Multitarget particle filter addressing ambiguous Radar data in TBD

In this paper, we have addressed the problem of multiple target tracking in Track-Before-Detect (TBD) context using ambiguous Radar data. TBD is a method which uses raw measurement data, i.e. reflected target power, to track targets. Tracking can be defined as the estimation of the state of a moving object based on measurements. These measurements are in this case assumed to be the radar echoes ambiguous in range and doppler. The estimated states are produced by means of a tracking filter. The filtering problem has been solved by using a Particle Filter (PF). Particle filtering is a signal processing methodology, which can effectively deal with nonlinear and non-Gaussian signals by a sample-based approximation of the state probability density function (pdf). A standard multitarget SIR Particle Filter is extended so that it can handle range/doppler ambiguities and eclipsing effects. Such extension is required for its use in practice and to enhance tracking accuracy. The proposed particle filter succeeds in resolving range and doppler ambiguities, detecting and tracking multiple targets in a TBD context.