Multi-Target Track Association Model Based on Uncertain Noise Distribution
QIU Jianjie， CAI Yichao， LI Hao， LONG Weiyu， HAN Yu
To resolve the noise description in the traditional track correlation model is too ideal, thus a new multi-target track correlation model was proposed in this paper. The model utilized the uncertainty log-normal distribution from uncertainty theory to describe the track correlation noise. Taking a uniformly accelerated linear moving target as a case study, its motion equation and observation equation were re-constructed before the tracking filtering process was derived. To further verify the effectiveness and robustness of the proposed model, mixed noise was adopted to simulate various noises in a complex electromagnetic environment and verification under different noise environments, correlation algorithms, and target numbers was conducted. The results show that the model proposed has achieved better correlation accuracy and robustness.
Abstract：To resolve the noise description in the traditional track correlation model is too ideal, thus a new multi-target track correlation model was proposed in this paper. The model utilized the uncertainty log-normal distribution from uncertainty theory to describe the track correlation noise. Taking a uniformly accelerated linear moving target as a case study, its motion equation and observation equation were re-constructed before the tracking filtering process was derived. To further verify the effectiveness and robustness of the proposed model, mixed noise was adopted to simulate various noises in a complex electromagnetic environment and verification under different noise environments, correlation algorithms, and target numbers was conducted. The results show that the model proposed has achieved better correlation accuracy and robustness.
邱建杰, 蔡益朝, 李浩, 龙威宇, 韩瑜. 基于不确定噪声分布的多目标航迹关联模型[J]. 空天防御, 2023, 6(3): 104-112.
QIU Jianjie, CAI Yichao, LI Hao, LONG Weiyu, HAN Yu. Multi-Target Track Association Model Based on Uncertain Noise Distribution. Air & Space Defense, 2023, 6(3): 104-112.
