A Low-computation Matrix Inverse Method based on RDR Recursion and Iterative Extended Inverse

In space-time joint adaptive anti-jamming implementation, such as MVDR, LCMV, etc., the inversion of the covariance matrix is a critical step and the computation load is large. In this paper, we propose a low-computation algorithm for inverse of large-scale matrix: RDR Recursion based on Power and Iterative Extended Inverse Algorithm (PRDR-IEIA). PRDR-IEIA firstly determines the dimension M of the submatrix X<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</inf> based on the input covariance matrix $X(N \times N)$, then the inverse of the submatrix $\mathrm{X}_{M}^{-1}$ is calculated by the RDR block recursive algorithm and finally $X_{M}^{-1}$ is iterated to solve the complete inverse of $\mathbf{X}$. The reduction in computational load of PRDR-IEIA is significant.