基于协方差矩阵元素的多目标角跟踪方法

Multiple targets angle tracking algorithm based on the elements of the covariance matrix

提出了一种多目标角跟踪算法.将相邻时间段估计得到的协方差矩阵相减,对差距阵线性化近似得到了一组关于角度差的线性方程组,重复解方程组可以得到不同时间段各个目标的角度.本算法不需要更新信号子空间;不同时间段之间各个目标的角度是自动关联的,省去了运算量较大的数据关联过程;适用于目标个数大于阵元个数的情况;同时,该算法可以减小噪声的影响.仿真结果表明了算法的有效性.

Based on the elements of the covariance matrix, this paper presents a multiple targets angle tracking algorithm, in which by updating the elements of the covariance matrix, a set of linear equations is derived through linear approximations. Angle difference can be obtained by solving these equations. Different from the subspace tracking algorithm, this method need not update the signal subspace; the angles estimated at different times are auto associated, so we can omit the process of data association. This method works well when the number of targets is greater than the number of sensors. At the same time, this algorithm can diminish the effect of noise. Simulation results show this algorithm has high tracking performance.