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.

A novel concept to mitigate the unintended transmission and reception of signals at the third harmonic of dielectric rod antennas is proposed. To suppress the third-harmonic radiation, an additive-manufactured photonic bandgap material is used in the dielectric rod antenna. The antenna has been designed to operate at the frequency of 5 GHz and exhibit a bandgap at the third-harmonic frequency of 15 GHz. The design of the material is explained via the band diagram of the bandgap material and imperfections introduced due to the additive manufacturing (AM) process considered. Numerical simulations of dielectric rod antenna prototypes fed via a rectangular waveguide (RWG) with and without harmonic suppression are carried out to confirm the operational principle. The design proposed is verified experimentally. The manufactured antenna is characterized in terms of its input reflection coefficient and far-field radiation properties. The obtained experimental results agree well with predictions obtained through simulations and confirm the third-harmonic suppression capability of the antenna.

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.

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.

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.

Classification of human activities performed sequentially and with unconstrained durations using radar sensors has been studied in this work. A novel processing pipeline comprising a sequence segmentation stage, a segment processing stage, and a classification stage has been proposed to address this challenge. Specifically, the segmentation stage has been implemented by monitoring Rényi entropy for fluctuations in the radar data, with the entropy, derived from micro-Doppler spectrograms, functioning as a descriptive quantity of the activity being performed. The method has been experimentally verified on a challenging, publicly available dataset collected with a network of five simultaneously operating pulsed ultrawideband radars. Classification performance has been compared to reference works in the literature on the same dataset, and a test accuracy and macro F1-score of 89.3% and 82.0% have been, respectively, demonstrated.

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.

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.

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 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.

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.

Multi-path propagation over asphalt road surfaces at automotive radar frequencies is studied. A new expression for the reflection coefficient from asphalt which takes into account asphalt surface roughness is proposed. The classic two-ray method to take into account the multi-path is improved using antenna radiation patterns and the antenna tilt. A new method to estimate elevation of a reflector above road surface based on reflectivity variations is proposed.

This article focuses on the experimental validation of probing signals designed to enable radar operation in spectrally crowded environments using an S-band software defined radar (SDR). The tested waveforms ensure spectral coexistence between the sensing system and frequency-overlaid emitters, while optimizing radar performance. This is achieved through a bespoke notching of the radar signal spectrum to control the amount of interference injected by the radar in each shared frequency interval. In addition, some relevant features of the probing signal that influence radar performance are controlled via a similarity index, describing the maximum allowable distance between the spectrally notched waveform and a prescribed radar signal. In a first stage, the study is aimed at verifying whether the transmit and receive chains of the SDR system impair the spectral and temporal features of the designed waveforms. Subsequently, the tested signals are radiated into the environment to investigate their effectiveness to detect targets in the illuminated scene, as well as to ensure spectral coexistence in the presence of frequency-overlaid emitters. The results demonstrated that by exploiting the designed radar probing signals, the SDR system is capable of sharing spectrum with other radio frequency wireless systems while also allowing to detect both stationary and moving targets.

The challenge of avoiding aliasing in the Doppler spectrum for precipitation is addressed. A novel integrative signal processing approach has been proposed to address the research gaps from various disciplines. The proposed approach consists of several steps. First, an aperiodic way of sampling the echoes (aperiodic sampling refers to aperiodic pulse train in the context of radar echoes in slow time) has been proposed by which the maximum unambiguous Doppler frequency (velocity) is enhanced. Second, the Doppler spectrum moment estimation is performed with the help of a parametric form of its covariance. The performance of the moment estimation is assessed by the bias and the variance in the estimated counterparts. The theoretical variance for the parameter estimation is also derived. An aperiodic pulse train design recommendation has been proposed for adequately and unambiguously estimating the Doppler moments for one extended target (like precipitation). Finally, a spectrum reconstruction technique is implemented after the moment estimation on simulated radar echo samples for a realistic precipitation-like event. The comparison with the other approaches proves its superiority for parameter estimation and Doppler spectrum reconstruction.

In this paper, the full calibration chain of FMCW radar with simultaneous transmission of two orthogonally polarized orthogonal waveforms is considered. Specifically for this type of polarimetric radar, compensation of signals’ biases and equalization of the amplification gains of the parallel polarimetric channels in the receiver are jointly performed using the noise measurements. The calibrations of the absolute complex gains of the transmitter’s polarimetric channels together with complex antenna gains are done using the model-based fit of the measurements of the rotating dihedral reflector. Phase relations between polarimetric channels are treated in the Doppler domain using the unfolded velocity of the target. The performed calibration results in high-accurate measurements of the radar targets’ polarimetric scattering matrix (PSM) in the Doppler domain. All the proposed calibration steps are illustrated using real radar data.

The theory of radiation from an open-ended circular crosssection waveguide is extended by including the excitation of all possible higher-order waveguide modes at the waveguide-to- free-space boundary. The theoretical expressions are formulated using spectral-domain techniques. The validation is performed using the existing commercial tool FEKO with the Method of Moments (MoM) solver for a cylindrical waveguide with the fundamental mode excitation (TE11) and the corresponding higher-order mode, which has the same azimuthal variation as of the fundamental mode, which is (TE12).

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.

Additive manufacturing (AM) is increasingly recognized as an enabling technology for novel devices with increased functionality and bandwidth for microwave applications. However, due to the layer-by-layer build approach employed by most AM techniques, internal patterns are introduced to printed materials, which cause an effective anisotropy in their material parameters. An electrostatic model inspired by parallel plate capacitors, describing the dielectric anisotropy of materials manufactured with fused filament fabrication (FFF) AM techniques is proposed in this work. The accuracy of the model's predictions about the permittivity tensor of printed materials is investigated via numerical simulations. Furthermore, permittivity tensor measurements are carried out for samples printed with various materials and print settings. Measurement data is fitted to the model via a least squares approach, and excellent agreement is observed. Novel conclusions about the impact of material and print parameters on the effective permittivity of additively manufactured materials, improving the accuracy in modeling and designing additively manufactured microwave devices are drawn.

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.