Weak target detection based on whole-scale Hurst exponent of autoregressive spectrum in sea clutter background

Abstract

Autoregressive (AR) spectrum has the advantage of high frequency resolution over the Fourier spectrum in sea clutter analysis. In recent years, the multi-scale Hurst exponents are widely used in describing the AR spectrum of sea clutter due to their abundant clues about the local roughness of the sea clutter in different scale intervals. In this paper, a method based on whole-scale Hurst exponent of AR spectrum is proposed for weak target detection in the sea clutter background. The measured X- and S-band data sets are used to analyze the multi-scale Hurst exponent of AR spectrum and the results show that the difference degree based on the multi-scale Hurst exponent between sea clutter and targets varies with the scale intervals and the data band types. Then, the whole-scale Hurst exponent maximizes the difference degree by considering each scale of the multi-scale Hurst in the different data sets and thus for the convenience of weak target detection. Compared to the existing fractal methods and the traditional CFAR method, the proposed target detection method obtains a better detection performance in low SCR condition.