Robust detection and motion parameter estimation for weak maneuvering target in the alpha-stable noise environment

This paper focuses on the weak maneuvering target detection problem in the alpha-stable noise (ASN) environment. A novel coherent integration framework is proposed, where the range migration (RM) is first removed via approximate linear methods to make the noise distribution character remain unchanged, and then the ASN suppression, Doppler frequency migration (DFM) compensation, and coherent integration are simultaneously achieved in the proper transform domain. Based on this framework, a robust detection method is developed. First, the second-order keystone transform (SoKT) is employed to correct the range curve (RC) induced by the target's acceleration. Thereafter, the centroid axis rotation (CAR) is presented to remove the residual range walk (RW) by rotating the slow time axis parallel to the RW line in the fast-time and slow-time domain. Finally, the phase fractional lower-order Lv's distribution (PFLOLVD) is proposed to compensate the DFM and realize the energy accumulation of the target in the ASN environment. Furthermore, the performance of proposed algorithm in aspects of coherent integration time, computational complexity, and multiple targets detection are analyzed in detail. Compared with the existing coherent integration detection methods, the proposed method is both robust for ASN environments and superior in the detection performance. (C) 2020 Elsevier Inc. All rights reserved.