本文選題：衛(wèi)星識(shí)別　切入點(diǎn)：局部構(gòu)件　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：目前,針對(duì)以衛(wèi)星為主的重要太空資源與設(shè)施的攻擊、保護(hù)和在軌服務(wù)已成為世界各國航天技術(shù)的重要發(fā)展方向,而衛(wèi)星識(shí)別技術(shù)是其中的關(guān)鍵環(huán)節(jié)。而飛行器抵近技術(shù)的日益成熟,使得利用天基平臺(tái)對(duì)太空目標(biāo)進(jìn)行高分辨率光學(xué)成像成為可能,這對(duì)衛(wèi)星識(shí)別特別是局部構(gòu)件的精確識(shí)別也提出了更新更高的需求。然而,盡管國內(nèi)外在天基目標(biāo)探測識(shí)別方面的研究較多,但多集中在遠(yuǎn)距離情況下的點(diǎn)目標(biāo)探測及在軌運(yùn)動(dòng)狀態(tài)的識(shí)別方面,針對(duì)衛(wèi)星本體、太陽帆板等重要構(gòu)件的識(shí)別方面的研究罕有報(bào)道。本論文針對(duì)該問題,開展典型衛(wèi)星局部構(gòu)件光學(xué)特性及其聚類特征與參數(shù)化表征方法研究,在此基礎(chǔ)上研究復(fù)雜太空環(huán)境下的目標(biāo)局部構(gòu)件識(shí)別方法與算法,并開展實(shí)驗(yàn)驗(yàn)證。主要研究工作與成果如下:(1)建立衛(wèi)星典型局部構(gòu)件的參數(shù)化表征方法與聚類模型。針對(duì)不同類型的典型衛(wèi)星,分析了衛(wèi)星局部構(gòu)件的幾何結(jié)構(gòu)與光學(xué)特性,在此基礎(chǔ)上提取了用于辨識(shí)衛(wèi)星本體、太陽帆板等局部構(gòu)件的聚類特征,并研究了這些特征的參數(shù)化表征方法,同時(shí)結(jié)合衛(wèi)星先驗(yàn)知識(shí),建立了本體、太陽帆板、天線構(gòu)件的聚類參數(shù)集與聚類模型。利用該模型可實(shí)現(xiàn)局部構(gòu)件特征的分類與識(shí)別,并支持后續(xù)目標(biāo)識(shí)別方法與算法研究;(2)提出衛(wèi)星典型局部構(gòu)件的識(shí)別方法與算法。結(jié)合典型衛(wèi)星先驗(yàn)知識(shí)與構(gòu)件聚類模型,綜合考慮衛(wèi)星在在軌運(yùn)行中成像系統(tǒng)離焦、電子學(xué)噪聲、平臺(tái)抖動(dòng)等導(dǎo)致的圖像模糊、噪聲、對(duì)比度下降等像質(zhì)退化問題,以及太空目標(biāo)與天基平臺(tái)之間復(fù)雜相對(duì)運(yùn)動(dòng)關(guān)系導(dǎo)致的目標(biāo)幾何變形以及遮擋等問題,提出了基于單幀與多幀序列圖像的衛(wèi)星局部構(gòu)件識(shí)別方法及算法流程和策略,從而提出了基于單幀與多幀序列圖像的衛(wèi)星局部構(gòu)件識(shí)別算法;(3)算法實(shí)驗(yàn)驗(yàn)證及適用性分析。結(jié)合數(shù)字仿真和地面半物理實(shí)驗(yàn),獲取不同像質(zhì)退化程度及太空目標(biāo)與天基平臺(tái)間不同相對(duì)位姿關(guān)系導(dǎo)致局部構(gòu)件自遮擋與互遮擋以及幾何變形條件的序列幀圖像數(shù)據(jù)。利用該數(shù)據(jù),驗(yàn)證了基于單幀和多幀序列圖像的目標(biāo)聚類與識(shí)別算法的準(zhǔn)確性與適用性。
[Abstract]:At present, protection and in-orbit services have become an important development direction of space technology around the world in response to attacks on important space resources and facilities dominated by satellites. The technology of satellite identification is one of the key links, and the increasingly mature technology of aircraft approach makes it possible to use space-based platform for high-resolution optical imaging of space targets. This also puts forward a higher demand for satellite recognition, especially for the precise identification of local components. However, although there are many researches on space-based target detection and recognition at home and abroad, However, most of them are focused on the detection of point targets and the recognition of the state of motion in orbit at a long distance. There are few reports on the identification of important components such as satellite body, solar canvas and so on. The optical characteristics, clustering characteristics and parameterized characterization of typical satellite local components are studied. Based on this, the methods and algorithms of target local component identification in complex space environment are studied. The main research work and results are as follows: 1) the parameterized characterization method and clustering model of typical local satellite components are established. The geometrical structure and optical properties of local satellite components are analyzed for different types of typical satellites. On this basis, the clustering features used to identify local components such as satellite body, solar canvas and so on are extracted, and the parameterized representation method of these features is studied. At the same time, combined with the prior knowledge of satellite, the ontology and solar canvas are established. The clustering parameter set and clustering model of antenna components can be used to classify and recognize local component features. The method and algorithm of satellite typical local component recognition are proposed. Combined with the prior knowledge of typical satellite and component clustering model, the defocusing of imaging system in orbit is considered synthetically. The image quality degradation problems such as electronic noise, platform jitter, noise, contrast decline, and the geometric distortion and occlusion caused by the complex relative motion relationship between the space target and the space-based platform are discussed. This paper presents a method of local satellite component recognition based on single-frame and multi-frame sequence images, as well as its algorithm flow and strategy. The experimental verification and applicability analysis of satellite local component recognition algorithm based on single-frame and multi-frame sequence images are presented, which are combined with digital simulation and ground semi-physical experiments. The serial frame image data with different degradation degree of image quality and different position and pose relationship between space target and space-based platform resulting in local component self-occlusion and mutual occlusion and geometric deformation condition are obtained. The accuracy and applicability of the target clustering and recognition algorithm based on single frame and multi frame sequence images are verified.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：V52;TP391.41

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