Weak Target Detection Based on Joint Fractal Characteristics of Autoregressive Spectrum in Sea Clutter Background

To overcome the shortcomings of fractal analysis in the time domain and Fourier transform domain, this letter mainly studies the joint fractal property of sea clutter of autoregressive (AR) spectrum and its application on weak target detection. Since the box-counting dimension is the most popular parameter to describe a fractal set and simply to calculate, we combined the box-counting dimension with AR spectrum estimate theory, which considers the correlation property of sea clutter series. Moreover, the intercept is regarded as an auxiliary feature for target detection. Then the box-counting dimension and intercept are used as a 2-D feature to analyze the joint fractal characteristic of AR spectrum, and a novel weak target detection algorithm is proposed based on the joint fractal characteristic of AR spectrum. In fact, radar target detection can be regarded as a binary-classification question, and the support vector machine (SVM) is applied to target detection. Finally, real S-band sea clutter data sets are analyzed. Compared to the traditional CFAR method and existing fractal methods, the proposed method improves the detection performance without complex computations.