Fast Principal Component Auto-Regressive Algorithm for Estimation of Parameters of Radar Interference Signal
Md Shahnawaz Hussain, Srikanta Pal · 2019
The issue of estimation of parameters of radar interference signal has been addressed by algorithms like Grid Search-Maximum Likelihood Estimator (GS-MLE) and Principal Component Auto-Regressive Estimator (PCAR). GS-MLE algorithm is an optimal algorithm as it achieves the Cramer-Rao Lower Bound (CRLB) but its time-complexity is high. Another algorithm, that is, the PCAR Algorithm is a suboptimal algorithm but it is comparatively faster. In this paper, we propose a hybrid method to further reduce the time-complexity of the PCAR algorithm for large data size (>3000) at both high SNR (>0 dB) and low SNR (<0 dB). However, the estimates of parameters obtained by the Fast PCAR algorithm are reliable only in the SNR range of −20 dB to infinity. This is validated by comparing the Fast PCAR algorithm with the CRLB.