基于IST-RSCKF-MB的雷达多目标跟踪算法
A Multi-Target Tracking Algorithm for Radar Based on IST-RSCKF-MB
李艳玲 1方遒 1屠亚杰1
作者信息
- 1. 厦门理工学院机械与汽车工程学院,福建 厦门 361024
- 折叠
摘要
针对多目标跟踪(MTT)算法存在较大的计算量问题,将改进渐消因子的强跟踪(IST)引入快速平方根容积卡尔曼滤波(RSCKF)中,并联合新息自相关矩阵和Murty算法确定最佳假设的多伯努利(MB)算法,提出改进强跟踪平方根容积卡尔曼多伯努利(IST-RSCKF-MB)的雷达多目标跟踪算法.仿真结果显示,所提出算法的运算效率和滤波精度比平方根容积卡尔曼多伯努算法、改进强跟踪平方根容积卡尔曼多伯努利混合算法、扩展卡尔曼多伯努利算法和无迹卡尔曼多伯努利算法均有不同程度提高,误差率分别减少0.36%、4.71%、14.75%和0.17%,适用于嵌入式目标跟踪算法实现.
Abstract
The idea of strong tracking in improving the fading factor is introduced into the simplified square root cubature Kalman filter,the multi-Bernoulli algorithm with the best hypothesis determined by the new autocorrelation matrix and Murty algorithm,and an IST-RSCKF-MB algorithm of radar multi-target tracking is thus developed to address the issue of the computationally intensive calculation of multi-target tracking(MTT).Simulation results show that the proposed algorithm guarantees better efficiency and precision than those by the multi-Bernoulli algorithm based on square root Cubature Kalman,the multi-Bernoulli mixture algorithm based on improved strong tracking square root Cubature Kalman,the multi-Bernoulli algorithm based on Unscented Kalman filter and Extended Kalman filter respectively.It is a good solution to the implementation of embedded target tracking algorithm.
关键词
雷达/多目标跟踪/平方根容积卡尔曼滤波(RSCKF)/强跟踪滤波(STF)/多伯努利算法(MB)Key words
radar/multi-target tracking/square-root Cubature Kalman filter(RSCKF)/strong tracking filter(STF)/multi-Bernoulli algorithm(MB)引用本文复制引用
基金项目
福建省自然科学基金(2022J011247)
出版年
2024
NETL
NSTL
万方数据