In this article, the classification of dynamic vulnerable road users (VRUs) using polarimetric automotive radar is considered. To this end, a signal processing pipeline for polarimetric automotive MIMO radar is proposed, including a method to enhance angular resolution by combining data from all polarimetric channels. The proposed signal processing pipeline is applied to measurement data of three different types of VRUs and a car, collected with a custom automotive polarimetric radar, developed in collaboration with Huber+Suhner AG. Several polarimetric features are estimated from the range-velocity signatures of the measured targets and are subsequently analyzed. A Bayesian classifier and a convolutional neural network (CNN) using these estimated polarimetric features are proposed and their performance is compared against their single-polarized counterparts. It is found that for the Bayesian classifier, a significant increase in classification performance is achieved, compared to the same classifier using single polarized information. For the CNN-based classifier, utilizing the distribution of polarimetric features of the target’s range-velocity signatures also increases classification performance, compared to its single-polarized version. This shows that polarimetric information is valuable for classification of VRUs and objects of interest in automotive radar.@en

Monitoring gait symmetry reliably is crucial, as it is an early indicator of Parkinson's disease (PD). In this work, a method is presented to analyze gait asymmetries using a 24-GHz frequency-modulated continuous-wave (FMCW) multiple-input-multiple-output (MIMO) radar in a nonclinical environment. The proposed method is validated by analyzing the gait of 60 people recorded in an environment that presents multiple challenges such as multipath, gait interruption events, and different trajectories, which cause aspect angle variability. This article presents algorithms to address these challenges to extract useful gait information from the radar data and discusses the performance of the proposed system, analyzing the ratio of correct feet identifications and the accuracy of the asymmetry gait parameters. It achieved a mean absolute error of the symmetry ratio below 8% for all gait parameters. This shows that the proposed system and algorithms are robust for both clinical and in-home implementations.@en

One of the key problems in automotive radar is its limited cross-range resolution, despite many approaches developed to address this. Conventional high-resolution algorithms in synthetic aperture radar (SAR) can provide good resolution imaging ability but suffer from high computational costs. In this paper, a computationally efficient high-resolution imaging algorithm is proposed, easing its potential implementation for higher throughput. The proposed approach is verified with experimental data of realistic driving scenarios.

@en

This paper focuses on the challenge of estimating the 2D instantaneous ego -motion of vehicles equipped with an automotive radar. To further improve our previous study based on the weighted least squares (wLSQ) method and purpose-designed neural networks (NNs), this work proposes a new network architecture that supports local and global feature extraction as well as point-wise dynamic feature channel mixing. Compared with our previous work, the proposed method provides better estimation accuracy, lighter network size, and faster runtime performance.

@en

In this paper, we address the limitations of traditional constant false alarm rate (CFAR) target detectors in automotive radars, particularly in complex urban environments with multiple objects that appear as extended targets. We propose a data-driven radar target detector exploiting a highly efficient 2D CNN backbone inspired by the computer vision domain. Our approach is distinguished by a unique cross-sensor supervision pipeline, enabling it to learn exclusively from unlabeled synchronized radar and lidar data, thus^eliminating the need for costly manual object annotations. Using a novel large-scale, real-life multi-sensor dataset recorded in various driving scenarios, we demonstrate that the proposed detector generates dense, lidar-like point clouds, achieving a lower Chamfer distance to the reference lidar point clouds than CFAR detectors. Overall, it significantly outperforms CFAR baselines detection accuracy.

@en

The application of distributed radar to human motion monitoring is considered. A novel sensor fusion method has been proposed that yields a two-dimensional map of reflection intensity and a vector field of reconstructed velocities in lieu of conventional Doppler spectrograms or radial velocity components. The method has been verified using experimental datasets in two case studies involving fall detection in sequences of activities, and arm motion discrimination for in-place activities. A true positive rate and precision of respectively 99.3 % and 93.0 % have been demonstrated for the fall detection task, and the output of the proposed method for arm motion characterisation indicates suitability for classification in future research.

@en

In this paper, the problem of formulating effective processing pipelines for indoor human tracking is investigated, with the usage of a Multiple Input Multiple Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar. Specifically, two processing pipelines starting with detections on the Range-Azimuth (RA) maps and the Range-Doppler (RD) maps are formulated and compared, together with subsequent clustering and tracking algorithms and their relevant parameters. Experimental results are presented to validate and assess both pipelines, using a 24 GHz commercial radar platform with 250 MHz bandwidth and 15 virtual channels. Scenarios where 1 and 2 people move in an indoor environment are considered, and the influence of the number of virtual channels and detectors' parameters is discussed. The characteristics and limitations of both pipelines are presented, with the approach based on detections on RA maps showing in general more robust results.

@en

In this paper the problem of tracking multiple people in an indoor environment is formulated and analyzed with the usage of a Multiple Input Multiple Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar. The objective is to evaluate the performance of FMCW MIMO radar with relatively limited bandwidth for accurately tracking single and multiple individuals in various scenarios. Three different tracking approaches are compared and quantitatively analyzed with single and multi-target scenarios. As the angular resolution is significant for distinguishing multiple targets, the effect of the number of MIMO channels used is compared among different trackers. The performance is analyzed by metrics of distance error and cardinality error in track association/assignment.

@en

A feasibility study to detect the respiratory rate and heart rate of primates using non-invasive contactless radar sensors and a dedicated processing pipeline is performed. The proposed approach is validated using measurement data from an individual bonobo, simultaneously collected by two different types of radar sensors (pulsed Ultra Wide Band and FMCW, operating at different frequencies). The results show that it is feasible to infer both respiration and heart rate data from the radar sensors.

@en

A new modification to a method to determine the normalised radar cross section of road surfaces is proposed, so that the radar cross section of anisotropic pavement materials can also be considered. This method is applied to two types of brick pavements with different bond patterns under two different azimuth angles, both in wet and dry conditions. It is demonstrated that the radar cross section of anisotropic road surfaces can vary significantly as function on the azimuthal observation angle, and that it is dependent on the bond pattern of the pavement.

@en

A neural network (NN) based multi-frame classification approach is proposed to solve the problem of classification of tracked objects. Initially, a baseline tracker is implemented that uses the classification output of an object detection network for classification. Afterwards, two approaches for multi-frame classification are applied to perform classification of tracked objects. The first approach aggregates points from multiple frames and applies a single frame NN for classification, whereas the second approach uses bidirectional long short term memory (BiLSTM) layers to process points from multiple frames. Extensive experiments on the opensource 2D RadarScenes dataset showed a consistent increase in track performance when using either of the two techniques for multi-frame classification.@en

Automatic target classification or recognition is a critical capability in noncooperative surveillance with radar in several defence and civilian applications. It is a well-established research field and numerous techniques exist for recognizing targets, including miniature unmanned air systems or drones (i.e., small, mini, micro, and nano platforms), from their radar signatures. These algorithms have notably benefited from advances in machine learning (e.g., deep neural networks) and are increasingly able to achieve remarkably high accuracies. Such classification results are often captured by standard, generic, object recognition metrics, and originate from testing on simulated or real radar measurements of drones under high signal to noise ratios. Hence, it is difficult to assess and benchmark the performance of different classifiers under realistic operational conditions. In this article, we first review the key challenges and considerations associated with the automatic classification of miniature drones from radar data. We then present a set of important performance measures, from an end-user perspective. These are relevant to typical drone surveillance system requirements and constraints. Selected examples from real radar observations are shown for illustration. We also outline here various emerging approaches and future directions that can produce more robust drone classifiers for radar.

@en

The effect of different time-frequency (TF) resolution values is analyzed in the context of Human Activity Recognition (HAR) using multiple radars distributed in a network. Specifically, different spectrograms computed with various Short-Time Fourier Transform (STFT) window lengths and Morse wavelet transform are compared as input representation to a Convolutional Neural Network (CNN), together with a coherent combination of multiple spectrograms. The study emphasizes the importance of selecting appropriate window sizes for TF analysis and for classification, balancing the observation time with the physical duration of the diverse activities, and also avoiding correlation between different data samples that may compromise the generalization ability of the method. The results employing this coherent sensor fusion demonstrate the efficacy of the investigated method, achieving an F1 score of 0.943 on a challenging public dataset containing 9 activities performed by 15 participants.

@en

The design of bespoke adaptive detection schemes relying on the joint use of multistatic/polarimetric measurements requires a preliminary statistical inference on the clutter interference environment. This is of paramount importance to develop an analytic model for the received signal samples, which is mandatory for the synthesis of radar detectors. In this respect, the aim of this article is the development of suitable learning tools to study some important statistical features of the sea-clutter environment perceived at the nodes of a multistatic/polarimetric radar system. Precisely, the stationarity of the data in the slow-time domain is first assessed by resorting to generalized inner product (GIP) based statistics. Then, the possible presence of structural symmetries in the clutter covariance matrices is investigated. Finally, relationships between some statistical parameters characterizing the sea-clutter returns on the bistatic polarimetric channels are explored via specific sequential hypothesis testing. This research activity is complemented by the use of radar returns measured via the netted RADar (NetRAD), which collects simultaneously monostatic and bistatic polarimetric measurements. The results indicate that the analyzed data can be modeled as drawn from a stationary Gaussian process within the coherence time. In addition, the bistatic returns on the different polarimetric channels can be assumed statistically independent with speckle components possibly exhibiting proportional/equal covariance matrices depending on the transmit/receive polarization and bistatic geometry.

@en

Passive sensing exploiting Wi-Fi as an illuminator of opportunity has attracted considerable interest for the ubiquitous presence of Wi-Fi systems in many environments, and the relative low cost & complexity of related passive solutions. In this paper, we consider a reference-free, amplitude-based sensing strategy exploiting Wi-Fi signals of opportunity and we investigate the potential advantage conveyed by the use of polarimetric diversity on receive. Specifically, we exploit the data collected by a dual-pol receiver to experimentally demonstrate that the joint availability of the signals collected by differently polarized antennas (H and V) could remarkably enhance the detection performance of the resulting Wi-Fi sensor, even if based on such a simple and cost-effective reference-free sensing approach. Moreover, we analyze the characteristics of the target signatures extracted by the considered amplitude-based approach for either single and dual-pol receivers, in order to investigate their suitability for detecting and recognizing different human movements, in view of future applications for automatic classification.

@en

This paper examines how training data affects machine learning-assisted antenna pattern prediction under mutual coupling. For demonstration, a neural network-based approach is used to predict the embedded pattern of a central patch antenna element near randomly distributed patches. It is shown that when the full-wave simulated dataset size is excessively reduced, the high prediction error in the validation set may become a critical issue. Maintaining sufficient accuracy in pattern prediction with a relatively small dataset remains an open challenge.@en

Fall detection systems can play an important role in assuring safe independent living for vulnerable people. These sensors not only have to detect falls but also have to recognize uncritical, normal activities of daily living in order to differentiate them from falls. Radar sensors are very attractive for human activity recognition thanks to their contactless capabilities and lack of plain videos recorded. In this article, a novel approach to recognize single activities in a continuous stream of radar data is proposed, whereby the stream is divided into windows of fixed length and, then, multilabel classification is used to recognize all activities taking place in these time segments. While the initial feasibility of this approach was presented in an earlier contribution presented at the 2023 IEEE SENSORS conference, in this extended work, additional in-depth studies on critical parameters are performed. Specifically, multiple combinations of different radar data domains/representations (e.g., range-time maps, range-Doppler maps, and spectrograms) and different radar nodes in a network of five cooperating sensors are considered as inputs to two considered multilabel classification networks. In addition, a parametric study on the probability thresholds of the networks to assign labels to specific classes is also performed.

@en

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.

@en

Mutual interference between automotive frequency-modulated continuous-wave (FMCW) radar systems has been a concern over recent years. Several interference mitigation (IM) techniques have been proposed to mitigate this phenomenon, which is deemed to grow in severity as more systems are deployed on the road. In this article, an inexpensive technique, based on well-known moving target indicator (MTI) processing, is proposed to separate interference from target signals. It exploits the contrast after stretch processing between uncorrelated FMCW interference (sparse and chirp-like) and beat signals (stable sinusoids). The interference is therefore marked and subtracted. This eliminates interference at the cost of introducing a distortion dependent on the relative radial velocity of the targets. To validate the proposed approach, called MTI-IM, numerical simulations and experiments with commercial-grade radars have been performed, with comparisons between MTI-IM and other IM techniques. Results show good capabilities to fight the uncorrelated interference coming from more than one interfering radar. This is achieved at reduced computational cost, which is a key limiting factor in automotive systems.

@en