Pulse-order recursive method for inverse covariance matrix computation applied to space-time adaptive processing
The conventional space-time adaptive processing (STAP) method such as the typical sample matrix inversion (SMI)-based STAP method is difficult to implement for a practical system because intense computational complexity arises in calculating the inversion of a space-time covariance matrix directly. According to the block Hermitian matrix property of space-time covariance matrix, a new pulse-order recursive method is proposed in this paper to calculate the inverse covariance matrix for the STAP adaptive weight, which can reduce the computational complexity significantly. The proposed method requires initially calculating the inverse covariance matrix of the first pulse-order recursively based on the block Hermitian matrix property. In the following, the inversion of space-time covariance matrix is obtained recursively based on the previous pulse-order inverse covariance matrix. Next, the STAP adaptive weight is calculated based on the inversion space-time covariance matrix previously obtained. Compared with the conventional SMI-based STAP algorithms, the computational complexity of the proposed method is reduced to more than 50% for the same clutter suppression performance. This method can be applied to practical systems benefiting from small computational complexity and stable clutter suppression performance.
recursive method inverse covariance matrix space-time adaptive processing computational complexity clutter suppression