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.

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.

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.

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 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.

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.

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.

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.

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 challenge of processing incoherent sets of radar echoes to estimate the Doppler moments and noise standard deviation from precipitation with a fast-scanning radar is addressed. The recently proposed maximum likelihood parametric Doppler spectrum estimator (PSE) is extended to accommodate the noise standard deviation along with the Doppler moments in the parametric model of the Doppler power spectral density (PSD). Its performance in estimating the Doppler moments and the noise standard deviation is compared with another maximum likelihood approach. The proposed approach has a smaller bias in the estimation of the noise standard deviation for a wide range of spectral widths on simulated data. The algorithm is verified on experimental data from a fast-scanning weather radar.

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.

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.

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.

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.

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.

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.

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.

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.