Dual Detection-Guided Newborn Target Intensity Based on Probability Hypothesis Density for Multiple Target Tracking

被引:2
作者
Gao, Li [1 ]
Ma, Yongjie [1 ]
机构
[1] Shangqiu Polytech, Dept Mech & Elect Engn, Shangqiu 476000, Peoples R China
来源
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | 2016年 / 10卷 / 10期
关键词
Multi-target tracking; probability hypothesis density; newborn target intensity; Gaussian mixture; RANDOM FINITE SETS; PHD; FILTER;
D O I
10.3837/tiis.2016.10.025
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Probability Hypothesis Density (PHD) filter is a suboptimal approximation and tractable alternative to the multi-target Bayesian filter based on random finite sets. However, the PHD filter fails to track newborn targets when the target birth intensity is unknown prior to tracking. In this paper, a dual detection-guided newborn target intensity PHD algorithm is developed to solve the problem, where two schemes, namely, a newborn target intensity estimation scheme and improved measurement-driven scheme, are proposed. First, the newborn target intensity estimation scheme, consisting of the Dirichlet distribution with the negative exponent parameter and target velocity feature, is used to recursively estimate the target birth intensity. Then, an improved measurement-driven scheme is introduced to reduce the errors of the estimated number of targets and computational load. Simulation results demonstrate that the proposed algorithm can achieve good performance in terms of target states, target number and computational load when the newborn target intensity is not predefined in multi-target tracking systems.
引用
收藏
页码:5095 / 5111
页数:17
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