本文選題：雜波建模 + 多參數(shù)聯(lián)合二進(jìn)制檢測�。� 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：面對日益復(fù)雜的海洋環(huán)境,以及以海洋環(huán)境為掩護(hù),不斷優(yōu)化升級的武器技術(shù)嚴(yán)重威脅著沿海地區(qū)的安全,同時也意味著對現(xiàn)代雷達(dá)探測性能的要求越來越高,海洋環(huán)境下的小目標(biāo)檢測作為現(xiàn)代雷達(dá)技術(shù)研究中的熱點(diǎn)與難點(diǎn)持續(xù)吸引著大家的注意力。本文針對海面艦船小目標(biāo)檢測技術(shù),進(jìn)行了專題研究。提出了一種基于M/N檢測器思路的多參數(shù)聯(lián)合二進(jìn)制檢測方法,利用實(shí)測數(shù)據(jù)對該方法的有效性作驗證。論文具體工作可分為三部分:第一:海雜波建模與統(tǒng)計特性分析,文中采用瑞利、威布爾、對數(shù)正態(tài)、K分布、Gamma分布、廣義Gamma分布6種模型對雜波進(jìn)行擬合分析,除K分布外,廣義Gamma分布在特定條件下都可以退化為其他4種分布,廣義Gamma模型作為三參數(shù)模型,能更好地適應(yīng)高分辨率雷達(dá)海面回波。利用高階矩估計和極大似然估計方法對6種雜波模型進(jìn)行參數(shù)估計,使用卡方檢驗和K-S檢驗2種擬合優(yōu)度方法對6種分布的擬合程度進(jìn)行評估,確定最優(yōu)擬合模型。經(jīng)實(shí)測數(shù)據(jù)分析表明,統(tǒng)計過程中提取的模型形狀參數(shù)和雜波序列的去相關(guān)時間能夠有效區(qū)分純雜波單元與目標(biāo)單元。第二:多參數(shù)聯(lián)合二進(jìn)制檢測,針對單參數(shù)檢測的局限性,根據(jù)M/N檢測器思路,本文提出了一種多參數(shù)聯(lián)合二進(jìn)制檢測算法,在文中具體指雙門限檢測。第一門限采用單元平均恒虛警檢測算法,根據(jù)雜波建模結(jié)果選擇合適的檢測器。對通過第一門限的距離單元重新編號進(jìn)行第二門限檢測,第二門限使用的多參數(shù)包括分形參數(shù)Hurst指數(shù)、遞歸參量、統(tǒng)計特征參數(shù)等。對各參數(shù)設(shè)置單門限,統(tǒng)計檢測結(jié)果,設(shè)置二進(jìn)制檢測門限做最后判決。利用IPIX雷達(dá)數(shù)據(jù)對所用參數(shù)有效性進(jìn)行初步驗證,受限于IPIX雷達(dá)數(shù)據(jù)距離單位的數(shù)目,在使用恒虛警檢測時易造成較大的恒虛警損失,因此采用本單位雷達(dá)系統(tǒng)在外場試驗收集的X波段數(shù)據(jù)對多參數(shù)聯(lián)合二進(jìn)制算法進(jìn)行驗證,證實(shí)了該方法對海面小目標(biāo)檢測的有效性。第三:基于動態(tài)規(guī)劃的檢測前跟蹤,利用目標(biāo)在多幀數(shù)據(jù)間相關(guān)性強(qiáng)于噪聲背景,數(shù)據(jù)積累時目標(biāo)能量大于噪聲背景的特點(diǎn)實(shí)現(xiàn)弱目標(biāo)的檢測。根據(jù)指標(biāo)函數(shù)確定檢測結(jié)果,同時利用檢測結(jié)果回溯航跡。通過仿真和半實(shí)測仿真對該方法的有效性進(jìn)行驗證。
[Abstract]:In the face of the increasingly complex marine environment and the continuous optimization and upgrading of weapons technology, which is sheltered by the marine environment, it is a serious threat to the security of coastal areas, and it also means that the performance of modern radar detection is becoming more and more demanding, As a hot and difficult point in the research of modern radar technology, small target detection in marine environment continues to attract people's attention. In this paper, a special research on small target detection technology of sea surface ships is carried out. A multiparameter combined binary detection method based on M- N detector is proposed. The validity of the method is verified by the measured data. The specific work of this paper can be divided into three parts: first, sea clutter modeling and statistical characteristic analysis. In this paper, Rayleigh, Weibull, logarithmic normal K distribution Gamma distribution and generalized Gamma distribution are used to fit and analyze clutter, except K distribution. The generalized Gamma distribution can degenerate into the other four distributions under certain conditions. As a three-parameter model, the generalized Gamma model can better adapt to the high-resolution radar sea surface echo. High-order moment estimation and maximum likelihood estimation are used to estimate the parameters of six clutter models. The fitting degree of the six distributions is evaluated by chi-square test and K-S test, and the optimal fitting model is determined. The analysis of the measured data shows that the model shape parameters extracted in the statistical process and the decorrelation time of the clutter sequence can effectively distinguish the pure clutter unit from the target unit. Second, multi-parameter joint binary detection, aiming at the limitation of single-parameter detection, according to the idea of M / N detector, a multi-parameter joint binary detection algorithm is proposed in this paper, in which double threshold detection is specified. The first threshold adopts the unit average CFAR detection algorithm and selects the appropriate detector according to the clutter modeling results. The second threshold is detected by renumbering the distance unit through the first threshold. The multiparameter used in the second threshold includes the fractal parameter Hurst exponent recursive parameter statistical characteristic parameter and so on. Set a single threshold for each parameter, statistical test results, set the binary detection threshold to make the final decision. Using IPIX radar data to verify the validity of the parameters used, limited by the number of IPIX radar data range units, CFAR detection is easy to cause a large CFAR loss. Therefore, the X-band data collected by the unit radar system in the field experiments are used to verify the multi-parameter combined binary algorithm, and the effectiveness of the method for small target detection on the sea surface is verified. Third, based on dynamic programming, the detection of weak targets is realized by using the feature that the correlation between multi-frame data is stronger than that of noise background, and the energy of target is larger than the noise background when the data is accumulated. The detection results are determined according to the index function, and the track is backtracked by the detection results at the same time. The effectiveness of the method is verified by simulation and semi-test simulation.
【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】：2017
【分類號】：E925;TN957.52
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本文編號：2002641