杂波环境下基于全邻模糊聚类的联合概率数据互联算法

刘俊; 刘瑜; 何友; 孙顺

doi:10.11999/JEIT150849

杂波环境下基于全邻模糊聚类的联合概率数据互联算法

doi: 10.11999/JEIT150849

刘俊¹,
刘瑜²,
何友¹,
孙顺¹

1.
(海军航空工程学院信息融合研究所烟台 264001) ②(北京航空航天大学电子信息工程学院北京 100191)

基金项目:

国家自然科学基金(61471383)

计量
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- 被引次数: 0
出版历程
- 收稿日期: 2015-07-16
- 修回日期: 2016-03-08
- 刊出日期: 2016-06-19

Joint Probabilistic Data Association Algorithm Based on All-neighbor Fuzzy Clustering in Clutter

1.
(Research Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai 264001, China)

Funds:

The National Natural Science Foundation of China (61471383)

摘要

摘要: 针对杂波环境下的多目标跟踪数据互联问题，该文提出基于全邻模糊聚类的联合概率数据互联算法(Joint Probabilistic Data Association algorithm based on All-Neighbor Fuzzy Clustering, ANFCJPDA)。该算法根据确认区域中量测的分布和点迹-航迹关联规则构造统计距离，以各目标的预测位置为聚类中心，利用模糊聚类方法，计算相关波门内候选量测与不同目标互联的概率，通过概率加权融合对各目标状态与协方差进行更新。仿真分析表明，与经典的联合概率数据互联算法(Joint Probabilistic Data Association algorithm, JPDA)相比，ANFCJPDA较大程度地改善了算法的实时性，并且跟踪精度与JPDA相当。
- 多目标跟踪 /
- 多传感器 /
- 数据互联 /
- 模糊聚类
Abstract: This paper proposes a new Joint Probabilistic Data Association algorithm based on All-Neighbor Fuzzy Clustering (ANFCJPDA) for mutitarget tracking in the clutter. Firstly, distance measure is established according to measurements distribution in validation area and data correlation rules. Then, the predicted position is set up as a cluster center, and the association probabilities are calculated on the basis of fuzzy clustering, which are used as weights to update targets state and the covariance. Simulation results show that the proposed method reduces highly the computational complexity compared to conventional Joint Probabilistic Data Association (JPDA) technique, and is effective for multiple target tracking in a cluttered environment.
- Multiple target tracking /
- Multisensor /
- Data association /
- Fuzzy clustering

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