MIMO雷达降维的低运算量波束形成方法

Dimension-reduced and Low Computation Beamforming Method in MIMO Radar

  • 摘要: 多输入多输出(Multiple Input and Multiple Output, MIMO)雷达在运用常规地最小方差无失真响应(Minimum Variance Distortionless Response, MVDR)波束形成器和线性约束最小方差(Linearly Constrained Minimum Variance, LCMV)波束形成器进行接收波束形成的时候,需处理的数据维数比常规雷达要大许多,由此导致了其运算量十分巨大。考虑到MIMO雷达发射的是多个相互正交的波形,所以匹配滤波之后在进行接收波束形成的时候可以将MIMO雷达全维波束形成等效为多个单输入多输出(Single Input and Multiple Output, SIMO)雷达的波束形成合成,由此在进行数据处理的时候降低了数据的维数,减少了估计协方差矩阵需要的快拍数目,大大降低了运算量,并且与已有的降维算法相比不需要对波束形成权矢量进行迭代求解。仿真表明在大量数据快拍数时新方法与全维的接收波束形成性能基本一致,且在低数据快拍数时依然保持良好的性能,同时运算量与全维方法相比大大下降。

     

    Abstract: Compared with conventional radar system, MIMO radar system has huge computation because of the large dimensions of echo data in receiving beamforming using MVDR or LCMV. Considering that the transmitted waveforms of MIMO radar is orthogonal to each other, the full-dimensional MIMO radar can be thought a combination of multiple SIMO radars in receiving beamforming after matched filter. This kind of equivalent not only reduces the dimensions and number of data snapshot, but also cuts down the computation when the covariance matrix and its inverse operation were calculated. In addition, the iterative solution is not required compared with the existing algorithm. Simulation results validates that the new method has a similar performance with the full-dimensional algorithm when the number of data snapshot is large enough, and keeps good performance as the number of data snapshot is fewer, meanwhile, the computation of the new method is greatly decreased compared to the full-dimensional method.

     

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