發(fā)布時間：2018-01-16 08:20

  本文關(guān)鍵詞：長基線導(dǎo)航系統(tǒng)濾波算法的研究與實現(xiàn)　出處：《沈陽理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 載人潛水器 LBL/DR組合導(dǎo)航系統(tǒng) 迭代平方根濾波 自適應(yīng)平方根濾波

【摘要】：作為海洋探索與開發(fā)的重要工具,載人潛水器無論在軍用領(lǐng)域、民用領(lǐng)域還是科研領(lǐng)域都具有非常廣闊的應(yīng)用前景,導(dǎo)航定位技術(shù)是其發(fā)展的關(guān)鍵。隨著世界各國對海洋探索與開發(fā)的日漸深入,人們對水下導(dǎo)航定位系統(tǒng)提出了更高的要求,但由于硬件和成本的限制,使得通過高精度的導(dǎo)航傳感器來獲得高精度的導(dǎo)航定位系統(tǒng)變得愈加困難。此外,復(fù)雜未知的海洋環(huán)境,要求導(dǎo)航定位系統(tǒng)具有高度的自主性、可靠性和強(qiáng)抗干擾能力,由單一傳感器構(gòu)成的導(dǎo)航系統(tǒng)已難以滿足要求。因此研究適應(yīng)性強(qiáng)精度高的非線性濾波算法成為獲得可靠的高精度的導(dǎo)航定位系統(tǒng)的主要途徑之一。無跡卡爾曼濾波算法是目前廣泛使用的非線性濾波算法,而平方根無跡卡爾曼濾波算法相對于前者具有顯著的優(yōu)勢,因此本文以實際科研項目為研究背景,主要圍繞平方根無跡卡爾曼濾波算法及其在LBL/DR組合導(dǎo)航系統(tǒng)中的應(yīng)用開展相關(guān)研究工作。首先,簡要介紹了LBL/DR組合導(dǎo)航系統(tǒng)的組成及工作原理。其次,對無跡卡爾曼濾波算法和平方根無跡卡爾曼濾波算法的應(yīng)用前提和算法的實現(xiàn)過程進(jìn)行了詳細(xì)地闡述,并從理論上證明了平方根無跡卡爾曼濾波算法不僅能夠解決無跡卡爾曼濾波算法應(yīng)用過程中存在的濾波器的計算發(fā)散問題,而且還可以提高計算效率。再次,在平方根無跡卡爾曼濾波算法的基礎(chǔ)上,圍繞其測量更新方法的不足和不具有應(yīng)對噪聲統(tǒng)計變化的自適應(yīng)能力問題開展研究工作。針對平方根無跡卡爾曼濾波算法測量更新方法的不足,前人已經(jīng)做了大量的理論研究工作,本文僅對其中一種易于工程實現(xiàn)的迭代平方根無跡卡爾曼濾波算法進(jìn)行介紹,該方法可使濾波估計輸出具有更高的精度和更小的方差。針對平方根無跡卡爾曼濾波算法不具有應(yīng)對噪聲統(tǒng)計變化的自適應(yīng)能力,其在噪聲統(tǒng)計未知時變情況下易出現(xiàn)濾波精度下降甚至發(fā)散的問題,本文提出了一種帶時變噪聲統(tǒng)計估計器的自適應(yīng)平方根無跡卡爾曼濾波器。在濾波過程中,自適應(yīng)濾波一方面利用量測值修正預(yù)測值,另一方面也對未知的或不確切的噪聲統(tǒng)計參數(shù)進(jìn)行估計修正。最后,利用載人潛水器以往的海試數(shù)據(jù)對迭代平方根無跡卡爾曼濾波算法和自適應(yīng)平方根無跡卡爾曼濾波算法進(jìn)行了驗證。
[Abstract]:As an important tool of ocean exploration and development, manned submersible has a very broad application prospect in military, civil and scientific research fields. Navigation and positioning technology is the key to its development. With the deepening of ocean exploration and development in the world, people put forward higher requirements for underwater navigation and positioning system, but due to hardware and cost constraints. It becomes more and more difficult to obtain high precision navigation and positioning system by high precision navigation sensor. In addition, the complex unknown marine environment requires the navigation and positioning system to have a high degree of autonomy. Reliability and strong anti-jamming ability. The navigation system composed of a single sensor is difficult to meet the requirements. Therefore, the study of nonlinear filtering algorithm with high adaptability and high precision has become one of the main ways to obtain reliable navigation and positioning system with high accuracy. Filtering algorithm is a widely used nonlinear filtering algorithm. The square root unscented Kalman filter algorithm has a significant advantage over the former, so this paper takes the actual research project as the research background. This paper mainly focuses on square root unscented Kalman filter algorithm and its application in LBL/DR integrated navigation system. First of all. The composition and working principle of LBL/DR integrated navigation system are introduced briefly. Secondly. The application premise and realization process of unscented Kalman filter algorithm and square root unscented Kalman filter algorithm are described in detail. It is proved theoretically that square root unscented Kalman filter algorithm can not only solve the problem of filter divergence in the application of unscented Kalman filter algorithm, but also improve the computational efficiency. Based on the square root unscented Kalman filtering algorithm. The research work is focused on the deficiency of the measurement updating method and the adaptive ability to deal with the statistical changes of noise, and the deficiency of the square root unscented Kalman filter algorithm. Previous researchers have done a lot of theoretical research. This paper only introduces one of the iterative square root unscented Kalman filtering algorithms which is easy to be implemented in engineering. This method can make the output of filter estimation have higher accuracy and smaller variance. The unscented Kalman filter algorithm for square root has no adaptive ability to deal with the noise statistical changes. In this paper, an adaptive square root unscented Kalman filter with time-varying noise estimator is proposed. On the one hand, the adaptive filter uses the measured value to correct the prediction value, on the other hand, it also estimates the unknown or inaccurate noise statistical parameters. Finally. The iterative square root unscented Kalman filter algorithm and the adaptive square root unscented Kalman filter algorithm are verified by the previous sea test data of the manned submersible.
【學(xué)位授予單位】：沈陽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P715;TN713

【共引文獻(xiàn)】

相關(guān)期刊論文 前10條

1 錢默抒;姜斌;杜董生;楊浩;;新型UKF在非線性系統(tǒng)執(zhí)行器故障估計中的應(yīng)用[J];東南大學(xué)學(xué)報(自然科學(xué)版);2011年S1期

2 周本川;程向紅;;魯棒UKF濾波算法在SINS初始對準(zhǔn)中的應(yīng)用(英文)[J];Journal of Southeast University(English Edition);2011年01期

3 馬錦垠;陳高杰;;艦船抗沖擊試驗爆源與靶船定位方法研究[J];電子測量技術(shù);2012年03期

4 朱洪前;胡豁生;桂衛(wèi)華;;軟海底強(qiáng)干擾環(huán)境下的集礦車自適應(yīng)定位[J];高技術(shù)通訊;2009年11期

5 丁達(dá)理;羅建軍;宋磊;馬衛(wèi)華;;基于ICSM-UKF算法的UCAV目標(biāo)狀態(tài)估計[J];電光與控制;2012年11期

6 金瑤;蔡之華;梁丁文;;基于差分演化算法的自適應(yīng)無跡卡爾曼濾波[J];電子與信息學(xué)報;2013年04期

7 趙利強(qiáng);羅達(dá)燦;王建林;于濤;;自適應(yīng)強(qiáng)跟蹤容積卡爾曼濾波算法[J];北京化工大學(xué)學(xué)報(自然科學(xué)版);2013年03期

8 王思思;齊國清;;自適應(yīng)SCKF在機(jī)動目標(biāo)跟蹤中的應(yīng)用[J];電光與控制;2013年08期

9 武斌;陳鵬;胡永江;王長龍;;小型尾坐式飛行器自適應(yīng)SRUKF姿態(tài)算法[J];傳感技術(shù)學(xué)報;2014年05期

10 何昆鵬;王曉雪;王剛;韓繼韜;王晨陽;;改進(jìn)的自適應(yīng)卡爾曼濾波在SINS初始對準(zhǔn)中的應(yīng)用[J];兵工自動化;2015年01期

，

本文編號：1432305