Joint Detection and Tracking of Unresolved Targets with a Monopulse Radar Using a Particle Filter

Detection and estimation of multiple unresolved targets with a monopulse radar is a challenging problem. For ideal single bin processing, it was shown in the literature that at most two unresolved targets can be extracted from the complex matched filter output signal. In this thesis, a new algorithm is developed to jointly detect and track more than two targets from a single detection. This method involves the use of tracking data in the detection process. For this purpose, target states are transformed into the detection parameter space, which involves high nonlinearity. In order to handle this, the sequential Monte Carlo (SMC) method, which has proven to be effective in nonlinear non-Gaussian estimation problems, is used as the basis of the closed loop system for tracking multiple unresolved targets. In addition to the standard SMC steps, the detection parameters corresponding to the predicted particles are evaluated using the nonlinear monopulse radar beam model. This in turn enables the evaluation of the likelihood of the monopulse signal given tracking data. Hypothesis testing is then used to find the correct detection event. The particles are updated and resampled according to the hypothesis that has the highest likelihood (score). A simulated amplitude comparison monopulse radar is used to generate the data and to validate the extraction and tracking of more than two unresolved targets.