Weak and High Maneuvering UAV Detection via Long-time Coherent Integration based on KT-BCS-LSM Method

This paper intends to address the long-time coherent integration (LTCI) of high maneuvering unmanned aerial vehicle (UAV) in low SNR environment with the idea of sparse reconstruction. Due to the small radar cross section (RCS), high maneuverability and micro-Doppler (m-D) effect, the robust detection of weak UAV target is a challenging problem. Both of the across range unit (ARU) effect and Doppler frequency migration (DFM) will be introduced in UAV returns due to high maneuverability. Furthermore, m-D effect induced by the rotation of rotor blades will also disturb the LTCI performance. To address these problems, a LTCI method called KT-BCS-LSM is proposed for high maneuvering UAV detection. To mitigate the disturbance of m-D effect, the null space pursuit (NSP) with third-order differential operator is constructed to separate the m-D signal from UAV returns. Then, the keystone transform (KT) is applied for range walk correction. Motion parameters estimation, including velocity, acceleration and jerk, is modeled as a sparse representation problem and solved by Bayesian compressive sensing (BCS) with Laplacian scale mixture (LSM) prior. Compared with the searching-based method, like generalized RFT (GRFT), the proposed method has significant higher computational efficiency. Compared with transform-based methods, like fractional Fourier transform (FrFT) and Lv's distribution (LVD), the proposed algorithm is able to compensate the DFM induced by jerk motion and has higher estimation accuracy and noise robustness. The effectiveness of proposed method is validated by numerical simulations and real-measured data of a DJI UAV.