Nulling Widen and Deepening Algorithm using a Modified Correlation Subtraction Algorithm Multistage Wiener Filtering

The calculation of beamforming weights takes time due to the constantly changing direction of interference in highly dynamic environments. The traditional anti-jamming means under static or low dynamic are almost all invalid, so the nulling widen algorithm is studied. However, the commonly used cavitation widening and deepening algorithms are often accompanied by a large amount of computational complexity, which may lead to computational inefficiency and slow processing speed in practical applications. In order to solve this problem, a nulling widen and deepening algorithm using a modified correlation subtraction algorithm multistage Wiener filtering is proposed. The algorithm achieves the deepening after nulling widening by constructing a new covariance matrix, and then reduces the rank by truncating the multilevel Wiener filter at the r-level. It finds the blocking matrix with the normalized reference vectors instead of calculating the blocking matrix directly so that the normalized reference vectors are orthogonal to each other, and finally completes the interference suppression by using the power inversion algorithm to improve the performance and reduce the amount of computation. The computational complexity of the algorithm based on the modified correlation subtraction algorithm multistage Wiener filtering (MCSA-MWF) isO(rM2), which is greatly reduced compared to the computational complexity of the traditional null-spread class algorithm with direct inversion O(M3).