Target tracking in uncertain Multipath Environment using distributed Angle-of-Arrival observation

This work considers the problem of target tracking in complex multipath environments using Angle-of-Arrival (AoA) observations produced by a multistatic passive radar array network. In the presence of uncertain multipath channel properties, Simultaneous Target and Multipath Positioning (STAMP) problem is formulated by jointly estimating the target state and multipath channel parameters. In this work, a single cluster process is formulated using a recursive Bayesian estimation framework, where the target state is defined as a parent process and the reflector state is defined as the daughter process. A multi-hypothesis data association method is used with Probability Hypothesis Density (PHD) filtering to improve the accuracy of the target state estimate. Simulation results for an indoor target positioning problem demonstrates substantial improvements in localization accuracy with this method.