Novel motion parameter estimation and coherent integration algorithm for high maneuvering target with jerk motion

• Published in: Signal processing 2024-08, Vol.221, p.109467, Article 109467
• Main Authors: Niu, Zhiyong, Zheng, Jibin, Su, Tao
• Format: Article
• Language: English
• Subjects: Doppler frequency migration; Long time coherent integration; Parameter estimation; Radar maneuvering target detection; Symmetric autocorrelation function
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2024.109467

•A fourth-order polynomial phase signal model is used to model the echo, and a novel parameter estimation method is proposed.•A frequency domain implementation method of SoWVD is proposed, which reduce the computational cost of SoWVD significantly.•For the fourth order polynomial phase signal model, the proposed algorithm achieves a balance between the accumulation performance and the operational complexity. The high maneuvering target with jerk motion detection suffers from range migration (RM) and Doppler frequency migration (DFM). In this paper, the fourth order polynomial signal model is used to model the radar echo signal, and a novel high maneuvering target detection method is proposed. The RM is eliminated with Keystone transform (KT) and acceleration searching, and then the aligned echoes are extracted. With two defined symmetric autocorrelation functions (SAF) and the scaled Fourier transform, the third and fourth order motion parameters are estimated. Based on the estimated high order motion parameters, the fourth order polynomial phase signal is turned into linear frequency modulated (LFM) signal. To estimate the chirp rate of the LFM signal, a fast implementation method of second order Wigner Ville Distribution (SoWVD), named frequency domain SoWVD (FDSoWVD) is proposed. Based on the triple estimated motion parameters, the DFM is compensated and then the coherent integration can be achieved by fast Fourier transform, followed which the detection process is performed. Compared with other representative algorithms, the proposed method achieves a good balance between the computation complexity and detection ability. The effectiveness of the proposed method is verified by experiment with synthetic and raw data.