An overview of polarimetric sensing and its growing application in automotive radar systems is presented. While polarimetric techniques are extensively used in fields like weather monitoring and target imaging, their integration into automotive radar presents unique challenges, particularly in calibration and measurement accuracy across wide scanning angles. This paper reviews key polarimetric principles and their use in different applications, with a focus on current automotive radar implementations, and the calibration challenges posed by off-broadside measurements. Future research directions for improving polarimetric accuracy in dynamic automotive environments are also discussed.

An antenna array with dual-functionality - electromagnetic radiation and thermal cooling - is proposed. An iterative array design procedure is developed to improve cooling, mutual coupling, side lobe levels, and gain levels in dual-functional antenna arrays with adaptive beam steering. Heatsink-attached patch elements are combined with complementary split ring resonator (CSRR) structures in between the elements, resulting in a novel modular heatsink antenna array. Based on the proposed design, the beam scanning performances of four-element and eight-element linear arrays at 26 GHz are studied. A conventional shorted patch antenna array is used for benchmarking. Through thermal and electromagnetic simulations, it is demonstrated that the proposed antenna array decreases the maximal array temperature by more than 40°C as compared to the benchmark. Moreover, the new design resolves the pattern performance degradation problems in heatsink arrays, while approaching to the electromagnetic performance of the benchmarked array.

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.

This article offers a wide perspective of the research topic of orthogonal time–frequency space (OTFS)-based integrated radar–communication systems. OTFS modulation is a recently proposed evolution of orthogonal frequency division multiplexing that promises better Doppler tolerance and therefore is a firm candidate for future communications in high-mobility scenarios. As radar sensing is often present in these scenarios (urban mobility, drones), OTFS is also a firm candidate for multifunctional systems capable of communications and radar sensing. In this article, we present the state of the art of OTFS for integrated sensing and communications (ISAC), focusing on radar receiver and multiple-input multiple-output (MIMO) waveform design, and from this overview, we identify a set of open research challenges that are used to identify opportunities for research in the topic. It is shown that OTFS-based ISAC is an emerging topic with promising first results and plenty of room for growth.

Both thermal and electromagnetic performance of substrate-integrated waveguide (SIW) and microstrip line-fed shaped-beam arrays with slot and patch radiating elements are conducted. Three array types operating at 26 GHz band, namely SIW slot array, SIW array with patches, and proximity coupled patch array, are considered. The array performances regarding shaped radiation pattern stability with frequency and maximal temperature at the power amplifier chips are discussed. The study highlights intriguing trade-offs between radiation pattern performance and cooling ability in phased arrays.

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.

The problem of detection and localization of multiple people using a network of Ultra-Wide Band (UWB) radar nodes in the cabin of a vehicle is addressed in this paper. Specifically, an algorithm for decentralized vital signs detection is proposed, based on the analysis of a novel model for radar signatures of vital signs. Additionally, a centralized association processing block is used to fuse the detections from all radars in the network using a dedicated cost-matrix computation. The performance of the proposed processing pipeline is tested experimentally with a multistatic radar network, and a simulation framework is also developed to evaluate the performance in alternative topologies of the radar network beyond the experiments. It is shown that the detection and localization of humans within the car environment is possible with the proposed processing pipeline, with localization Root Mean Square Error.

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.

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.

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.

Automotive radar interference problem between multiple radar sensors is investigated. Phase-coded frequency modulated continuous wave (PC-FMCW) radar structure with low sampling and processing power demands is introduced to blindly mitigate mutual interference. The interference resiliency of the proposed structure is evaluated and compared with the conventional frequency modulated continuous wave (FMCW) automotive radar. It is demonstrated that the proposed approach is more robust to both coherent and non-coherent interference types, allowing resilience to both external interference of radars but also the self-interference in the simultaneous multiple input multiple output (MIMO) transmission.

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.

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.

The sunflower array topology concept is introduced, for the first time, to the constrained infinitesimal dipole modeling (IDM) technique to increase the computational efficiency and reduce the modeling errors. The concept is applied to embedded element pattern predictions via matrix inversion. A novel study on the impact of the type and orientation of the dipoles on the IDM performance in pattern mean square error (MSE) and stability against noise (linked to the matrix condition number) is conducted. A 5 by 5 patch antenna array modeled with 81 dipoles is used for demonstration. It is shown that using magnetic dipoles (oriented in the direction of a radiating edge of the patch) in IDM yields the optimal performance. Besides, the sunflower topology significantly lowers the MSE (by 5 dB, on average), while reducing the condition number by a factor of 10.

The problem of aliasing in precipitation Doppler spectrum with uniform pulse repetition time is addressed. This work focuses on de-aliasing such Doppler spectra using non-uniform sampling techniques, namely, Log-periodic and Periodic non-uniform sampling. These techniques reduce the ambiguous main lobes caused by aliasing (by going beyond the observable frequency limit) into ambiguous sidelobes that are distinguishable from the original spectra. The SNR is further enhanced by using an Iterative Adaptive Approach (IAA) algorithm. The performance of Doppler moment estimation is presented after applying the IAA algorithm on simulated precipitation-like radar echoes. However, the ambiguous sidelobe suppression is highly dependent on the spectral width of the Doppler spectra.

The problem of 3D ego-motion velocity estimation using multichannel Frequency Modulated Continuous Wave (FM CW) radar sensors has been studied. Special attention is given to presence of moving targets in the scene. These targets are first distinguished by the difference between the measured Doppler, and the Doppler calculated with an initial rough estimation of the vehicle ego-velocity. Then, an iterative algorithm is proposed to reduce the influence of the moving targets in the ego-motion estimation procedure, thus improving the overall accuracy. The performance of the proposed algorithm is compared with state-of-the-art alternatives based on simulated data, and superior performance has been demonstrated.

The problem of precipitation detection using Frequency Modulated Continuous Wave (FMCW) radar under strong sectorial interference is addressed. The effect of such strong interferences in the case of an FMCW scanning radar is presented. Three signal-processing pipelines (two reflectivity-based and one Doppler-based) are proposed. The performances of all these pipelines are analyzed and compared. The morphology-based pipeline performs better for higher signal-to-noise ratios (> −15dB), whereas the entropy-based pipeline performs better in the case of lower SNRs (< −15dB). On the other hand, the circular variance-based masking technique is computationally very efficient. The proposed techniques are applied to simulated and real X-band fast-scanning radar data.

A novel dual-linear polarized active aperiodic millimeter-wave (26 GHz band) phased array is prototyped. Four-channel integrated circuits are used for beamforming. An asymmetrical signal distribution and antenna feeding network is designed. A heuristic post-calibration method is applied in beam steering. The measurements show that, as compared to the benchmark array with a conventional square-grid layout, the proposed aperiodic topology can significantly decrease the peak sidelobe level, the extent of which depends on the polarization and scan angle. This comes at the expense of larger array size, higher feed network losses and increased cross-polarization levels.

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.

The challenge of reconstructing the Doppler spectrum of a precipitation-like event observed by a fast-scanning weather radar is addressed. A novel method is proposed where the echo sequence in time is assumed to be a complex Gaussian process with a known covariance structure. It is a two-step approach where the first step is the estimation of the hyperparameters of the covariance function with a maximum likelihood approach, and the second step is the reconstruction of the spectrum directly in the time or spectral domain. The proposed approach is applied to simulated data for hyper-parameter estimation performance analysis and real radar data for the complete Doppler spectrum reconstruction.