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.

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 estimating the mounting angle of millimeter-wave 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 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. Results show that stable and accurate mounting angle estimates are obtained within approximately 25 seconds of driving. To the best of our 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.

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.

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.

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 effect of amplifier-related signal amplitude compression in orthogonal time-frequency space (OTFS) waveform for radar and communications systems is considered. A novel approach to OTFS waveform generation is proposed, where complementary sequences are used with the Zak transform to encode delay-Doppler symbols and form an OTFS time-domain signal with a constant envelope. The high peak-to-average power ratio (PAPR) of conventional OTFS can cause amplifier saturation, leading to spectral noise and performance degradation in both communication and radar systems due to amplitude clipping. This issue can be critical in dual-function radar and communication applications, where high power may be crucial in both use cases. The proposed waveform, namely, constant modulus OTFS (CM-OTFS), offers an alternative to standard OTFS when high-power or low-cost amplification is required. The sensing and communications performances of CM-OTFS are evaluated through numerical simulations and compared with pristine and amplifier-distorted OTFS waveforms. CM-OTFS demonstrates slightly degraded sensing performance and lower communication rate than pristine OTFS but outperforms amplifier-distorted OTFS signals. The performance of CM-OTFS is evaluated through radar and communication simulations, as well as radar measurements using the waveform-agile PARSAX radar.

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.