【摘要】：海面上小目標(biāo)的雷達(dá)檢測技術(shù)在港口交通、海浪監(jiān)測、海難和空難搜救、軍事偵察等場合中具有廣泛應(yīng)用。對于海面上的小目標(biāo),其雷達(dá)信號通常較弱,這在目標(biāo)檢測中是個困難的任務(wù)。此外,海面雷達(dá)信號是典型的非平穩(wěn)隨機信號,對其進(jìn)行檢測需要選擇合適的特征和檢測器。為了實現(xiàn)海面上弱目標(biāo)檢測,需要在復(fù)雜海雜波背景下,迅速準(zhǔn)確地提取雷達(dá)回波信號中的有用特征信息,并進(jìn)行識別分類。本文研究了海面弱目標(biāo)檢測技術(shù),主要工作如下：(1)針對海雜波的非平穩(wěn)特性,應(yīng)用三參數(shù)的分?jǐn)?shù)階傅里葉變換來處理海雜波信號,使易混淆的主目標(biāo)與次目標(biāo)信號特征差別增大,為后續(xù)目標(biāo)識別奠定基礎(chǔ)。(2)根據(jù)海雜波的非平穩(wěn)特征,用Hurst指數(shù)、Lyapunov指數(shù)、分形維數(shù)、多重分形譜、近似熵等對其進(jìn)行表征,并用遺傳算法優(yōu)化選擇選取了一種新的聯(lián)合特征向量,通過特征互補,使特征向量更好表征海雜波的特性。(3)將深度信念網(wǎng)絡(luò)與隱馬爾科夫模型相結(jié)合作為目標(biāo)分類器,應(yīng)用聯(lián)合特征向量作為模式分類器的輸入進(jìn)行訓(xùn)練；訓(xùn)練完成后,將其用于海雜波信號的分類,取得了良好的實驗結(jié)果。測試數(shù)據(jù)選用加拿大McMaster大學(xué)IPIX雷達(dá)數(shù)據(jù)。仿真實驗結(jié)果表明,本文的海雜波目標(biāo)識別方法檢測準(zhǔn)確度較高,在低信噪比情況下也具有良好的檢測性能。
[Abstract]:Radar detection technology for small targets on the sea surface has been widely used in port traffic, ocean wave monitoring, maritime and air disaster rescue, military reconnaissance and other occasions. For small targets on the sea surface, the radar signal is usually weak, which is a difficult task in target detection. In addition, the sea surface radar signal is a typical nonstationary random signal, so it is necessary to select suitable features and detectors to detect it. In order to detect weak targets on the sea surface, it is necessary to quickly and accurately extract the useful feature information from radar echo signals and classify them under the background of complex sea clutter. The main work of this paper is as follows: (1) aiming at the non-stationary characteristics of sea clutter, a three-parameter fractional Fourier transform is applied to deal with sea clutter signals. The difference between the signal characteristics of the main target and the secondary target is enlarged, which lays a foundation for the subsequent target recognition. (2) according to the non-stationary feature of sea clutter, it is characterized by Hurst exponent, fractal dimension, multifractal spectrum, approximate entropy, etc. A new joint feature vector is selected by genetic algorithm. By feature complementation, the feature vector can better represent the characteristics of sea clutter. (3) the depth belief network and hidden Markov model are combined as target classifiers. The joint eigenvector is used as the input of the pattern classifier for the training. After the training is completed, the joint eigenvector is applied to the classification of sea clutter signals, and good experimental results are obtained. The test data are selected from IPIX radar data of McMaster University, Canada. The simulation results show that the detection accuracy of this method is high and the detection performance is good in the case of low signal-to-noise ratio (SNR).
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TN957.51

【參考文獻(xiàn)】

相關(guān)期刊論文 前7條

1 范一飛;羅豐;李明;胡沖;陳帥霖;;海雜波AR譜多重分形特性及微弱目標(biāo)檢測方法[J];電子與信息學(xué)報;2016年02期

2 行鴻彥;張強;徐偉;;海雜波FRFT域的分形特征分析及小目標(biāo)檢測方法[J];物理學(xué)報;2015年11期

3 田玉芳;尹志盈;姬光榮;鄭海永;;基于SVM的海面弱目標(biāo)檢測[J];中國海洋大學(xué)學(xué)報(自然科學(xué)版);2013年07期

4 劉寧波;王國慶;包中華;關(guān)鍵;;海雜波FRFT譜的多重分形特性與目標(biāo)檢測[J];信號處理;2013年01期

5 牛奉高;劉維奇;;分?jǐn)?shù)布朗運動與Hurst指數(shù)的關(guān)系研究[J];山西大學(xué)學(xué)報(自然科學(xué)版);2010年03期

6 劉寧波;關(guān)鍵;;海雜波的多重分形判定及廣義維數(shù)譜自動提取[J];海軍航空工程學(xué)院學(xué)報;2008年02期

7 李穎新;李建更;阮曉鋼;;腫瘤基因表達(dá)譜分類特征基因選取問題及分析方法研究[J];計算機學(xué)報;2006年02期

，

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