Adaptive Marginal Multi-Target Bayes Filter without Need for Clutter Density for Object Detection and Tracking

被引:2
|
作者
Liu, Zongxiang [1 ,2 ]
Zhou, Chunmei [1 ,2 ]
Luo, Junwen [1 ,2 ]
机构
[1] Shenzhen Univ, Coll Elect & Informat Engn, Shenzhen 518060, Peoples R China
[2] Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 19期
基金
中国国家自然科学基金;
关键词
multi-target tracking; least squares technique; random finite set; marginal multi-target Bayes filter; adaptive filter; MULTI-BERNOULLI FILTER; RANDOM FINITE SETS;
D O I
10.3390/app131911053
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The random finite set (RFS) approach for multi-target tracking is widely researched because it has a rigorous theoretical basis. However, many prior parameters such as the clutter density, survival probability and detection probability of the target, pruning threshold, merging threshold, initial state of the birth object and its error covariance matrix are required in the standard RFS-based filters. In real application scenes, it is difficult to obtain these prior parameters. To address this problem, an adaptive marginal multi-target Bayes filter without the need for clutter density is proposed. This filter obviates the need for prior clutter density and survival probability. Instead of using the prior initial states of newborn targets and their error covariance matrices, it uses two scans of observations to generate the initial states of potential birth targets and their error covariance matrices according to the least squares technique. Simulation results reveal that the proposed adaptive filter has smaller OSPA and OSPA(2) errors as well as less cardinality error than the adaptive RFS-based filters. The OSPA and OSPA(2) errors have been reduced by more than 20% compared to those of the adaptive RFS-based filters.
引用
收藏
页数:17
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