The role of radar for building situation awareness in (semi)autonomous vehicles is severely restricted by its low angular resolution. The physical size of the radar, which determines its antenna aperture size and thus the radar angular resolution, is often a subject of stringent limitations to physically fit the system in the vehicles. Multiple input multiple output systems are used to increase the achievable angular resolution, and these are often combined in the literature with algorithms inspired by synthetic aperture radar techniques that exploit the velocity of the vehicle itself for finer resolution. Some of the most common approaches are reviewed, in this context, with a specific focus on challenges for the implementation of data collected in real driving scenarios. Key experimental results using representative algorithms and driving data collected in the city of Delft, The Netherlands, are presented and 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.

The throughput performance of intelligently shaped and fixed analog elevation beam patterns in millimeter-wave (mm-wave) base stations with hybrid beamforming (HBF) is assessed for the first time. Distinct spatially heterogeneous user distributions (i.e., uniform, near-site, cell-edge, and weighted uniform and near-site) and propagation environments (i.e., line-of-sight (LoS) with multipath and non-line-of-sight (NLoS) with multipath) are considered. The cosecant-squared and flat-top shaped beam patterns are compared to the benchmark pencil beam pattern with a straightforward electrical downtilt. The LoS simulation results show that in case of unknown weight of user distribution scenarios, the cosecant-squared pattern is the most robust, with a gain of up to 16% in the average system throughput and up to 34% in the 90th percentile user throughout compared to the benchmark. If the near-site case has a greater probability of occurrence than the uniform user distribution (e.g., due to daily events and festivals), the flat-top pattern becomes preferable. In the NLoS scenario, the considered HBF architectures with elevation beam pattern shaping do not bring any performance disadvantages compared to the benchmark HBF.

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.

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.

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 paper presents an approach based on Doppler beam sharpening (DBS) to enhance the resolution of multiple ‘dynamic’ targets in automotive driving scenarios. The ambiguity inherent to the forward-looking DBS and the coupling between azimuth and elevation angles are jointly addressed with the proposed method. Demonstrated experimental results show the feasibility of the proposed technique in automotive radar sensing.

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.

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.

A far-field calibration method based on broadside measurements with a co-polarized and a cross-polarized reference dihedral target is demonstrated for the first time for an asymmetric ±45o linearly polarized 77 GHz frequency modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) automotive radar. A novel extension of the calibration technique for off-broadside targets' phase response is proposed by using an optimized progressive phase compensation. Rotated dihedral and trihedral targets are used for validation. On broadside, the standard deviation of the channels' phase difference is nearly zero degrees for reference targets and under 5o for validation targets. The calibration for off-broadside targets shows that the phase relation in the target's scattering matrix can be retrieved, the accuracy of which is dependent on the target's type and angular position.

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 novel ensemble prediction technique is introduced to enhance the accuracy of far-field embedded element pattern (EEP) prediction under mutual coupling (MC) effects, while relaxing the training data size challenge in neural network (NN)-based algorithms. The proposed method integrates a two-stage NN for direct EEP prediction from full-wave simulated pattern data in spherical coordinates with a fully connected NN for the prediction of excitation coefficients of an array of infinitesimal dipoles, approximating the full-wave simulated EEPs via constrained infinitesimal dipole modeling (IDM). Quasi-randomly distributed five-element pin-fed S-band patch antenna arrays are used for demonstration purpose. It is shown that, for a large-sized (3500 topologies) and relatively small-sized (1500 topologies) dataset, incorporating IDM-NN with the benchmarked direct EEP-NN in an ensemble technique increases the pattern prediction accuracy by 11% and 60% on average, respectively.

The utilization of low-cost hybrid additive manufacturing equipment for creating a fully integrated patch antenna system is demonstrated. A miniaturized stacked dual-band patch antenna for GNSS applications with integrated feed, amplifier, matching, and bias network is considered. Design and manufacturing challenges due to the peculiarities of hybrid additive manufacturing, like anisotropic substrates, thermal curing steps, and poor layer adhesion between different materials, are discussed, and solutions are proposed. In this work, the passive part of the antenna is manufactured, and its measured performance is compared to results obtained via numerical simulations. An excellent agreement between simulation and measurement of the passive antenna is observed. The results of the fully integrated and active antenna will be demonstrated at the conference.

The problem of human activity classification using a distributed network of radar sensors has been considered. A novel sensor fusion method has been proposed that processes data from a network of radar sensors and yields 3-D representations of both reflection intensity and velocity distribution. The formulated method has been verified in an experimental case study, where activity classification was performed using data collected with 14 participants moving in diverse, unconstrained trajectories and executing nine activities. The classification performance of the proposed method has been compared to alternative fusion methods on the same dataset, and a test accuracy and macro $F1$ -score of, respectively, 87.4% and 81.9% have been demonstrated. A feasibility study has also been performed to demonstrate the ability of the proposed method to generate 3-D distributions of intensity and target velocity.

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.

An innovative reconfigurable beamforming network concept for multiple high-gain agile and mutually zero-forced beam generation is proposed. Four-port couplers and variable phase shifters are used to synthesize the network. A 2 beam by 7 element beam former is designed and fabricated as proof of concept. A static scenario with the two beams separated by the approximated array beamwidth is considered in the prototype. At the operating frequency of 9 GHz, based on the array factor using the measured scattering parameters, it is demonstrated that the null levels stay below -37 dB, while the beam weights remain close to uniform for the best efficiency.

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.

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.

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.

In this study, the problem of multipath in radar sensor networks for human activity recognition (HAR) has been examined. Traditionally considered as a source of additional clutter, the multipath is being investigated for its potential to be exploited through the creation of virtual radar nodes. These virtual nodes are conceptualized to observe targets from aspect angles that differ from those of physically existing radars. To realize this idea, an innovative processing pipeline is proposed that extracts information from multipath signals to improve HAR. The pipeline isolates and tracks the line-of-sight (LOS) and multipath components of a moving human target performing continuous sequences of activities observed by a network of three radar sensors. Furthermore, the method has been verified with experimental data consisting of six activities and 14 volunteers by comparing classification metrics with the use of a single radar as well as only the LOS components of the three radars in the network. A 12-layer convolutional neural network (CNN) classifier has been designed to operate on range-Doppler (RD) images derived from the LOS and multipath components, extracted by the proposed method. A substantial performance improvement using the leave-one-person-out (L1Po) test set is demonstrated in the order of +11% by exploiting a multiradar network with its LOS and multipath components.