自适应目标新生δ广义标签多伯努利滤波算法

doi:10.19665/j.issn1001-2400.2019.02.003

摘要/Abstract

摘要：

针对传统广义标签多伯努利滤波算法因需已知新生目标状态分布信息而导致在实际场景中估计精度下降的问题,提出一种新的自适应目标新生δ广义标签多伯努利算法。该算法以广义标签多伯努利滤波器为基础,利用上一时刻接收到的量测信息反推当前时刻新生目标的存活概率和状态信息,并给出其标签伯努利随机集的参数表示。仿真结果表明,所提算法对于未知新生目标先验信息的复杂运动场景具有较强的多目标跟踪鲁棒性,且跟踪精度以及时间耗费均优于传统广义标签多伯努利滤波器。

关键词: 多目标跟踪, 随机有限集, δ广义标签多伯努利, 自适应目标新生

Abstract:

Aiming at the problem that the standard δ-generalized labeled multi-Bernoulli (δ-GLMB) filter requires a priori knowledge of target birth distributions, which leads to the reduction of estimation accuracy in a real world scenario, an adaptive target birth δ-GLMB filtering algorithm is proposed. Based on the δ-GLMB filter, the new algorithm approximates the existence probabilities and kinematic states of birth targets using measured data from the previous time, and provides parameterized representations of labeled Bernoulli random finite sets of new birth targets in the current time. Simulation results indicate that the proposed algorithm has a strong robustness, and a better performance in tracking accuracy and time consumption than the standard δ-GLMB filtering algorithm under the unknown priori knowledge of the birth targets complex scenario.

Key words: multi-target tracking, random finite sets, δ-generalized labeled multi-Bernoulli, adaptive target birth

中图分类号:

TN953

李翠芸,陈东伟,石仁政. 自适应目标新生δ广义标签多伯努利滤波算法[J]. 西安电子科技大学学报, 2019, 46(2): 12-16.

LI Cuiyun,CHEN Dongwei,SHI Renzheng. Adaptive target birth δ-generalized labeled multi-Bernoulli filtering algorithm[J]. Journal of Xidian University, 2019, 46(2): 12-16.

图/表 4

参考文献 11

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