Mitigating the Ground-Based Radar Interference Efficiently Based on an Autoregressive Model With Optimal Order

Abstract

In this letter, we address the interference mitigation problem caused by ground-based space surveillance radars on spaceborne debris monitoring radars. Suppression-based methods often involve high computational complexity, which limits their applicability in scenarios requiring rapid response, such as space debris detection. In contrast, autoregressive (AR) model-based reconstruction offers faster processing but suffers from performance degradation when the model order is selected solely by information criteria. Moreover, AR-based reconstruction in a single direction (either forward or backward) accumulates estimation errors over time, resulting in increasing deviations from the true signal. To overcome these challenges, we propose an interference mitigation method based on an AR model with optimal order, which includes the following key steps: interference detection, bidirectional signal reconstruction with candidate orders, weighted fusion of forward and backward reconstruction results to enhance reconstruction accuracy, and optimal order selection based on the signal-to-interference-plus-noise ratio (SINR) after coherent accumulation. Experimental results demonstrate superior interference mitigation and computational efficiency of the proposed approach.