges. In the previous companion paper (Part I), we presented signal processing techniques for a distributed IBFD MRMC system. In this paper, we introduce joint design of statistical MIMO radar codes, uplink/downlink precoders, and corresponding receive filters using a novel metric called compounded-and-weighted sum mutual information. To solve the resulting highly non-convex problem, we employ a combination of block coordinate descent (BCD) and alternating projection methods. Numerical experiments show convergence of our algorithm, mitigation of uplink interference, and stable data rates under varying noise levels, channel estimate imperfections, and self-interference. The subsequent companion paper (Part III) extends the discussion to multiple targets and evaluates the tracking performance of our MRMC system., Comment: 25 pages, 5 figures. arXiv admin note: text overlap with arXiv:2006.14774

Maritime radars are prevalently adopted to capture the vessel's omnidirectional data as imagery. Nevertheless, inherent challenges persist with marine radars, including limited frequency, suboptimal resolution, and indeterminate detections. Additionally, the scarcity of discernible landmarks in the vast marine expanses remains a challenge, resulting in consecutive scenes that often lack matching feature points. In this context, we introduce a resilient maritime radar scan representation LodeStar, and an enhanced feature extraction technique tailored for marine radar applications. Moreover, we embark on estimating marine radar odometry utilizing a semi-direct approach. LodeStar-based approach markedly attenuates the errors in odometry estimation, and our assertion is corroborated through meticulous experimental validation., Comment: IEEE Robotics and Automation Letter

This paper analyzes monostatic sensing by a user equipment (UE) for a setting in which the UE is unable to resolve multiple targets due to their interference within a single resolution bin. It is shown how sensing accuracy, in terms of both detection rate and localization accuracy, can be boosted by a reconfigurable intelligent surface (RIS), which can be advantageously used to provide signal diversity and aid in resolving the targets. Specifically, assuming prior information on the presence of a cluster of targets, a RIS beam sweep procedure is used to facilitate the high resolution sensing. We derive the Cram\'er-Rao lower bounds (CRLBs) for channel parameter estimation and sensing and an upper bound on the detection probability. The concept of coherence is defined and analyzed theoretically. Then, we propose an orthogonal matching pursuit (OMP) channel estimation algorithm combined with data association to fuse the information of the non-RIS signal and the RIS signal and perform sensing. Finally, we provide numerical results to verify the potential of RIS for improving sensor resolution, and to demonstrate that the proposed methods can realize this potential for RIS-assisted high resolution sensing.

Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body's interaction with electromagnetic signals. Current Wi-Fi sensing applications predominantly employ data-driven learning techniques to associate the fluctuations in the physical properties of the communication channel with the human activity causing them. However, these techniques often lack the desired flexibility and transparency. This paper introduces DeepProbHAR, a neuro-symbolic architecture for Wi-Fi sensing, providing initial evidence that Wi-Fi signals can differentiate between simple movements, such as leg or arm movements, which are integral to human activities like running or walking. The neuro-symbolic approach affords gathering such evidence without needing additional specialised data collection or labelling. The training of DeepProbHAR is facilitated by declarative domain knowledge obtained from a camera feed and by fusing signals from various antennas of the Wi-Fi receivers. DeepProbHAR achieves results comparable to the state-of-the-art in human activity recognition. Moreover, as a by-product of the learning process, DeepProbHAR generates specialised classifiers for simple movements that match the accuracy of models trained on finely labelled datasets, which would be particularly costly., Comment: 8 pages, 9 figures, accepted at 27th International Conference on Information Fusion (FUSION 2024)

Current satellite precipitation retrievals like GPROF assume that brightness temperature is sufficient to constrain rainfall. This information, however, often represents multiple rain states, resulting in rainfall estimate uncertainties. These uncertainties, while dominated by random variability, can also exhibit substantial regional biases, complicating the use of traditional ground validation techniques which seek to understand these uncertainties. This study aims to characterize the physical contributors to these biases for use in uncertainty quantification. To do this, coincident GPROF Version 7, GMI, and GPM Combined observations were examined over three tropical land regions, the Amazon, Congo, and Southeast Asia, which are known to exhibit distinct biases relative to one another when comparing GPROF with GPM Combined. Rain intensity and ice-rain ratio were identified as the primary sources of GPROF regional biases. By incorporating the information from these sources, the self-similarity between these three regions was brought from within 13 percent to within 7 percent, reducing the interregional bias by half. Including a third constraint based on the polarization-corrected 37-GHz brightness temperature further improved this self-similarity to within 4 percent by accounting for second-order hydrometeor profile differences which were underutilized by GPROF. Comparing the effects of these three constraints between GPROF Version 7 with the 1D version of GPROF-NN showed similar improvements, indicating the utility of this uncertainty quantification and adjustment method across precipitation products. With these constraints, regional GPROF biases can be made more consistent, improving the fidelity of the precipitation climate data records and operational precipitation products which utilize this information., Comment: This work has been submitted to the IEEE Transactions on Geoscience and Remote Sensing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible