The PMHT for Passive Radar in a DAB/DVB Network

Passive Bistatic Radar (PBR), also known as Passive Coherent Location (PCL), uses illuminators of opportunity. Passive radar using signals in a single frequency network modulated according to the Digital Audio/Video Broadcasting (DAB/DVB) standards using orthogonal frequency division multiplexing (OFDM) has recently been of increasing interest. There has been considerable research to develop tracking systems addressing its inherent difficulties [3]—[7], [10]—[13]; the poor quality–or absence–of angular information, and the lack of label of the transmitter on top of the usual target/measurement association concerns. First, there are algorithms using the Multi-Hypothesis Tracker (MHT) [12], [13] addressing the complexity problem from association ambiguities between measurement, targets and illuminators by initially forming two dimensional (measurement-target) hypotheses in the two-dimensional range/Doppler domain. Tracking is thence performed directly on target parameters by the MHT without considering the association between measurements and illuminators: the range/Doppler MHT extracts measurements and removes false alarms. Then, de-ghosting is performed by evaluating likelihood probabilities of possible data associations. When a Cartesian track is confirmed, the remaining tracks from other possible associations are declared false and tracking starts in the Cartesian domain. This MHT approach is good but but is not without issues. One is the appropriate motion model in range and Doppler space: probably the target dynamics in the Cartesian domain are known, the trajectories are not easily described in a space of target parameters, because the trajectories are related to illuminator/receiver/target geometry and there is association ambiguity among measurements, illuminators, and targets. And that is another concern: the illuminator association is never explicitly addressed. Now, track maintenance algorithms that operate directly in Cartesian coordinates have been explored [4], [5], one using modified Joint Probabilistic Data Association (JPDA) and another a particle filter. For the former, in order to address the large number of threelist hypotheses, a “super-target” idea was proposed; and the particle filters work under the PMHT measurement model that each measurement’s assignments are independent of others’. These methods have also been examined downstream from an initiation approach (the PMHTI method, suggested in [6]) that initiates tracks in Cartesian coordinates. In fact the PMHT seems to be an effective and natural way to accommodate the data association with the extra list (transmitters). So in this paper, we present it: it is really very simple. This tracker, combined with the initiation algorithm (the modified PMHTI method in [6]), shows excellent performance in comparison with the JPDA filter and particle filter.