Bistatic CRLBs for TTD array resource allocation during JCAS

This paper conveys the theoretical sensing capability of a Joint Communication and Sensing (JCAS) system that utilizes a True-Time-Delay (TTD) array configuration. The TTD beamformer separates subcarrier beams into different angular locations for wide-beam coverage. The bistatic sensing performance of a TTD array configuration is modeled as a function of the number of array elements and the fractional power supplied by the transmitter. The sensing capability when employing a TTD array configuration is derived using the Cram\'er-Rao Lower Bound (CRLB) of a bistatic delay-AOA(Angle Of Arrival)-phase log-likelihood function which is obtained by cross-correlating complex eigenvectors extracted from the quadratic time-frequency domain representation of multicomponent phase-coded signals. The multicomponent phase-codes used here are synchronization signals that are defined by the 5G communications standard. The bistatic delay-AOA-phase estimation capability whilst utilizing these signals and a TTD configuration was seen to be highly non-linear with respect to the number of elements and fractional power supplied for the surveillance scenario shown; an allocation of 30 antenna elements supplied with maximum power made a sub-meter and sub-degree estimation capability theoretically possible.

Enhancing Vital Sign Estimation Performance of FMCW MIMO Radar by Prior Human Shape Recognition

Radio technology enabled contact-free human posture and vital sign estimation is promising for health monitoring. Radio systems at millimeter-wave (mmWave) frequencies advantageously bring large bandwidth, multi-antenna array and beam steering capability. \textit{However}, the human point cloud obtained by mmWave radar and utilized for posture estimation is likely to be sparse and incomplete. Additionally, human's random body movements deteriorate the estimation of breathing and heart rates, therefore the information of the chest location and a narrow radar beam toward the chest are demanded for more accurate vital sign estimation. In this paper, we propose a pipeline aiming to enhance the vital sign estimation performance of mmWave FMCW MIMO radar. The first step is to recognize human body part and posture, where we exploit a trained Convolutional Neural Networks (CNN) to efficiently process the imperfect human form point cloud. The CNN framework outputs the key point of different body parts, and was trained by using RGB image reference and Augmentative Ellipse Fitting Algorithm (AEFA). The next step is to utilize the chest information of the prior estimated human posture for vital sign estimation. While CNN is initially trained based on the frame-by-frame point clouds of human for posture estimation, the vital signs are extracted through beamforming toward the human chest. The numerical results show that this spatial filtering improves the estimation of the vital signs in regard to lowering the level of side harmonics and detecting the harmonics of vital signs efficiently, i.e., peak-to-average power ratio in the harmonics of vital signal is improved up to 0.02 and 0.07dB for the studied cases.