High-speed Maneuvering Target Detection Based on Non-searching Estimation of Motion Parameters

A novel algorithm for high-speed maneuvering target detection and parameter estimation is proposed. Firstly, the second-order Keystone Transform (KT) is utilized to remove the quadric coupling between the range frequency and the slow time, after that, the Symmetric Instantaneous Autocorrelation Function (SIAF) is calculated. Secondly, in order to achieve energy accumulation, Scaled Inverse Fourier Transform (SIFT), Scaled FT (SFT), and Fast FT (FFT) are successively performed on the different dimensions of the SIAF to obtain a new parameter space, then peak detection is carried out to achieve the estimation of radial velocity ambiguity integer and radial acceleration. Finally, a compensation function is constructed to compensate the range migration and the Doppler spread, then the KT algorithm is employed to realize target detection and the estimation of targets range and ambiguous radial velocity, with the radial velocity ambiguity integer and ambiguous radial velocity, the unambiguous radial velocity can be calculated. Since the brute-force searching procedure is eliminated, moreover, the SIFT and the SFT can be implemented with the FFT operation, the computational complexity of proposed algorithm is greatly reduced. The simulation results demonstrate the effectiveness of the proposed algorithm.