The multifractal properties of AR spectrum and weak target detection in sea clutter background

This paper focuses on the multifractal properties of sea clutter in power spectrum domain. To overcome the deficiencies of Fourier transform analysis, the power spectrum of the sea clutter is obtained by AutoRegressive (AR) spectrum estimation. The AR model is a linear predictive model, which estimates the power spectrum of sea clutter from its autocorrelation matrix and has a higher frequency resolution than Fourier analysis. This paper concentrates on analyzing the multifractal property of the power spectrum based on AR spectral estimation and its application to weak target detection. Firstly, Fractional Brownian Motion (FBM) is taken as an example to prove the multifractal property of the power spectrum. Then, real measured X-band data is used to verify the multifractal property of the AR spectrum of sea clutter by MultiFractal Detrended Fluctuation Analysis (MF-DFA) method. Finally, the generalized Hurst exponent of AR spectrum and its influence factors are analyzed, and a novel detection method based on local AR generalized Hurst exponent is proposed. The results show that the proposed method is effective for weak target detection in sea clutter background. Compared to the existing fractal method and the traditional CFAR method, the proposed method has a better detection performance in low SCR condition.