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.

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.

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.

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.

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 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.

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 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.

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 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.

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 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.

In this paper, an algorithm to generate a sparse linear antenna array for Direction of Arrival (DoA) estimation that works well in combination with Bayesian Compressive Sensing (BCS) is proposed. The proposed algorithms rely on the provided information inherent to BCS, i.e., the entropy of the recovered estimation vector, to place new sensor antenna elements in an initially empty array, so that the most additional information is gathered about the observed scene. It is shown by means of simulation and radar measurements that BCS methods for DoA estimation using sparse sensor arrays provide promising results in terms of detection probability and estimation accuracy. Furthermore, the proposed algorithms are able to generate sparse sensor arrangements which provide an improved performance when compared against randomly generated sparse arrays.

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.