Through-Wall Human Tracking With Multiple Doppler Sensors Using an Artificial Neural Network

Abstract

An artificial neural network is proposed to track a human using the Doppler information measured by a set of spatially distributed sensors. The neural network estimates the target position and velocity given the observed Doppler data from multiple sensors. It is trained using data from a simple point scatterer model in free space. The minimum required number of sensors is investigated for the robust target tracking. The effect of sensor position on the estimation error is studied. For the verification of the proposed method, a toy car and a human moving in a circular track are measured in line-of-sight and through-wall environments. The resulting normalized estimation errors on the target parameters are less than 5%.