In this paper, a Sequential Monte Carlo Cardinalized Probability Hypothesized Density (SMC-CPHD) filter based on Track-Before-Detect (TBD) algorithm in heavy-tailed clutter is proposed. The CPHD filter, which jointly propagates the first-order statistical moment and cardinality distribution, is an effective and efficient solution to multitarget filter problem in the presence of data association uncertainty, detect uncertainty, and time-varying. The TBD algorithm is extremely sensitive to the presence of clutter spikes in the data as the formulation of the TBD filter is dependent on knowledge of underlying clutter behavior. Therefore, the clutter is modeled in terms of K distribution with sea surface background and G0 distribution with primary forest background, while the popular Swerling targets of type 1 and 3 are considered to capture the target amplitude fluctuation frame-to-frame in this paper. The utilization of K distribution and G0 distribution clutter models instead of conventional Rayleigh distribution are verified through analysis of simulated and realistic data. The relevant results prove that proposed SMC-CPHD-TBD algorithm based on K distribution model and G0 distribution model can improve the detect and tracking performance over the conventional method in heavy-tailed clutter scenario. Additionally, the numerical simulations show that proposed CPHD-TBD algorithm can output the multitarget trajectories in the better precision with considerably less computational complexity compared with DP-TBD algorithm.
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