海杂波AR谱扩展分形特性及微弱目标检测方法

doi:10.3969/j.issn.1001-2400.2017.01.011

摘要/Abstract

摘要：

为克服频谱傅里叶分析的缺点，采用自回归谱估计的方法来计算海杂波的功率谱．自回归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．该文主要分析基于自回归谱估计的海杂波功率谱的扩展分形特性，以及在微弱目标检测中的应用．首先，以分数布朗运动模型为例，证明其功率谱具有自相似性．其次，根据X波段雷达的实测海杂波数据，分析了海杂波自回归谱的多尺度Hurst指数及其最优尺度区间．最后，提出一种基于自回归谱多尺度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．与现有基于扩展分形的目标检测方法和传统恒虚警检测方法相比，该算法在低信杂比情况下具有较好的检测性能．

关键词: 目标检测, 海杂波, 分形, 扩展分形, 自回归谱估计

Abstract:

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.

范一飞;罗丰;李明;胡冲;陈帅霖. 海杂波AR谱扩展分形特性及微弱目标检测方法[J]. 西安电子科技大学学报, 2017, 44(1): 59-64.

FAN Yifei;LUO Feng;LI Ming;HU Chong;CHEN Shuailin. Extended fractal properties of the AR spectrum and its application in weak target detection in sea clutter background[J]. Journal of Xidian University, 2017, 44(1): 59-64.

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