Open AccessAdaptive cardinality balanced multi‐target multi‐Bernoulli filter based on cubature Kalman
Author(s) -
Wang Haihuan,
Lyu Xiaoyong,
Ma Long
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0670
Subject(s) - cardinality (data modeling) , particle filter , robustness (evolution) , kalman filter , computer science , computation , bernoulli's principle , algorithm , computational complexity theory , range (aeronautics) , filter (signal processing) , mathematical optimization , monte carlo method , tracking (education) , control theory (sociology) , mathematics , statistics , engineering , artificial intelligence , data mining , psychology , pedagogy , biochemistry , chemistry , control (management) , computer vision , gene , aerospace engineering
The sequential Monte Carlo cardinality balanced multi‐Bernoulli (SMC‐CBMeMBer) filter provides a good framework to cope with the multi‐target tracking problem. However, the standard SMC‐CBMeMBer filter suffers from the particles’ degradation problem seriously. Using the measurements to construct the proposal density in the step of predict can effectively solve the above problem, but this kind of approach brings an amount of computation and causes the overestimation of the target number. To examine the quality of each predicted particle adaptively and use the cubature Kalman filter (CKF) to refine the poor‐quality particles with the aid of the current measurements is proposed in this study. This method manages to alleviate the particles degradation problem without increasing the computational complexity seriously since only a part of the particles is refined by the CKF. Also, the proposed method can avoid cardinality overestimation caused by abuse of measurements. A range of simulations is performed to test the performance of the proposed method. The results confirm the effectiveness and robustness of the novel method.