Extended fractal properties of the AR spectrum and its application in weak target detection in sea clutter background

This paper mainly studies the extended fractal properties of the power spectrum of the sea clutter. 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 the sea clutter from its autocorrelation matrix and has a higher frequency resolution than Fourier analysis. This paper concentrates on analyzing the extended fractal 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 self-similar property of the power spectrum. Then, the real measured X-band data is used to analyze the multi-scale Hurst exponent of the AR spectrum of the sea clutter and its optimized scale interval. Finally, a novel detection method based on the multi-scale Hurst exponent of the AR spectrum is proposed. The results show that the proposed method is effective for weak target detection in sea clutter background. Compared to the existing extended fractal method and the traditional CFAR method, the proposed method has a better detection performance in the low SCR condition.