本文選題：慣性導(dǎo)航系統(tǒng) + 精度　； 參考：《哈爾濱工業(yè)大學(xué)》2014年碩士論文

【摘要】：最近幾年,現(xiàn)代控制理論的不斷發(fā)展完善以及計(jì)算機(jī)技術(shù)的進(jìn)步,組合導(dǎo)航的應(yīng)用也愈加廣泛。組合導(dǎo)航從其得名可以知道就是讓多種不同的導(dǎo)航系統(tǒng)通過算法的融合研究最終達(dá)到優(yōu)勢(shì)互補(bǔ)的目的。目前,國內(nèi)外針對(duì)此項(xiàng)技術(shù)已經(jīng)取得了不錯(cuò)的成效,先后推出了多種系列的組合導(dǎo)航系統(tǒng),使其成為現(xiàn)如今最為重要的導(dǎo)航系統(tǒng)。本論文研究了慣性導(dǎo)航/全球定位組合系統(tǒng)中的關(guān)鍵技術(shù),并重點(diǎn)研究后期不同數(shù)據(jù)融合算法對(duì)計(jì)算量與精度的影響因素。為了更好的得到長時(shí)間、高精度的參數(shù),本論文中采用INS為基礎(chǔ)系統(tǒng)同時(shí)GPS為輔助系統(tǒng)構(gòu)成的INS/GPS組合導(dǎo)航,使得以上兩種系統(tǒng)可以達(dá)到“互補(bǔ)”效果。進(jìn)一步將慣性導(dǎo)航系統(tǒng)和全球定位系統(tǒng)得到的測量結(jié)果送入導(dǎo)航計(jì)算機(jī)中,進(jìn)行數(shù)據(jù)濾波融合,最終得到導(dǎo)航參數(shù)。本論文中INS/GPS組合系統(tǒng)主要參數(shù)包含:物體姿態(tài)角,空間位置以及物體的空間速度。本論文著重討論了INS/GPS組合系統(tǒng)的數(shù)據(jù)融合過程,并具體研究了四種不同的數(shù)據(jù)融合算法:基本卡爾曼濾波KF、擴(kuò)展卡爾曼濾波EKF、無敏卡爾曼濾波UKF以及粒子濾波PF。首先從理論層次上針對(duì)四種不同的濾波算法做以下研究:分析同一種濾波系統(tǒng)下影響精度或計(jì)算量的因素并總結(jié)整體規(guī)律;分析同一種系統(tǒng)下不同濾波算法間計(jì)算量與精度關(guān)系,同時(shí)結(jié)合以上所有算法的研究結(jié)論對(duì)其應(yīng)用條件和優(yōu)缺點(diǎn)做出總結(jié)。最后使用matlab軟件進(jìn)行仿真驗(yàn)證,并給出仿真結(jié)果與分析。結(jié)果表明,在基本KF濾波中濾波步長,狀態(tài)向量維數(shù)和初始值是現(xiàn)在仿真過程中影響精度的幾大因素,通過仿真進(jìn)行驗(yàn)證并得到最優(yōu)的經(jīng)驗(yàn)輸入值。在同一種18維系統(tǒng)且濾波步長、初始值最優(yōu)的情況下,分別運(yùn)行組合導(dǎo)航的EKF和UKF濾波算法,結(jié)果發(fā)現(xiàn)UKF的平均使用時(shí)間是EKF時(shí)間的1.3倍,EKF的誤差項(xiàng)是UKF的2倍。在經(jīng)典粒子濾波中,主要是粒子數(shù)目影響精度和計(jì)算量,通過仿真進(jìn)一步驗(yàn)證這一結(jié)論�？偨Y(jié)以上四種濾波算法,總體表明計(jì)算量越復(fù)雜性的系統(tǒng)則非線性化越強(qiáng),需要應(yīng)用更為復(fù)雜的濾波融合算法,并且耗費(fèi)時(shí)間更長,但精度也會(huì)成倍的提高。
[Abstract]:In recent years, with the development of modern control theory and the progress of computer technology, the application of integrated navigation has become more and more extensive. From the name of integrated navigation, it can be known that many different navigation systems can finally achieve the goal of complementary advantages through the fusion of algorithms. At present, many kinds of integrated navigation systems have been developed, which make them become the most important navigation system. In this paper, the key technologies of inertial navigation / global positioning integrated system are studied, and the influence factors of different data fusion algorithms on computation and precision are studied. In order to obtain long time and high precision parameters, the ins / R / GPS integrated navigation system based on ins and GPS is adopted in this paper, which makes the two systems "complementary". The results obtained from inertial navigation system (ins) and global positioning system (GPS) are fed into the navigation computer for data filtering and fusion, and finally the navigation parameters are obtained. In this paper, the main parameters of ins / GPS integrated system include: attitude angle, space position and space velocity of object. This paper mainly discusses the data fusion process of ins / GPS integrated system, and studies four different data fusion algorithms: basic Kalman filter KF, extended Kalman filter EKF, unsensitive Kalman filter UKF and particle filter PF. Firstly, four different filtering algorithms are studied from the theoretical level as follows: analyze the factors that affect the accuracy or computational complexity of the same filtering system and summarize the overall law; This paper analyzes the relationship between computational complexity and accuracy among different filtering algorithms in the same system, and summarizes the application conditions, advantages and disadvantages of all the above algorithms. Finally, the simulation results and analysis are given by using matlab software. The results show that the filter step size, the dimension of state vector and the initial value are the main factors that affect the accuracy of the basic KF filter. The results are verified by simulation and the optimal empirical input value is obtained. The EKF and UKF filtering algorithms of integrated navigation are run under the condition of the same 18 dimensional system with filtering step size and optimal initial value. The results show that the average time of use of UKF is 1.3 times that of EKF and the error term of EKF is twice that of UKF. In the classical particle filter, the number of particles mainly affects the precision and calculation, and the simulation further verifies this conclusion. Summing up the above four filtering algorithms, it can be concluded that the more complex the system is, the stronger the nonlinearity is, and the more complex filtering fusion algorithm is needed, the longer the time is, but the higher the precision is.
【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】：2014
【分類號(hào)】：TN967.2

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