This paper proposes a method for cooperative multi-static Multiple Input Multiple Output (MIMO) radar that can estimate the number of targets. The purpose of this system is to monitor humans in an indoor environment. First, target positions within the estimation range are roughly detected by the Capon method and the mode vector corresponding to the detected positions is calculated. The mode vector is multiplied by the eigenvector to eliminate the virtual image. The spectrum of the evaluation function is calculated from the remaining positions, and the number of peaks in the spectrum is defined as the number of targets. Experiments carried out in an indoor environment confirm that the proposed method can estimate the number of targets with high accuracy.

This paper introduces a method that identifies human activity from the height and Doppler Radar Cross Section (RCS) information detected by Multiple-Input Multiple-Output (MIMO) radar. This method estimates the three-dimensional target location by applying the MUltiple SIgnal Classification (MUSIC) method to the observed MIMO channel; the Doppler RCS is calculated from the signal reflected from the target. A gesture recognition algorithm is applied to the trajectory of the temporal transition of the estimated human height and the Doppler RCS. In experiments, the proposed method achieves over 90% recognition rate (average).

This paper presents the Received-Signal-Strength-Indicator (RSSI) based living-body radar, which uses only a single RF front-end and a few parasitic antennas. This radar measures the RSSI variation at the single active antenna while varying the terminations of the parasitic antennas. The propagation channel is estimated from just the temporal transition of RSSI; our proposal reconstructs the phase information of the signal. In this paper, we aim to estimate the direction of living-body. Experiments are carried out and it is found that most angular errors are within the limit of the angular width of the living-body.