In the automotive sector, both radars and wireless communication are susceptible to interference. However, combining the radar and communication systems, i.e., radio frequency (RF) communications and sensing convergence, has the potential to mitigate interference in both systems. This article analyses the mutual interference of spectrally coexistent frequency modulated continuous wave (FMCW) radar and communication systems in terms of occurrence probability and impact, and introduces RadChat, a distributed networking protocol for mitigation of interference among FMCW based automotive radars, including self-interference, using radar and communication cooperation. The results show that RadChat can significantly reduce radar mutual interference in single-hop vehicular networks in less than 80 ms.

Microwave radar technology is very attractive for ubiquitous short-range health monitoring due to its non-contact, see-through, privacy-preserving and safe features compared to the competing remote technologies such as optics. The possibility of radar-based approaches for breathing and cardiac sensing was demonstrated a few decades ago. However, investigation regarding the robustness of radar-based vital-sign monitoring (VSM) is not available in the current radar literature. In this paper, we aim to close this gap by presenting an extensive experimental study of vital-sign radar approach. We consider diversity in test subjects, fitness levels, poses/postures, and, more importantly, random body movement (RBM) in the study. We discuss some new insights that lead to robust radar heart-rate (HR) measurements. A novel active motion cancellation signal-processing technique is introduced, exploiting dual ultra-wideband (UWB) radar system for motion-tolerant HR measurements. Additionally, we propose a spectral pruning routine to enhance HR estimation performance. We validate the proposed method theoretically and experimentally. Totally, we record and analyze about 3500 seconds of radar measurements from multiple human subjects.

Practical, in-band, full-duplex (IBFD) systems typically require more than 100 dB of self-interference cancellation (SIC). Digital processing alone is insufficient for achieving this target, which drives us towards supplementary analog mitigation techniques. We propose an analog-domain, self-interference cancellation circuit to enable pass-band, analog SIC in an IBFD system. Analog SIC is limited by several hardware constraints and design choices, including finite tap-delay resolution, non-negative tap constraints, and bit precision quantization. We characterize the performance impact of each of these limitations as a function of signal bandwidth, carrier frequency, bit precision, and other system design parameters. We further characterize the achievable system performance under all of these limitations combined. We simulate several realistic examples to illustrate the relationship between the achievable self-mitigation performance and various system design choices. We implement a simple constrained optimization algorithm informed by these results to optimize the tap-delay weights of the analog circuit under these system constraints. We simulate the achievable mitigation performance and demonstrate as much as 45 dB of analog-domain, self-interference mitigation of a wide-band signal with realistic system configurations.

The purpose of this study was to classify, and model various physical activities performed by a diverse group of participants in a supervised lab-based protocol and utilize the model to identify physical activity in a free-living setting. Wrist-worn accelerometer data were collected from ( $N=152$ ) adult participants; age 18–64 years, and processed the data to identify and model unique physical activities performed by the participants in controlled settings. The Gaussian mixture model (GMM) and the hidden Markov model (HMM) algorithms were used to model the physical activities with time and frequency-based accelerometer features. An overall model accuracy of 92.7% and 94.7% were achieved to classify 24 physical activities using GMM and HMM, respectively. The most accurate model was then used to identify physical activities performed by 20 participants, each recorded for two free-living sessions of approximately six hours each. The free-living activity intensities were estimated with 80% accuracy and showed the dominance of stationary and light intensity activities in 36 out of 40 recorded sessions. This work proposes a novel activity recognition process to identify unsupervised free-living activities using lab-based classification models. In summary, this study contributes to the use of wearable sensors to identify physical activities and estimate energy expenditure in free-living settings.

Novel use of radar acoustics for noncontact cardiac parameter estimation is presented as a potentially invaluable addition to wireless sensor networks in Healthcare IoT. Here, radar acoustics refers to the radar captured high-frequency mechanical motion, beyond 20 Hz, on the skin surface induced by heart sound (HS) acoustic waves. Conventional radar-based heart rate (HR) detection methods rely on detecting the heartbeat motion, around 1 Hz, while the unaddressed respiration motion coupling issue prevents consistent heartbeat estimation accuracy in the presence of stronger respiration interference, below 1 Hz. Another issue common to prior methods is the nonadaptive spectral filter design for separating heartbeat signal. As a consequence, these methods cannot be generalized to measurement spaces with high heartbeat variability as demonstrated in this study. To effectively address these limitations, a new HR detection method using radar is proposed to derive a high-fidelity pulse template from the smoothed envelogram of HS measurements. What is more, the feasibility study of radar HS-based HR variability analysis is performed, in which inter-beat-internal is derived from the first and second HS signatures. To that end, automatic identification of HS signatures and signal denoising techniques are developed accordingly. Multiple validation experiments are conducted on human subjects. Our results imply that the new methodology effectively improves remote cardiac sensing performance using the radar technology and presents an exciting opportunity to expand Healthcare IoT, improving patient experiences and outcomes.