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2024-07-29
Modified Adaptive RFT with Sample Covariance Matrix Inversion Recursive Estimation
By
Progress In Electromagnetics Research C, Vol. 145, 181-187, 2024
Abstract
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, the 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 for the moving target with the consideration of RCM effect. 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 (Rcn-1) is order M3, i.e., O(M3), in which $M$ is the order of the matrix. It is the most complexity consumed step in ARFT. In this paper, we propose a modified adaptive RFT (MARFT) method to obtain Rcn-1 with recursive computation, which takes the complexity order M2, i.e., O(M2). Simulations show that the proposed method has the same clutter suppression results as the conventional ARFT method, where the computational complexity is much lower.
Citation
Haibo Wang, Wenhua Huang, Haichuan Zhang, Tao Ba, and Zhiqiang Yang, "Modified Adaptive RFT with Sample Covariance Matrix Inversion Recursive Estimation," Progress In Electromagnetics Research C, Vol. 145, 181-187, 2024.
doi:10.2528/PIERC24051602
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