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Fast and Refined Processing of Radar Maneuvering Target Based on Hierarchical Detection via Sparse Fractional Representation

Reliable and fast detection of maneuvering target in complex background is important for both civilian and military applications. It is rather difficult due to the complex motion resulting in energy spread in time and frequency domain. Also, high detection performance and computational efficiency are difficult to balance in case of more pulses. In this paper, we propose a fast and refined processing method of radar maneuvering target based on hierarchical detection, utilizing the advantages of moving target detection (MTD), and the proposed sparse fractional representation. The method adopts two-stage threshold processing. The first stage is the coarse detection processing screening out the rangebins with possible moving targets. The second stage is called the refined processing, which uses robust sparse fractional Fourier transform (RSFRFT) or robust sparse fractional ambiguity function (RSFRAF) dealing with high-order motions, i.e., accelerated or jerk motion. And the second stage is carried out only within the rangebins after the first stage. Therefore, the amount of calculation can be greatly reduced while ensuring high detection performance. Finally, real radar experiment of UAV target detection is carried out for verification of the proposed method, which shows better performance than the traditional MTD method, and the FRFT-FRAF hierarchical coherent integration detection with less computational burden.