本文選題：高頻地波雷達(dá) + 目標(biāo)檢測��； 參考：《哈爾濱工業(yè)大學(xué)》2014年碩士論文

【摘要】：高頻地波超視距雷達(dá)（High Frequency Surface Wave Radar, HFSWR）利用高頻電磁波在海面繞射原理，有效的解決了傳統(tǒng)微波雷達(dá)的隱身技術(shù)所帶來的檢測性能損失，實現(xiàn)了大范圍、全天候遠(yuǎn)程海態(tài)監(jiān)測和關(guān)注于艦船目標(biāo)檢測的海事監(jiān)控。由于大量的外部噪聲和多種類型的雜波占據(jù)了部分HFSWR檢測背景，使其表現(xiàn)出時變、多源、起伏的復(fù)雜特性，僅應(yīng)用經(jīng)典的恒定虛警概率（Constant False Alarm Rate，CFAR）算法易于導(dǎo)致較高的虛警和較差的檢測性能，使得HFSWR艦船目標(biāo)檢測成為一個難點問題。 為有效地改善多目標(biāo)共存環(huán)境下的艦船目標(biāo)的檢測性能，本文研究了一種基于背景識別分類的自適應(yīng)多策略目標(biāo)檢測方法。具體包括檢測背景分析、背景分割處理、背景數(shù)據(jù)指數(shù)歸一化等部分，主要研究內(nèi)容如下： 1，深入研究HFSWR檢測環(huán)境，介紹其不均勻，多雜波的復(fù)雜特性，并且著重分析了幾種主要雜波類型的產(chǎn)生原理和物理特征。為后文基于雜波類型的背景分割做理論鋪墊，得出了進(jìn)行背景感知和信息提取的必要性結(jié)論。 2，針對檢測背景的多源、不均勻的復(fù)雜特征，對檢測背景做分割處理：利用K-L散度原理，將檢測背景分割為均勻部分和分均勻部分；按照雜波類型分割雷達(dá)檢測背景為四個雜波區(qū)域，基于統(tǒng)計理論分析每一個分割區(qū)域，得出各分割區(qū)數(shù)據(jù)服從不同參數(shù)的威布爾分布的結(jié)論，通過上述研究，實現(xiàn)對檢測背景有用信息和知識的提取，用于目標(biāo)檢測策略的設(shè)計和參數(shù)選擇，以完成利用檢測環(huán)境感知技術(shù)的多策略檢測系統(tǒng)。 3，為進(jìn)一步提高檢測策略的性能，研究HFSWR檢測背景的預(yù)處理技術(shù)，利用曲線回歸理論去除背景中影響數(shù)據(jù)統(tǒng)計和背景區(qū)域分割的大功率點。介紹了背景數(shù)據(jù)的指數(shù)歸一化技術(shù)，把服從威布爾分布的背景數(shù)據(jù)歸一化為服從同一參數(shù)的指數(shù)分布數(shù)據(jù)，實現(xiàn)檢測背景的均勻化處理，有效解決檢測背景數(shù)據(jù)統(tǒng)計差異所帶來的檢測性能損失，增強(qiáng)恒虛警檢測的性能。在以上研究基礎(chǔ)上，提出一種基于預(yù)分割的雙參數(shù)CFAR檢測方法，按照分割結(jié)果分別對各分割區(qū)域進(jìn)行指數(shù)歸一化，并分別進(jìn)行經(jīng)典的CFAR檢測處理，有效的改善了檢測性能。 4，綜合以上研究成果，提出了一種針對于HFSWR復(fù)雜檢測背景的多策略目標(biāo)檢測策略，，介紹該策略的詳細(xì)算法流程。將背景分割技術(shù)、大功率目標(biāo)去除技術(shù)、背景數(shù)據(jù)指數(shù)歸一化技術(shù)綜合起來，運用到檢測策略中。并使用半仿真半實測數(shù)據(jù)進(jìn)行檢測性能分析，實驗表明，與其他幾種廣泛應(yīng)用的檢測算法相比，該多策略目標(biāo)檢測方法的弱目標(biāo)發(fā)現(xiàn)能力更強(qiáng)。 本文提出的復(fù)雜雜波背景下的多策略目標(biāo)檢測方法，有效地利用背景在線感知信息，在多源雜波背景下對弱目標(biāo)檢測具有很好的性能，為未來雷達(dá)智能處理方面的研究提供了參考。
[Abstract]:The high Frequency Surface Wave Radar, HFSWR) of high frequency ground wave over-the-horizon radar utilizes the high frequency electromagnetic wave diffraction principle in the sea surface, effectively solves the detection performance loss caused by the traditional microwave radar stealth technology, and realizes a wide range. All-weather remote sea monitoring and marine monitoring focused on ship target detection. Because a large number of external noise and various types of clutter occupy part of the background of HFSWR detection, it shows the complex characteristics of time-varying, multi-source and undulation. Only using the classical constant false alarm probability constant False Alarm algorithm can easily lead to high false alarm and poor detection performance, which makes HFSWR ship target detection become a difficult problem. In order to effectively improve the performance of ship target detection in multi-target coexisting environment, an adaptive multi-strategy target detection method based on background recognition and classification is studied in this paper. It includes detection background analysis, background segmentation processing, background data index normalization and so on. The main research contents are as follows: The main contents are as follows: 1. The HFSWR detection environment is deeply studied, and the complex characteristics of its heterogeneous and multi-clutter are introduced, and the generation principle and physical characteristics of several main clutter types are emphatically analyzed. The background segmentation based on clutter type is the theoretical foundation, and the necessity of background perception and information extraction is obtained. 2. Aiming at the complex features of multi-source and uneven detection background, the detection background is divided into uniform part and homogeneous part by K-L divergence principle. The radar detection background is divided into four clutter regions according to the clutter type. Each segmentation region is analyzed based on the statistical theory, and the conclusion that the data of each segmentation region is distributed according to different parameters is obtained. It can extract useful information and knowledge from detection background and be used in the design of target detection strategy and parameter selection in order to complete a multi-strategy detection system using detection environment awareness technology. 3. In order to further improve the performance of the detection strategy, the pre-processing technology of HFSWR detection background is studied, and the high-power points which affect the data statistics and background region segmentation in the background are removed by the curve regression theory. This paper introduces the exponential normalization technology of background data, normalizes the background data from Weibull distribution to exponential distribution data from the same parameter, and realizes the uniform processing of the detection background. The loss of detection performance caused by statistical difference of background data is effectively solved, and the performance of CFAR detection is enhanced. On the basis of the above research, a two-parameter CFAR detection method based on pre-segmentation is proposed. According to the segmentation results, the segmentation regions are exponentially normalized, and the classical CFAR detection processing is carried out respectively, which improves the detection performance effectively. 4. Based on the above research results, a multi-strategy target detection strategy for HFSWR complex detection background is proposed, and the detailed algorithm flow of the strategy is introduced. Background segmentation technology, high power target removal technology and background data index normalization technology are integrated and applied to detection strategy. The detection performance is analyzed with semi-simulated semi-measured data. The experimental results show that the multi-strategy target detection method has stronger weak target detection ability than other widely used detection algorithms. The multi-strategy target detection method proposed in this paper can effectively utilize the background on-line sensing information and has good performance for weak target detection in multi-source clutter background. It provides a reference for the research of radar intelligent processing in the future.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN958.93

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