基于分形的海杂波目标检测研究

发布时间：2018-04-03 00:25

本文选题：海杂波　切入点：分形　出处：《南京信息工程大学》2017年硕士论文

【摘要】：海杂波是局部海平面对雷达发射信号的反向散射回波,受到海风、海浪等因素的影响。海杂波的特性可用来分析海洋状态,实现海平面或低空小目标的检测。有些情况下,海面是否存在小目标不能仅仅依靠回波的幅值来判定,利用多种信号处理过程对海杂波数据进行预处理有利于海杂波背景下的目标检测研究。采用多重分形理论研究海杂波的内在物理特性,通过目标信号与海杂波信号的分形差异完成小目标检测是本文的主要研究内容,具体如下:利用DFA( Detrended Fluctuation Analysis)方法分析了海杂波信号的特点,研究了海杂波单尺度分形特性,在计算Hurst指数过程中,根据无标度区间选取对分形参数提取的影响,提出了一种改进无标度区间选取方法,成功提取了更加精确的Hurst指数,有效地区分了目标序列与海杂波序列。为了降低复杂海情对海杂波背景下小目标检测的影响,提高微弱信号检测准确度,采用多重分形去势波动分析法分析了海杂波数据在高尺度条件下的分形特性,将分形尺度q扩展到[-30, 30],对多重分形参数H(q)的估计进行了研究,提出了一种基于高尺度分形差量的海杂波中小目标检测方法。实验结果表明:选择高尺度指数q时,所提方法能够有效的区分动目标与纯海杂波之间的分形差异性,完成目标检测。在分析双树复小波变换的基础上,提出了基于双树复小波变换的分形特征算法,利用双树复小波变换的抗混叠和平移不变性,对非平稳时间序列进行分解,完成信号的多重分形分析。基于尺度指数q与H(q)的曲线关系,采用局部积分方法得到广义Hurst指数。最后将算法运用到实际海杂波数据进行了检测,发现在相同虚警概率的情况下,检测概率有了明显的提高。
[Abstract]:Sea clutter is the backscattering echo of radar emission signal from local sea level, which is influenced by sea wind, sea wave and other factors.The characteristics of sea clutter can be used to analyze the state of the sea and to detect small targets at sea level or at low altitude.In some cases, whether there is a small target on the sea surface can not be judged by the amplitude of the echo, and the preprocessing of sea clutter data by using a variety of signal processing processes is beneficial to the research of target detection in the background of sea clutter.Multifractal theory is used to study the inherent physical characteristics of sea clutter. It is the main content of this paper to complete small target detection by fractal difference between target signal and sea clutter signal.The main contents are as follows: the characteristics of sea clutter signal are analyzed by Detrended Fluctuation Analysis method, and the single-scale fractal characteristics of sea clutter are studied. In the process of calculating Hurst exponent, the influence of selecting scale-free interval on the extraction of fractal parameters is discussed.An improved scale-free interval selection method is proposed to extract more accurate Hurst exponents and effectively distinguish target sequences from sea clutter sequences.In order to reduce the influence of complex sea conditions on the detection of small targets in the background of sea clutter and improve the accuracy of weak signal detection, the fractal characteristics of sea clutter data at high scale are analyzed by multifractal castration analysis.In this paper, the fractal scale Q is extended to [-30,30], and the estimation of multifractal parameters is studied, and a method for small and medium target detection of sea clutter based on high scale fractal difference is proposed.The experimental results show that the proposed method can effectively distinguish the fractal difference between moving target and pure sea clutter and complete target detection.Based on the analysis of bitree complex wavelet transform, a fractal feature algorithm based on bitree complex wavelet transform is proposed. The anti-aliasing and translation invariance of bitree complex wavelet transform are used to decompose the non-stationary time series.Multifractal analysis of signals is completed.The generalized Hurst exponent is obtained by using the local integral method based on the curve relation of scale exponent Q and Hu Q.Finally, the algorithm is applied to the real sea clutter data for detection, and it is found that the detection probability is obviously improved under the same false alarm probability.
【学位授予单位】：南京信息工程大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN957.51

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