PROJECT TITLE :

A Bernoulli Filter for Extended Target Tracking Using Random Matrices in a UWB Sensor Network

ABSTRACT:

During this paper, we have a tendency to propose a replacement tractable Bernoulli filter based on the random matrix framework to track an extended target in an ultra-wideband (UWB) sensor network. The ensuing filter jointly tracks the kinematic and form parameters of the target and is termed the extended target Gaussian inverse Wishart Bernoulli (ET-GIW-Ber) filter. Closed kind expressions for the ET-GIW-Ber filter recursions are presented. A clustering step is inserted into the measurement update stage in order to possess a computationally tractable filter. Still, a replacement technique that's per the applied clustering technique is embedded into the filter recursions so as to adaptively estimate the time-varying number of measurements of the extended target. The simulation results demonstrate the strong and effective performance of the proposed filter. Furthermore, real information collected from a UWB sensor network are used to assess the performance of the proposed filter. It is shown that the proposed filter yields a terribly promising performance in estimation of the kinematic and form parameters of the target.