Multiple Target Tracking Using Likelihood Particle Filtering and Adaptive Waveform Design

In multiple target tracking with radar, weak target measurements are often hard to observe due to masking by sidelobes of measurements from stronger targets. The result is lost tracks and deteriorated joint tracking performance. In this work, we design configurable waveforms and adjust the positioning of their ambiguity function (AF) sidelobes to unmask weak targets and improve tracking performance. To track multiple targets, we develop a tracker that is based on the likelihood particle filter and the independent partitions proposal method. The proposed independent partitions likelihood particle filter (IPLPF) accurately processes the high resolution measurements resulting from the use of Björck CAZAC sequences that we use to construct our configurable waveforms and tracks a fixed and known number of targets. We provide simulation results that demonstrate the improvement in tracking performance of multiple targets when using adaptively configured MCPC Björck CAZAC waveforms versus using non-adaptive configurations or single Björck CAZACs.