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.

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 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.

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

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.

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 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 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.

To perform the polarimetric calibration of the Antenna Dome – a fixed multi-node over-the-air measurement setup for antenna pattern characterization – a fast simulation model is developed. The model enables characterization and compensation of angle-dependent polarimetric distortions in the transmitted field of an antenna under test (AUT). An open ended waveguide and a horn antenna at 26.5 GHz are used for referencing and validation, respectively. The total power simulated at broadside with the proposed model showed an average power difference of 0.5 dB compared with the measurement results for the reference antenna. The proposed calibration method reduces the power deviation between the full-wave simulation model and the practical Dome measurement system of about 67% in the antenna used for validation. The calibration procedure compensating for systematic power imbalances across the sensing nodes and correcting angle-dependent polarization basis distortions.

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.

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 problem of estimating instantaneous distributed target velocity using noisy measurements by multiple asynchronous automotive radar sensors is investigated. Two novel neural networks (NNs)-based approaches are proposed to address the problem. Both NNs use the point cloud with radar detections as an input. In the first approach, a hybrid NN is designed to take a set of points inside a cluster as its input, extract spatial-dynamic features to be used as weights for each input point, and apply them to obtain a weighted least square (WLS) solution for instantaneous velocity estimation. To this end, dedicated loss functions are proposed to allow the model to predict weights that can follow a velocity profile curve satisfying target constraints. Moreover, a small offset in the radial velocity value of each point is applied to adjust errors in the sensor measurements. In the second approach, a deep NN is proposed that takes a set of points inside a cluster as its input and directly outputs velocity estimates. Both approaches have been verified experimentally using the large open-source automotive RadarScenes dataset. The results show a significant improvement in terms of mean absolute error in velocity estimation over the state-of-the-art alternative techniques. Moreover, the estimated velocity is used as an additional measurement value inside a target tracker. Results show that this can increase the performance of the tracker, especially during challenging scenarios such as abrupt changes in the velocity of the target.

The problem of diminished unambiguous target velocity interval induced by the time-division-multiplex mode (TDM) of multiple-input-multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) automotive radar has been explored. A novel MIMO antenna array activation mode and a parametric approach for Doppler de-aliasing based on a two-step cross-entropy optimization are proposed. The TDM Doppler signal model has been derived, and a novel two-step cost function is proposed to achieve robust and efficient estimation. In contrast to the state-of-the-art method, the method proposed does not need multiple overlapped antennas and can resolve multiple targets in the same range and Doppler bins. The proposed method has been verified with numerical simulations with different parameter settings, as well as experimental data from a radar target simulator.