Super-Resolution Technique of Multi-Radar Fusion 2D Imaging Based on ExCoV Algorithm in Low SNR

Abstract

Limited by the hardware, the bandwidth of the transmitted signal is not wide enough for super resolution; this is the same for cross resolution, which is limited by the observation angle. In this paper, we propose a technique for imaging fusion using 2D-imaging super-resolution by using multi-radar data from different observation locations, and the resultant effective band is proposed. First, a sparse 2D parametric model based on GTD theory is introduced to construct a dictionary by matching the scattering theory of the radar observation target. Then, the multi-radar fusion imaging framework is constructed. Meanwhile, the 2D model’s sparse parameters are obtained in low SNR using an expansion-compression variance-component algorithm. Finally, radar echo data is expanded to realize the fusion imaging process. The simulation results show that the image quality is improved after multi-radar fusion, which is better than that of the single radar echo, verifying the effectiveness of our method.