Approaches to Cartesian Data Association Passive Radar Tracking in a DAB/DVB Network

Abstract

We present a target tracking system for passive radar suitable for a digital audio/video broadcast (DAB/DVB) network with orthogonal frequency division multiplexing (OFDM) illuminators of opportunity. In this system bistatic range and range-rate are available as a measurement while angular information is assumed unavailable or of very poor quality. There is also a novel association ambiguity between the signals and illuminators, this in addition to the usual one between the signals and targets, and hence an interesting data association problem. Our intention is to provide tracks directly in the geographic space (3-D Cartesian domain). Here, four filters are suggested. The first two are the extended Kalman filter (EKF) and unscented Kalman filter (UKF) based on a joint probabilistic data association (JPDA) modified to incorporate the additional assignment complexity. The other two filters are a bootstrap particle filter (BPF) and an auxiliary particle filter (APF) based on the data association under the probabilistic multi-hypothesis tracker (PMHT) measurement model, which means that each measurement's assignment is independent of all others. Simulation results show that these four filters can work properly, albeit naturally with different performance characteristics and complexity.