A Novel Parameter Estimation Method Based on Piecewise Nonlinear Amplitude Transform for the LFM Signal in Impulsive Noise
Haiying Wang, Qunying Zhang, Wenhai Cheng, Jiaming Dong, Xiaojun Liu · Electronics · 2023
In a complex electromagnetic environment, any noise present generally exhibits strong impulsive characteristics. The performance of existing parameter estimation methods carried out in Gaussian white noise for the linear frequency modulation (LFM) signal degrades or even fails under impulsive noise. This paper proposes a novel parameter estimation method to address this problem. Firstly, the properties of the piecewise nonlinear amplitude transform (PNAT) are derived. This manuscript verifies that the PNAT can retain phase information of the LFM signal while suppressing the impulsive noise. Subsequently, a new concept known as piecewise nonlinear amplitude transform parametric symmetric instantaneous autocorrelation function (PNAT-PSIAF) is proposed. Based on this concept, a novel method called piecewise nonlinear amplitude transform Lv’s distribution (PNAT-LVD) is proposed to estimate the centroid frequency and chirp rate of the LFM signal. The simulations show that the proposed algorithm can effectively suppress the impulsive noise without prior knowledge of the noise for both the single-component and double-component LFM signal. In addition, two parameters of the LFM signal can be precisely estimated by the proposed method under low generalized signal-to-noise ratios (GSNR). The stronger the impulsive characteristics of the noise, the better the performance of the algorithm.