Novel Fast Coherent Detection Algorithm for Radar Maneuvering Target With Jerk Motion

Abstract

The detection performance of radar maneuvering target with jerk motion is affected by the range migration (RM) and Doppler frequency migration (DFM). To address these problems, a fast algorithm without searching target's motion parameters is proposed. In this algorithm, the second-order keystone transform is first applied to eliminate the quadratic coupling between the range frequency and slow time. Then, by employing a new defined symmetric autocorrelation function, scaled Fourier transform, and inverse fast Fourier transform, the target's initial range and velocity are estimated. With these two estimates, the azimuth echoes along the target's trajectory, which can be modeled as a cubic phase signal (CPS), are extracted. Thereafter, the target's radial acceleration and jerk are estimated by approaches for parameters estimation of the CPS. Finally, by constructing a compensation function, the RM and DFM are compensated simultaneously, followed by the coherent integration and target detection. Comparisons with other representative algorithms in computational cost, motion parameter estimation performance, and detection ability indicate that the proposed algorithm can achieve a good balance between the computational cost and detection ability. The simulation and raw data processing results demonstrate the effectiveness of the proposed algorithm.