本文選題：海雜波　切入點(diǎn)：分形　出處：《南京信息工程大學(xué)》2017年碩士論文

【摘要】：海雜波是局部海平面對(duì)雷達(dá)發(fā)射信號(hào)的反向散射回波,受到海風(fēng)、海浪等因素的影響。海雜波的特性可用來分析海洋狀態(tài),實(shí)現(xiàn)海平面或低空小目標(biāo)的檢測(cè)。有些情況下,海面是否存在小目標(biāo)不能僅僅依靠回波的幅值來判定,利用多種信號(hào)處理過程對(duì)海雜波數(shù)據(jù)進(jìn)行預(yù)處理有利于海雜波背景下的目標(biāo)檢測(cè)研究。采用多重分形理論研究海雜波的內(nèi)在物理特性,通過目標(biāo)信號(hào)與海雜波信號(hào)的分形差異完成小目標(biāo)檢測(cè)是本文的主要研究?jī)?nèi)容,具體如下:利用DFA( Detrended Fluctuation Analysis)方法分析了海雜波信號(hào)的特點(diǎn),研究了海雜波單尺度分形特性,在計(jì)算Hurst指數(shù)過程中,根據(jù)無標(biāo)度區(qū)間選取對(duì)分形參數(shù)提取的影響,提出了一種改進(jìn)無標(biāo)度區(qū)間選取方法,成功提取了更加精確的Hurst指數(shù),有效地區(qū)分了目標(biāo)序列與海雜波序列。為了降低復(fù)雜海情對(duì)海雜波背景下小目標(biāo)檢測(cè)的影響,提高微弱信號(hào)檢測(cè)準(zhǔn)確度,采用多重分形去勢(shì)波動(dòng)分析法分析了海雜波數(shù)據(jù)在高尺度條件下的分形特性,將分形尺度q擴(kuò)展到[-30, 30],對(duì)多重分形參數(shù)H(q)的估計(jì)進(jìn)行了研究,提出了一種基于高尺度分形差量的海雜波中小目標(biāo)檢測(cè)方法。實(shí)驗(yàn)結(jié)果表明:選擇高尺度指數(shù)q時(shí),所提方法能夠有效的區(qū)分動(dòng)目標(biāo)與純海雜波之間的分形差異性,完成目標(biāo)檢測(cè)。在分析雙樹復(fù)小波變換的基礎(chǔ)上,提出了基于雙樹復(fù)小波變換的分形特征算法,利用雙樹復(fù)小波變換的抗混疊和平移不變性,對(duì)非平穩(wěn)時(shí)間序列進(jìn)行分解,完成信號(hào)的多重分形分析�；诔叨戎笖�(shù)q與H(q)的曲線關(guān)系,采用局部積分方法得到廣義Hurst指數(shù)。最后將算法運(yùn)用到實(shí)際海雜波數(shù)據(jù)進(jìn)行了檢測(cè),發(fā)現(xiàn)在相同虛警概率的情況下,檢測(cè)概率有了明顯的提高。
[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.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN957.51

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