Modififed Adaptive RFT with Sample Covariance Matrix Inversion Recursive Estimation.

Radon-Fourier transform (RFT) is able to effectively overcome the coupling between the range cell migration (RCM) effect and Doppler modulation by searching along range and velocity dimensions jointly for the moving target, which depends on envelope alignment and Doppler phase compensation. However, without effective clutter suppression, clutter would also be intergraded via RFT. Thus, adaptive RFT (ARFT) has been proposed to clutter suppression by introducing an optimal filter weight, which is determined from the clutter's covariance matrix as well as the steering vector. Nevertheless, the ARFT needs to address the difficulty for real implementation, i.e., computational complexity is too high to a large number of pulse samples. It is known that to obtain the inversion the sample covariance matrix (bR-1 cn) is order M3, i.e., O(M3), which is the most complexity consumed step in ARFT. In this paper, we propose a modified adaptive RFT (MARFT) method to obtainbR-1 cn with recursive calculation, which takes the complexity orderM2, i.e., O(M2). Simulations show that the proposed method has the same clutter suppression ability as the conventional ARFT method, while the computational complexity is much lower. [ABSTRACT FROM AUTHOR]