海杂波AR谱多重分形特性及微弱目标检测方法

摘要

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.%该文研究了海杂波功率谱的多重分形特性。为了克服频谱傅里叶分析的缺点，用现代谱估计的方法来计算海杂波的功率谱。AR模型是一个线性预测模型，它通过序列的自相关函数矩阵来估计功率谱，并且具有更精确的频谱分辨率。该文主要分析基于AR谱估计的海杂波功率谱的多重分形特性，以及在微弱目标检测中的应用。首先，以分数布朗运动(FBM)模型为例，证明其功率谱具有多重分形特性。其次，根据 X波段雷达的实测海杂波数据，通过多重去趋势分析法(MF-DFA)验证了海杂波AR谱的多重分形特性。最后，分析了海杂波AR谱的广义Hurst指数以及影响参数，并提出一种基于局部AR谱广义Hurst指数的目标检测方法。实验结果表明，该种检测方法具有海杂波背景下微弱目标检测的能力。与现有的分形检测方法和传统的 CFAR 检测方法对比，该算法在低信杂比情况下具有较好的检测性能。