Eryildirim, AbdulkadirGuldogan, Mehmet Burak2025-10-242025-10-2420161530-437X1558-1748https://doi.org/10.1109/JSEN.2016.2544807https://hdl.handle.net/20.500.12899/3588In this paper, we propose a new tractable Bernoulli filter based on the random matrix framework to track an extended target in an ultra-wideband (UWB) sensor network. The resulting filter jointly tracks the kinematic and shape parameters of the target and is called the extended target Gaussian inverse Wishart Bernoulli (ET-GIW-Ber) filter. Closed form expressions for the ET-GIW-Ber filter recursions are presented. A clustering step is inserted into the measurement update stage in order to have a computationally tractable filter. In addition, a new method that is consistent with the applied clustering method is embedded into the filter recursions in order to adaptively estimate the time-varying number of measurements of the extended target. The simulation results demonstrate the robust and effective performance of the proposed filter. Furthermore, real data 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 very promising performance in estimation of the kinematic and shape parameters of the target.eninfo:eu-repo/semantics/closedAccessRandom finite set; extended target tracking; Bernoulli filter; Gaussian inverse Wishart; inverse Wishart distribution; random matrix; ultra-wideband; sensor network; localizationArticle10.1109/JSEN.2016.25448071611436243732-s2.0-84968750646Q1WOS:000375563700047Q1