Noise mitigated compressive sensing for radar applications
Résumé
During decades microwave imaging technology has achieved remarkable progress, and at the same time encountered increasing complexity in system implementation. Recently, the sparse systems, where the compressed sensing (CS) is applied, introduce the sparse signal processing theory to radar imaging to obtain a new system methodology of microwave imaging and facilitate the burden of computing large-scale data. Basically, CS recovery is a kind of sparse regularized optimization, where the regularization parameter λ plays an important role for a stable solution. Although there are a lots of methods to estimate λ, e.g. the L-curve method, the cross-validation method, etc., however, they are still complex and even only work for particular conditions. In this paper, we will introduce a novel approach, named noise mitigated method (NMM), to get a stable result even without a precise estimation of λ. For radar applications we will take the stepped frequency radar (SFR) as an example to present the feasibility of NMM.