ERA-Net: End-to-End Recognition-Aware Sparse SAR Imaging Network

High-resolution synthetic aperture radar (SAR) imaging is crucial for effective target recognition. Current sparse aperture SAR imaging algorithm based on compressed sensing (CS) often lack constraints related to recognition performance, leading to the loss of key features beneficial for recognition. This results in lower recognition rates in the resulting images. This paper proposes a sparse SAR imaging method driven by target recognition tasks, and constructs an end-to-end recognition-aware sparse SAR imaging network (ERA-Net) from the perspective of SAR imaging interpretation integration. It not only effectively improves the accuracy of downstream recognition tasks, but also reconstructs higher quality SAR images that are conducive to recognition.