海杂波AR谱扩展分形特性及微弱目标检测方法

发布时间：2019-01-10 07:55

【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.
[Abstract]:In order to overcome the shortcoming of spectrum Fourier analysis, the power spectrum of sea clutter is calculated by using the method of autoregressive spectrum estimation. The autoregressive model is a linear predictive model which estimates the power spectrum by the autocorrelation function matrix of the sequence and has a more accurate spectral resolution. This paper mainly analyzes the extended fractal characteristics of sea clutter power spectrum based on autoregressive spectrum estimation and its application in weak target detection. Firstly, the fractional Brownian motion model is taken as an example to prove that the power spectrum is self-similar. Secondly, based on the measured sea clutter data of X-band radar, the multiscale Hurst exponent and its optimal scale range of sea clutter autoregressive spectrum are analyzed. Finally, a target detection method based on autoregressive multiscale Hurst exponents is proposed. The experimental results show that this detection method has the ability of weak target detection in sea clutter background. Compared with the existing target detection methods based on extended fractal and traditional CFAR detection methods, the proposed algorithm has better detection performance in the case of low signal-to-clutter ratio.
【作者单位】：西安电子科技大学雷达信号处理国家重点实验室;
【基金】：国家重大科学仪器设备开发专项基金资助项目(2013YQ20060705)
【分类号】：TN957.52

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