(A) statistical theory of long-range target tracking and sensor formation

Abstract

In this paper, a target localization method using multiple sensors based on the time difference of arrival (TDOA) data is investigated under the assumption that the target is far off from the sensors. We first examine the geometric features of the problem, which provide a intuitional perspective for understanding the localization method. Next, we compute the Fisher information matrix (FIM) and the Cramer-Rao lower bounds (CRLB) by using the power series expansion and analyze the variability of the angle and the range estimates. These values reveal the relationship between the sensor formation and the tracking performance. We also present a method for finding the maximum likelihood estimate of the target location and suggest a dynamic target tracking method using the extended Kalman filter.