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.

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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.

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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.

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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.

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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.

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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.

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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.@en

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.

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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.@en

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.

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The problem of the limited accuracy of precipitation Doppler spectrum moments estimation measured by fast azimuthally scanning weather radars is addressed. A novel approach for the Doppler moment estimation based on maximum likelihood estimation is proposed. A simplified semianalytical parametric model for the precipitation power spectral density (PSD) as a function of the velocity parameters of the scatterers and the finite radar observation time is derived for typical precipitation-like weather conditions. An inverse problem for estimating the Doppler moments from measurements of the PSD is formulated and solved. It is demonstrated that the variance of the estimation of the Doppler moments approaches the Cramer Rao Lower Bound (CRB) when the observation time approaches infinity. The performance of the proposed approach is compared with some classical techniques and another realization of the maximum likelihood approach based on simulated and experimental data. The results indicate the superiority of the proposed approach, especially for short observation time. Furthermore, a scanning strategy to accurately estimate the Doppler moments based on the true velocity dispersion of the scatterers is provided with the help of the proposed approach.@en

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. @en

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.@en

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.

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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. @en

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.

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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.@en

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.

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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). @en

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.@en