Conventional radar segmentation research has typically focused on learning category labels for different moving objects. Although fundamental differences between radar and optical sensors lead to differences in the reliability of predicting accurate and consistent category labels, a review of common radar perception tasks in automotive applications reveals that determining whether an object is moving or static is a prerequisite for most tasks. To fill this gap, this study proposes a neural network (NN)-based solution that can simultaneously segment static and moving objects from radar point clouds. Furthermore, since the measured radial velocity of static objects is correlated with the motion of the radar, this approach can also estimate the instantaneous 2-D velocity of the moving platform/vehicle (ego-motion). Notably, despite performing dual tasks, the proposed method employs very simple yet effective building blocks for feature extraction: multilayer perceptrons (MLPs) and recurrent NNs (RNNs). In addition to being the first of its kind in the literature, the proposed method also demonstrates the feasibility of extracting the information required for the dual tasks directly from unprocessed point clouds, without the need for cloud aggregation, Doppler compensation, motion compensation, or any other intermediate signal processing steps. To measure its performance, this study introduces a set of novel evaluation metrics and tests the proposed method using a challenging real-world radar dataset, RadarScenes. The results show that the proposed method not only performs well on the dual tasks but also has broad application potential in other radar perception tasks. More qualitative results can be viewed here: https://youtu.be/3ejS1chSvQ8?si=uGRugVA63BCyvNBV

The problem of contactless accurate monitoring of vital signs of patients in psychiatric settings is addressed in this work. Most state-of-the-art radar-based approaches rely on measurements acquired at short distances with the sensor positioned directly in front of the subject’s chest, typically within 1 m, to ensure a strong line-of-sight signal. In contrast, we investigate a substantially more challenging and clinically safer configuration, where multiple frequency-modulated continuous wave (FMCW) radars are mounted at ceiling height in a tilted position, remaining completely out of reach and outside the direct line of sight of patients. An experimental dataset involving 30 participants performing seven activities was collected using two of these ceiling-mounted radars and one reference radar mounted at hip height. A new processing pipeline is proposed with automatic range bin selection and fast autoregressive (AR) spectral estimation, including an approach that leverages all chirps per frame to improve performances with short observation window lengths. Despite the unfavorable sensing geometry and increased radar-to-subject distance, the results demonstrate that ceiling-mounted radars achieve mean absolute respiration rate errors comparable to the hip height reference radar. The best configuration yields a mean absolute error (MAE) of 2.18 respirations per minute (rpm) for participants in a sitting position at distances exceeding 4 m from the ceiling-mounted radars. Moreover, the proposed AR-based method significantly improves estimation accuracy for short windows of 3.1 s, achieving a MAE below 4.3 rpm for all radars and participant positions.

The problem of detecting and resolving UAV swarms using radar systems is considered in this paper. Conventional FMCW radars operating with fixed waveform configurations are limited by the trade-off between range resolution and spatial coverage within the maximum unambiguous range. To address this, an adaptive bandwidth selection approach based on Proximal Policy Optimization (PPO) is proposed within the cognitive radar framework. The radar adjusts its transmitted bandwidth on a per-CPI basis using closed-loop feedback to improve swarm detectability. The approach is validated using a dedicated FMCW radar simulator with realistic target motion and detection modeling. Results show that the learned policy via PPO consistently outperforms fixed-bandwidth baselines and approaches optimal-level performance (i.e., that achievable by access to ground-truth information) across multiple swarm scenarios.

Sparse antenna arrays reduce hardware cost but introduce sidelobe-related challenges for direction-of-arrival (DoA) estimation. Conventional array-design metrics such as sidelobe level (SLL) and half-power beamwidth do not directly predict how well a given geometry supports target detection, false-alarm control, or angular accuracy. This paper bridges that gap with a receiver-operating-characteristic (ROC)-based framework that quantifies probability of detection (P _D), false alarm rate (FAR), and angular root-mean-square error (RMSE) for equi-amplitude spatially tapered arrays (STAs). Three STA families are synthesized via convex optimization to isolate the effects of aperture scaling, inter-element spacing, and sidelobe-shaping objective, and each is benchmarked against aperture-matched uniform linear arrays through single-snapshot Monte Carlo simulations with two unequal-power sources spanning a 30dB dynamic range. A two-dimensional analysis maps P _D and RMSE jointly over angular separation and inter-source SNR difference, explicitly characterizing weak-target detectability beside a strong interferer. Three estimators - Bartlett beamforming, Orthogonal Matching Pursuit, and FOCUSS - are compared to reveal how pattern-limited and sparse-recovery methods exploit different array geometries. The results show that peak SLL reduction improves detection primarily in the low-FAR regime, yielding up to 0.2 gain in P _D per 10dB of SLL suppression at FAR = 10 ^-3 , while sparse estimators remain robust to irregular layouts. The proposed framework provides a direct, system-level link between STA synthesis parameters and practical DoA performance for radar and joint radar-communications systems.

To characterize atmospheric turbulence, the Doppler moments are estimated by weather radars. However, moment accuracy is highly sensitive to radar transmission parameters such as pulse repetition time (T_s) and number of pulses (N_p), which affect Doppler ambiguity and estimation variance. Traditional fixed-parameter radars face trade-offs between aliasing and measurement precision. This paper proposes an adaptive radar framework that dynamically adjusts T_s and N_p on a per-scan basis to improve Doppler moment estimation at a single resolution cell level. Inspired by the Fully Adaptive Radar (FAR) concept, the method also includes a novel multi-lag Doppler width estimation scheme. Results demonstrate enhanced estimation accuracy, enabling better responsiveness to localized and non-stationary weather conditions.

The problem of estimating the mounting angle of millimeter-wave (mmWave) automotive radars installed on moving vehicles is investigated. We address this angle estimation problem during normal driving, without relying on controlled environments, dedicated radar targets, or specially designed driving routes. To achieve this, we propose a signal processing pipeline that combines radar and inertial measurement unit (IMU) data to enable accurate and reliable estimation under realistic driving conditions. Unlike previous studies, the method employs neural networks (NNs) to process sparse and noisy radar measurements, reject detections from moving objects, and estimate radar motion. In addition, a measurement model is introduced to correct IMU bias and scale factor errors. Using vehicle kinematics, the radar mounting angle is then computed from the estimated radar motion and the vehicle's yaw rate. To benchmark performance, the proposed approach is comprehensively compared with two alternative problem formulations and four estimation techniques reported in the literature. Validation is carried out on the challenging RadarScenes dataset, covering over 79 km of real-world driving with different velocities and trajectories. The results show that stable and accurate mounting angle estimates are obtained within approximately 25 s of driving. To the best of the authors' knowledge, this is the first study to demonstrate that automotive radar mounting angles can be estimated during complex, real traffic conditions using only onboard sensor data.

The problem of interference in frequency modulated continuous wave (FMCW) radars is considered. The application of modulo analog-to-digital converters (ADCs) to sample interfered intermediate-frequency (IF) signals is proposed to preserve both target returns and interference signals with high-amplitude resolution without signal saturation. Both the high dynamic range (HDR) self-interference (SI) and the desired IF signal segments are recovered from modulo samples with low quantization. To integrate modulo sampling with interference mitigation, a new interference detection method leveraging the spectral spread property of the modulo nonlinearity is proposed. The different recovered segments of the IF signals are then aligned in the temporal domain. The identified mutual interference (MI) can be interpolated from the recovered IF signal segments. The effectiveness of the proposed method in handling MI and preserving high-resolution HDR samples of both targets and SI is demonstrated in numerical experiments for different automotive radar scenarios.

Calibration of mm-Wave fully-polarimetric MIMO radar is considered. An enhanced polarimetric phase calibration method is proposed, which operates in the far-field region of the radar but utilizes calibration targets within their nearfield scattering region. The method exploits near-field dihedral scattering with yaw angle correction to improve calibration repeatability across different measurement sessions. Simulations and measurements show that using a larger dihedral compared to the smaller one employed for far-field calibration in the near-field region increases received power by up to 15.7 dB, thereby enhancing the signal-to-noise ratio. Although near-field sensitivity to alignment introduces phase variations, the proposed yaw angle correction effectively compensates them, achieving phase consistency comparable to far-field calibration under high-SNR conditions, and outperforming it under low-SNR conditions. The method provides a robust approach for stable polarimetric calibration in compact automotive radar setups.

Accurate Doppler moment estimation in weather radars is challenging when multiple scatterer populations create multi-modal velocity spectra. In this paper, an adaptive framework that combines an Expectation Maximization (EM) algorithm with folded-Gaussian mixture components to handle velocity aliasing is presented. An adaptive transmission policy adjusts pulse repetition time and coherent pulse count to improve Doppler resolution while avoiding ambiguities. Monte Carlo tests across drift regimes show adaptive selection clearly lowers estimation error when compared to fixed settings, and effectively reduces aliasing artifacts, enabling more robust multi-modal Doppler estimation.

Distributed radars offer the potential to overcome the limited field of view and ambiguities of single radar systems. However, effectively fusing data from multiple radar nodes remains challenging, particularly for reliable 3-D signature reconstruction and classification of human activities under diverse motion conditions. To address these challenges, we propose a distributed fusion method for 3-D signature reconstruction and human activity recognition (HAR) using distributed 60-GHz multiple-input multiple-output (MIMO) radar nodes. The proposed approach incorporates an adaptive radar signal processing pipeline designed to tackle both static and dynamic movements. At each radar node, Capon beamforming is applied to generate radar cubes in range, azimuth, and elevation, which are then fused to reconstruct 3-D intensity maps and voxelwise velocity vectors of the observed extended human target. The reconstructed signatures, consisting of 3-D intensity and Doppler vectors, are then used as inputs to deep learning networks for HAR. A hybrid CNN-bidirectional long short-term memory (BiLSTM) model is developed to capture both spatial and temporal features and realize the classification of activities in a sequence-by-sequence fashion. The method was evaluated with a dataset collected using a network of three 60-GHz radars and including seven activities performed by 30 participants. The results show that both static postures and dynamic activities can be captured effectively by the reconstructed 3-D signatures. The proposed method can also handle the diversity of movements and achieves an average macro-F1 score of 90.9% for a four-class task under a leave-one-person-out (L1PO) evaluation, and 92.0% under subjectwise cross-validation for the same four-class task. Furthermore, ablation studies show that comparable HAR performance is achieved using only two out of the three radars, provided that sufficient spatial diversity is ensured. This suggests that the viewing and deployment geometry of the radar nodes is more critical than their number in absolute terms.

This article introduces a compact thermal model (CTM) for antenna-level analysis of the printed circuit board (PCB) structures that enables fast and accurate prediction of the chip temperature (T _chip) under varying structural and environmental conditions. The CTM represents conduction within the antenna system and convection and radiation to the environment through an equivalent lumped-element network, providing a computationally efficient alternative to full thermal simulations. A principal contribution is the definition of the antenna cooling factor (ACF), a new parameter that quantifies cooling capability of the antenna and its influence on the resulting T _chip. The ACF offers designers a direct means to assess thermal behavior during the antenna design process, thereby supporting integrated electrothermal codesign of antenna systems. Using the proposed model, designers can simultaneously assess electromagnetic (EM) characteristics (e.g., bandwidth (BW) and maximum realized gain), structural thermal constraints (e.g., chip or antenna temperature), and joint parameters such as antenna noise temperature. The proposed CTM and ACF framework is validated experimentally using a dedicated thermal measurement setup. Nine antenna prototypes resonating at 3.5 GHz with different dielectric materials, element sizes, and antenna types are fabricated, connectorized, and characterized through combined EM and thermal measurements. The strong agreement between measurements and model predictions confirms the accuracy and practical relevance of the approach. The results establish a systematic methodology for incorporating thermal performance into antenna design with minimal computational load.

The feasibility of using hybrid additive manufacturing (AM) to produce low-cost, phased array antennas for SatCom applications is investigated in this work. A 4x2 planar array comprised of dielectric resonator antennas (DRAs) operating in the fundamental mode within the K-band SatCom downlink band (17.7-21.2 GHz) has been designed, fabricated and measured. The impact of print settings on the material properties is assessed and incorporated into the antenna design process. Manufactured prototypes of the single element and the planar array geometry are experimentally verified in terms of their scattering parame-ters and far-field radiation patterns. The potential of combining hybrid AM and DRA technologies for future mmWave phased array developments is highlighted by the presented results.

A novel over-the-air (OTA) measurement setup for simultaneous and near-real-time characterization and mapping of the phased array radiation patterns to the error-vector-magnitude (EVM) is introduced. A cost-effective software-defined radio (SDR) system, including a Pluto SDR for both transmission and reception, is employed. For setup demonstration, the performance of a 4 × 4 active phased array antenna (APAA) is studied at 26.5 GHz. Calibration of the transmitter results in an EVM of 0.5%, proving the accuracy of the measurement system. The over-the-air measured EVM of the APAA is 1.2%. The proposed setup and approach provide deep insights into how array radiation characteristics affect signal quality, advancing the evaluation and development of APAAs for next-generation wireless communication systems.

A novel receiver front-end simulation model is proposed to perform a systematic analysis of signal- and noise levels, SNR degradation, system IP1dB compression point and power consumption. The model is applied to a fully digital receiver in a K-band SatCom use-case. Using this simulation model, key design trade-offs in component properties, multiplebeam forming architectures and power consumption are jointly identified and visualized.