Backtracking Dichotomous -Least Squares-Based Sidelobe Suppression Algorithm for Multiple Targets with Straddling

The performance of sidelobe suppression methods based on on-grid signal models deteriorates significantly when target parameters deviate from the predefined discrete parameter grids. To address this issue, we propose a backtracking dichotomous-least squares (BD-LS) algorithm based on the minimum residual energy criterion. BD-LS iteratively estimates the parameters of targets whose powers are higher than the threshold used for stopping the iteration, in the descending order of the target powers. During each iteration, the coarse position of the strongest target is first estimated based on the two-dimensional matched filtering (2-D MF) results of the residual signal, and then the straddling offset of the target is estimated according to the minimum residual energy criterion and the dichotomous iterative strategy. After the straddling offsets of all targets are accurately estimated, the complex amplitudes of targets are simultaneously estimated by using the LS algorithm. To mitigate the impact of target interference on the estimation of straddling offsets, multiple backtracking refinements are adopted. In each backtracking refinement processing, the straddling offsets of all the targets above a given threshold are sequentially refined by using the obtained straddling offset estimates of targets as prior information. Simulation results show that BD-LS can effectively suppress the sidelobes of targets with range-Doppler-straddling, and accurately estimate the straddling offsets and powers of targets. Compared to the straddling-robust iterative adaptive filtering (SR-IAF) algorithm, BD-LS provides better sidelobe suppression performance, more accurate estimation of the straddling offsets and powers of targets with lower computational complexity in multi-target scenarios. The effectiveness of the proposed method is demonstrated by both simulated and real data