Integrated signal processing, data association, and tracking

This thesis discusses four techniques to successfully track multiple closely-spaced and unresolved targets using monopulse radar measurements. It explores statistical estimation techniques based on the maximum likelihood criterion and Gibbs sampling, and addresses concerns about the accuracy of the measurements delivered thereby. In particular, the Gibbs approach can deliver joint measurements (and the associated covariances) from both targets, and it is therefore natural to consider a joint single filter. The ideas are compared; and amongst the various strategies discussed, a particle filter that approximates the targets' states' conditional pdf, bypassing the measurement extraction stage, and operating directly on the monopulse sum/difference data, i.e., without measurement extraction proves to be best. With successful tracking of those targets being achieved with the aid of nonlinear particle filters, the problem of detecting a target spawn will be tackled. Particle filters will be employed as nonlinear tracking filters to approximate the posterior probability densities of the targets' states under different hypotheses of the number of targets, which in turn can be used to evaluate the likelihood ratio between two different hypotheses at subsequent time steps. Ultimately, a quickest detection procedure based on sequential processing of the likelihood ratios will be used to decide on a change in the underlying target model as an indication of a newly spawning target. Radar signal processing, data association and target tracking are handled simultaneously.