Open AccessIMM Robust Cardinality Balance Multi‐Bernoulli Filter for Multiple Maneuvering Target Tracking with Interval Measurement
Author(s) -
Biao YANG,
Shengqi ZHU,
Xiongpeng HE
Publication year - 2021
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2021.08.009
Subject(s) - clutter , filter (signal processing) , control theory (sociology) , interval (graph theory) , bernoulli's principle , tracking (education) , mathematics , cardinality (data modeling) , algorithm , position (finance) , probability density function , computer science , constant false alarm rate , artificial intelligence , computer vision , radar , statistics , engineering , psychology , telecommunications , pedagogy , control (management) , finance , combinatorics , economics , data mining , aerospace engineering
This paper presents a novel Interacting multi‐model (IMM) Robust Cardinality balance multi‐target multi‐Bernoulli (R‐CBMeMBer) 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‐CBMeMBer filter is an extended application of R‐CBMeMBer filter. In the IMM R‐CBMeMBer 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‐CBMeMBer filtering process. In addition, a particle application of the IMM R‐CBMeMBer 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‐CBMeMBer 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.