RFS‐based extended target multipath tracking algorithm
Xinglin Shen, Zhiyong Song, Hongqi Fan, Qiang Fu · IET Radar Sonar & Navigation · 2017
The issue of extended target multipath tracking has been studied. Based on the geometry model on the sea surface, a novel extended target multipath tracking algorithm is proposed under the random finite sets (RFS) framework. In the algorithm, extended target probability hypothesis density (ET‐PHD) filter is used as a pre‐processor, and multipath Bernoulli filter (MPBF) is used as main processor. The outputs of the ET‐PHD filter are treated as pseudo‐measurements and fed into the MPBF. At last, probability of existence and target state are estimated from the outputs of MPBF. In contrast with the existing extended target filter and multipath filter, the proposed algorithm does not attempt to get the rigorous solution and avoids the high computational complexity. In order to get the solution of the algorithm, its particle filter implementation has been proposed. Results of simulation show that the proposed algorithm estimates target state accurately. Comparing with extended target Bernoulli filter, the proposed algorithm has good tracking performance at the presence of multipath effect.