Parameter estimation for maneuvering targets with complex motion via scaled double-autocorrelation transform

Abstract

In this paper, a novel parameter estimation method is proposed for maneuvering targets with complex motion. In the proposed method, the second-order keystone transform (SOKT) and modified range cell migration correction (RCMC)/integration are jointly applied to overcome the velocity ambiguity and eliminate the envelope migration. Then, since the azimuth echoes of maneuvering targets with complex motion can be modeled as cubic phase (CP) signals after motion compensation, a new transform, namely, scaled double-autocorrelation transform (SCDCT), is defined. This transform can be essentially interpreted as the two-dimensional (2-D) Fourier transform (FT) of a scaled parametric instantaneous double-autocorrelation (PIDAC) function. By employing this derived transform, the estimated chirp rates and derivative of chirp rates of CP signals can be obtained simultaneously without searching operation and thus the computational burden can be reduced significantly. Furthermore, the characteristics of cross terms and anti-noise performance of SCDCT are theoretically analyzed. Compared with three other popular methods, product high-order match phase transform, TC-dechirp Clean and modified discrete chirp Fourier transform, the proposed SCDCT-based method is more computationally efficient and has better estimation performance in low signal-to-noise ratio (SNR) circumstance. Simulation results verify the effectiveness of the proposed SCDCT-based method.