基于改进协方差控制的传感器管理算法

针对传统的基于协方差控制的传感器管理算法使用全遍历方法所造成的计算量大,以及传感器切换频繁的问题,提出了一种基于改进协方差控制的传感器管理算法。该算法在每一时刻首先判断前一时刻所用传感器组是否能够满足目标跟踪需求,以滤波协方差与期望协方差的偏差作为参考,结合量纲变换和特征值求取,为协方差偏差矩阵经过量纲变换后得到的量纲一致阵的所有特征值设定一个精度阈值,然后判断滤波协方差是否满足期望,从而决定是否维持当前选择的传感器组。在目标作匀速、匀加速、协同转弯等多种场景下进行了算法性能测试分析,仿真结果表明,该算法不仅在大部分场景下满足目标跟踪精度,而且能够提高传感器管理算法的实时性,同时降低传感器的切换频率。

Sensor Management Algorithm Based on Improved Covariance Control

To solve the problem of high computational need and frequent switching caused by complete search algorithm in traditional sensor management algorithm based on covariance control, an improved algorithm is proposed in this paper. The algorithm determines whether the sensors used at last time step can meet the accuracy of target tracking at first, by consulting the bias of the filtering covariance matrix and the desired one, using dimensional transformation and eigenvalue calculation. Set a threshold for all eigenvalues of the dimensional uniformity matrix which is transformed from the bias matrix, then determine whether the filtering covariance satisfys expectation or not, and decide whether to maintain the current sensors for choice. The performance of the proposed algorithm is tested in constant velocity (CV), constant acceleration (CA)and coordinate turn (CT)cases. The simulation results demonstrate that the algorithm not only meets target tracking accuracy in most of the tracking scenes, but also can improve the timeliness of sensor management and reduce switch frequency.