A Novel Range Super-Resolution Algorithm for UAV Swarm Target Based on LFMCW Radar
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We consider the problem of estimating range about unmanned aerial vehicle (UAV) swarm target. The main challenges are small radar cross-section (RCS) and high target density. In this paper, for better accumulation, we focus the original beat signal by Focus-Before-Detect (FBD) and obtain the velocity. Based on the velocity, we form a compensation matrix to eliminate the range migration (RM). Then, a novel range super-resolution algorithm based on the gridless sparse method is implemented that improves the range resolution to a great extent. Experimental results based on simulated and real measured data are carried out to demonstrate the accuracy of the model and the effectiveness of the algorithm.
KeywordsSwarm target detection UAV target detection Gridless sparse methods Range super-resolution Reweighted atomic norm minimization (RAM)
This work was supported by the National Natural Science Foundation of China (grant 61601341 and 61771367), Project Funded by China Postdoctoral Science Foundation (grant 2015M582615 and 2016T90891), Program for the National Science Fund for Distinguished Young Scholars (grant 61525105), National Natural Science Foundation of Shaanxi Province, Key R&D Program–The Key Industry Innovation Chain of Shaanxi (grant 2018JM6060) and the 111 Project (B18039).
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