Study on Fast Algorithm of Covariance Matrix Inverion for STAP
Space- time adaptive processing(STAP) is a crucial technique applied to clutter suppression and target detection for new generation airborne phase - array radar. However, the tremendous computational complexity poses a primary challenge to implement STAP in practical engineering. In order to improve the performance of the STAP real - time implementation with the higher dimension of sample matrix inversion, an improved Strassen matrix inversion algorithm is presented in this paper. The method combines the efficiency of Strasson matrix inversion algorithm and the feature that sample covariance matrix is positive dedinited Hermite matrix . The new algorithm has less computation,smaller storage,and is feasible. The measured data of DSP processor proves the effectiveness and feasibility of the new algorithm, and this algorithm achieves an obvious performance improvement in comparison with the Gauss elimination method and matrix inversion block which is commonly employed in applications.
Covariance matrix inversionSpace - time adaptive processing(STAP)Matrix inversion
NETL
NSTL
万方数据
维普
