IMM Robust Cardinality Balance Multi-Bernoulli Filter for Multiple Maneuvering Target Tracking with Interval Measurement  

作　　者：YANG Biao ZHU Shengqi HE Xiongpeng 

机构地区：[1]National Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China

出　　处：《Chinese Journal of Electronics》2021年第6期1141-1151,共11页电子学报（英文版）

基　　金：supported by the National Key R&D Program of China(No.2016YFE0200400);the National Natural Science Foundation of China (No.61771015);the Key R&D Program of Shaanxi Province(No.2017KW-ZD-12);the Innovative Research Group of National Natural Science Foundation of China (No.61621005);the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project)(No.B18039)。

摘　　要：This paper presents a novel Interacting multi-model(IMM) Robust Cardinality balance multitarget multi-Bernoulli(R-CBMe MBer) filter to solve the maneuvering target tracking problem in the case of interval measurement, unknown detection probability and unknown clutter density. In essence, IMM R-CBMe MBer filter is an extended application of R-CBMe MBer filter.In the IMM R-CBMe MBer filter, the target state is first extended to distinguish clutter from the real target.The detection probability and model probability of the target can be adaptively updated. Then, generalized likelihood function and IMM algorithm are introduced to interactively predict and update the state of the target in the IMM R-CBMe MBer filtering process. In addition,a particle application of the IMM R-CBMe MBer filter is given, and a numerical experiment is designed under nonlinear conditions. Meanwhile, Doppler information of the target is employed to estimate the velocity of each maneuvering target. Numerical experiments also verify that the IMM R-CBMe MBer filter can effectively estimate the target position, target velocity, target detection probability and clutter number in the condition of unknown detection probability, unknown clutter rate and interval measurement.

关 键 词：Multiple maneuvering target tracking Random finite set Interacting multiple model Cardinality balance multiple targets multi-Bernoulli filter Generalized likelihood function 

分 类 号：TN713[电子电信—电路与系统]