Autofocus technique for radar coincidence imaging with model error via iterative maximum a posteriori

Abstract

Radar coincidence imaging (RCI) is a recently developed staring imaging technique based on the wavefront random modulation and temporal–spatial stochastic radiation field. Before coincidence imaging processing, the RCI needs to compute the reference matrix accurately. Unfortunately, model error usually exists, which degrades the imaging performance considerably. In this article, by exploiting the sparse prior of target, the authors propose a sparsity-driven autofocus imaging via iterative maximum a posteriori (SA-MAP) algorithm for the RCI when model error exists. The algorithm performs well via simultaneous sparse imaging, self-calibration and parameters update. Simulation results demonstrate the validity of the proposed algorithm and performance improvement over the existing algorithms.

Keywords

• radar imaging
• iterative methods
• calibration
• stochastic processes
• maximum likelihood estimation
• matrix algebra
• autofocus technique
• radar coincidence imaging
• model error
• RCI
• wavefront random modulation
• temporal–spatial stochastic radiation field
• coincidence imaging processing
• imaging performance
• sparsity-driven autofocus imaging
• simultaneous sparse imaging
• staring imaging technique
• reference matrix
• iterative maximum a posteriori algorithm
• SA-MAP algorithm