Multi-static airborne passive SAR imaging using cross-validation-based SOMP algorithm

Abstract

The availability of multiple illuminators in the multi-static airborne passive synthetic radar (SAR) system can provide the improved SAR imaging capability. The reflection coefficients of targets in the observed scene are different due to diverse observation angles and carrier frequencies in multi-static scenarios. To tackle this problem, the study firstly introduces the forward signal model of multi-static airborne passive SAR system and then formulates the imaging problem as an joint sparsity optimisation problem of multiple measurement vectors. Under the distributed greedy sparse recovery framework, the study finally proposes the cross-validation-based simultaneous orthogonal matching pursuit (SOMP) imaging algorithm for multi-static airborne passive SAR system. The proposed imaging algorithm can avoid the requirement of sparsity level of the observed scene and achieve the accurate imaging reconstruction of the observed scene. The simulation results have verified the effectiveness and validity of the proposed imaging method.

Keywords

• synthetic aperture radar
• radar imaging
• iterative methods
• greedy algorithms
• image reconstruction
• passive radar
• airborne radar
• time-frequency analysis
• optimisation
• multistatic airborne passive synthetic radar system
• improved sar imaging capability
• multistatic airborne passive sar system
• cross-validation-based simultaneous orthogonal matching pursuit imaging algorithm
• multistatic airborne passive sar imaging
• cross-validation-based somp algorithm
• multiple illuminator availability
• reflection coefficients
• observation angles
• carrier frequencies
• forward signal model
• joint sparsity optimisation problem
• multiple measurement vectors
• distributed greedy sparse recovery framework
• somp imaging algorithm
• imaging reconstruction