本文關(guān)鍵詞：基于支撐向量機(jī)的衛(wèi)星姿控系統(tǒng)異變特征提取　出處：《電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

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【摘要】：目前,當(dāng)今迅猛發(fā)展的科學(xué)技術(shù)中的尖端技術(shù)之一,航天技術(shù)正以最快的速度在越來越廣的范圍內(nèi),包括在政治、經(jīng)濟(jì)、軍事、生活以及科學(xué)技術(shù)等眾多的領(lǐng)域內(nèi)發(fā)揮著非常卓越的作用。與此同時(shí),航天的快速發(fā)展也使得各類航天器內(nèi)部結(jié)構(gòu)以及所需要的功能變的日益復(fù)雜,對于價(jià)格高昂的一些航天器,系統(tǒng)具有很高可靠性是整個(gè)航天器系統(tǒng)的最基本的需求,衛(wèi)星系統(tǒng)是航天系統(tǒng)中很重要的一部分,一旦衛(wèi)星的某個(gè)部件發(fā)生故障,輕的會使整顆衛(wèi)星的提前設(shè)定的功能達(dá)不到預(yù)期目的或者直接喪失,非常嚴(yán)重的情況下,甚至可能導(dǎo)致發(fā)生一些嚴(yán)重災(zāi)難性的事件,還有這將會浪費(fèi)國家巨大的財(cái)產(chǎn)。直到目前為止,對衛(wèi)星的高可靠性的保證通常是通過確保軟硬件的高的可靠性以及冗余來實(shí)現(xiàn)的。本文主要對衛(wèi)星的下傳狀態(tài)數(shù)據(jù)進(jìn)行了分析,提取出可以表征衛(wèi)星當(dāng)前狀態(tài)的信息,判斷衛(wèi)星的運(yùn)行狀態(tài)。本文闡述了衛(wèi)星姿控系統(tǒng)的主要組成部分,在動(dòng)力學(xué)以及運(yùn)動(dòng)學(xué)兩個(gè)方面分別對模型進(jìn)行建模,并采用了四元數(shù)與歐拉角兩種并行的方式對姿態(tài)信息進(jìn)行詳細(xì)的描述,為后續(xù)的算法的研究提供了基礎(chǔ)。然后,采用了經(jīng)驗(yàn)?zāi)B(tài)分解法提取信號的特征值并利用信號的能量熵進(jìn)行閾值設(shè)定判斷是否有異常發(fā)生,接下來,介紹了支撐向量機(jī)(SVM)的主要原理以及如何對兩個(gè)影響比較明顯的參數(shù)采取優(yōu)化措施,提出了一種新的非線性的方式對參數(shù)進(jìn)行優(yōu)化,與現(xiàn)有的優(yōu)化算法進(jìn)行比較,它加快了參數(shù)的收斂速度,有效的降低了適應(yīng)度值。運(yùn)用SVM提出了兩種方法提取衛(wèi)星狀態(tài)信息,第一是對衛(wèi)星的狀態(tài)進(jìn)行建模,獲取與實(shí)際觀測值之間的殘差信息,進(jìn)行狀態(tài)信息提取,并針對復(fù)雜數(shù)據(jù)樣本情況,提出了一種優(yōu)化的SVM回歸方法,有效的改善了擬合效果并且支撐向量數(shù)較少,避免了過學(xué)習(xí),第二是提出了一種對信號進(jìn)行樣本的符號化,并提取符號熵,對符號熵利用SVM進(jìn)行分類識別運(yùn)算,信號的符號熵值能有效的區(qū)分各種不同的狀態(tài),再利用SVM算法進(jìn)行分類識別與現(xiàn)有方法比較提高了分類精度。
[Abstract]:At present, one of the cutting-edge technologies in the rapid development of science and technology, space technology is at the fastest speed in a wider and wider range, including political, economic, military. Life and science and technology play a very important role in many fields. At the same time, the rapid development of space also makes the internal structure of various spacecraft and the required functions become increasingly complex. For some expensive spacecraft, high reliability of the system is the most basic requirement of the whole spacecraft system, satellite system is a very important part of the space system, once a satellite component failure. The light can cause the entire satellite's predefined function to fail to achieve its intended purpose or to be directly lost, and in very serious cases, it may even lead to some serious catastrophic events. And it would be a waste of the country's huge assets. Until now. The guarantee of high reliability of satellite is usually realized by ensuring high reliability and redundancy of hardware and software. The main components of satellite attitude control system are described in this paper. The model is modeled in two aspects: dynamics and kinematics. And the quaternion and Euler angle are used to describe the attitude information in detail, which provides the basis for the subsequent research. The empirical mode decomposition method is used to extract the eigenvalue of the signal and the energy entropy of the signal is used to set the threshold to determine whether there is an anomaly or not. This paper introduces the main principle of support vector machine (SVM) and how to optimize the two parameters which have obvious influence, and puts forward a new nonlinear way to optimize the parameters. Compared with the existing optimization algorithm, it accelerates the convergence speed of the parameters and effectively reduces the fitness. Using SVM, two methods are proposed to extract satellite state information. The first one is to model the state of the satellite, obtain the residual information between the observed data and obtain the state information, and propose an optimized SVM regression method for the complex data samples. It can effectively improve the fitting effect and the number of support vectors is less, avoid overlearning. The second is to propose a signal sample symbolization, and extract symbol entropy. The symbol entropy is classified and recognized by SVM, and the signal entropy can effectively distinguish different states, and the classification accuracy is improved by using the SVM algorithm compared with the existing methods.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：V448.22

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本文編號：1427445