Sparse Lv's Distribution and Its Application for Radar Low-observable Maneuvering Target Detection

Radar low-observable maneuvering target detection gives a severe challenge for radar signal processing not only because the time-varying Doppler which is difficult to integrate but also the big computational cost in case of large amount of time serials. In this paper, a novel representation, named as sparse Lv's distribution (SLVD), is proposed combining the well energy focus ability for maneuvering target of LVD and sparse representation with fast calculation of SFT. It can represent the maneuvering target in a sparse centroid frequency and chirp rate (SCFCR) domain with sparse coefficients. Simulations with real radar data show that the proposed method can work well for maneuvering target detection in clutter background compared with the filter bank based moving target detection (MTD), fractional Fourier transform (FRFT), and robust SFT (RSFT). It is simple and easy to implement with less computational burden.