Effect of state space partitioning on Bayesian tracking for UWB radar sensor networks

Abstract

Multistatic radar systems based on ultrawide-band (UWB) technology, also known as UWB radar sensor networks (RSNs), have been shown to represent a very promising solution to localize an intruder moving within a small surveillance area. In this paper, a new algorithm based on particle filtering is proposed and compared with grid-based Bayesian approach for target tracking in UWB RSNs with one transmitter and multiple receivers. The grid-based Bayesian approach verifies the whole surveillance area in a discretized manner for the presence of target, whereas particle filtering only focuses on the predicted particle positions. Numerical results illustrate how consideration of only a subset of space in particle filtering and discretization of the space in grid-based Bayesian approach can affect the tracking performance. Finally, the two approaches are compared in terms of algorithm complexity.