Abstract
The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can effectively track multiple targets in a single scenario. However, for GM-PHD, unknown target behavior, e.g., target birth or target intersection, produces difficulties in terms of accurate estimation. First of all, GM-PHD assumes the model parameters about the birth target are prior information, which results in the inability to detect the birth target that occurs at random in complex scenarios. Then, since the measurements generated by the intersected targets overlap each other, GM-PHD cannot distinguish these targets, resulting in a biased estimation of the state and number of targets. To solve these problems, this paper proposes an improved GM-PHD filter with a birth intensity and spawned intensity updating method based on the trajectory situation feedback. In the filtering process, the trajectory initiation feedback formed by the rule-based correlation of Gaussian components is introduced to GM-PHD to adjust the birth intensity in real time, which is used to improve the detection of birth targets. Simultaneously, the analysis of trajectory situation is designed to determine the relative motion trend between targets. On this basis, the filter improves the recognition of the intersected targets by enhancing the spawned intensity. Simulation results demonstrate that the proposed algorithm achieves better performance on the state and number of targets in complex scenarios, and shows superiority to other GM-PHD filters.
Funder
National Natural Science Foundation of China
Equipment Preresearch Fund
Subject
General Earth and Planetary Sciences
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献