密集杂波环境下基于KD树优化的DBR-RANSAC目标跟踪算法

DBR-RANSAC Target Tracking Algorithm Based on KD Tree Optimization in Dense Clutter Environment

地面战场侦察雷达存在虚假目标多、真实目标难以区分等问题。为了降低复杂环境下密集杂波的影响,形成稳定航迹,并提高雷达数据处理的实时性,文中提出一种基于K维树优化的密度聚类随机采样一致性多目标跟踪算法,降低使用批处理进行航迹起始时对大量数据进行密度聚类带来的运算负担;同时,利用相控阵雷达的波位信息对数据关联过程进行优化。仿真和试验数据验证表明:文中提出的算法在密集杂波环境下可有效提高运算效率并保持较高的跟踪性能。

The ground battlefield reconnaissance radar has many false targets, and the real targets are difficult to distinguish. In order to reduce the impact of dense clutter in complex ground environments, make a stable track and improve the real-time performance of radar data processing, a density-based recursive random sample consensus multiple targets tracking algorithm based on K-dimensional tree optimization is proposed. This algorithm reduces the computational burden caused by the density clustering of a large amount of data at the track start using batch processing, at the same time, the wave position information of the phased array radar is used to optimize the data association process. By verifying the algorithm with simulation and experimental data, the results show that the proposed algorithm can effectively improve operation efficiency and maintain high tracking performance under dense clutter environment.