双基地MIMO雷达多目标高精度跟踪算法

Multi-target high precision tracking algorithm for bistatic MIMO radar

针对双基地多输入多输出(multiple-input multiple-output,MIMO)雷达自适应非对称联合对角化(adaptive asymmetric joint diagonalization,AAJD)跟踪算法在低信噪比时失效的问题,提出一种双基地MIMO雷达高精度跟踪算法。首先,针对低信噪比时AAJD算法信号子空间扩展问题,利用主成分顺序估计原理求出特征值,根据特征值的大小对导向矢量进行排序,得到更加精确的信号子空间。其次,根据跟踪状态的不同,将多目标分类(multiple signal classification,MUSIC)算法分为两步:第一步全空域大步长扫描,对应跟踪非稳定状态;第二步小空域小步长扫描,对应跟踪稳定状态,空域范围由上一时刻估计角度和运动速度确定,并将峰值搜索过程变为取最大值操作,降低了计算量。算法解决了低信噪比时信号子空间扩展问题,提高了跟踪性能,且采用了性能更高的MUSIC算法,并对其进行改进,降低了计算量。仿真结果证明了算法的有效性。

In order to solve the problem of low tracking performance of the adaptive asymmetric joint diagonalization（AAJD）algorithm which is invalid in low signal to noise ratio（SNR）,a high accuracy tracking algorithm for bistatic multiple-input multiple-output（MIMO）radar is proposed.Firstly,in order to solve the signal subspace expansion problem in the AAJD algorithm at low SNR,the eigenvalues are obtained by using the principal component order estimation principle.The steering vectors are sorted to obtain a more accurate signal subspace according to the size of the eigenvalues.Secondly,the multiple signal classification（MUSIC）algorithm is divided into two steps depending on the tracking status.The first step is to scan the whole airspace with a large step length,corresponding to tracking the unsteady state.The second step is to scan the small-space with a small step length,corresponding to tracking the steady state.The range of airspace is determined by the angle of the previous time and the velocity of the target.The peak search process is changed to find the maximum value operation which further reduces the amount of calculation.The problem of the signal subspace expansion is solved in low SNR,improving the tracking performance.The algorithm uses improved MUSIC which has higher performance and lower calculation.Simulation results are presented to verify the efficiency of the proposed method.