本文選題：組合導(dǎo)航 + 慣性元件　； 參考：《哈爾濱工程大學(xué)》2014年碩士論文

【摘要】：在工程領(lǐng)域,無論何種形式的對(duì)象,信息感知都是一項(xiàng)重要的課題。信息的感知可以是感知自己,感知對(duì)方或者感知環(huán)境。導(dǎo)航就是一種信息感知。無論何種載體,若想要到達(dá)目的地,都需要有導(dǎo)航信息來輔助運(yùn)動(dòng)控制。所以,導(dǎo)航的精度至關(guān)重要。為提升導(dǎo)航系統(tǒng)的精度和可靠性,人們提出了組合導(dǎo)航系統(tǒng),通過組合不同種類的子導(dǎo)航系統(tǒng),以克服子系統(tǒng)的局限并實(shí)現(xiàn)精度的提升。在組合導(dǎo)航系統(tǒng)中,慣導(dǎo)系統(tǒng)往往處于主導(dǎo)地位,而慣導(dǎo)的精度又由慣性元件決定。與此同時(shí),為進(jìn)行組合導(dǎo)航的數(shù)據(jù)融合,需要根據(jù)各子系統(tǒng)構(gòu)建精確的數(shù)學(xué)模型,否則將會(huì)使組合導(dǎo)航變得毫無優(yōu)勢(shì)可言。因此,慣性元件誤差的精確模型,對(duì)于誤差的補(bǔ)償或者組合導(dǎo)航系統(tǒng)的構(gòu)建均具有重要意義。針對(duì)這一點(diǎn),本文從組合建模的觀點(diǎn)出發(fā),組合使用小波閾值去噪和對(duì)角神經(jīng)網(wǎng)絡(luò)對(duì)慣性元件隨機(jī)誤差進(jìn)行建模。并在基于偽距、偽距率的組合導(dǎo)航系統(tǒng)中使用了這種模型以證明其有效性。首先,對(duì)INS/GNSS組合導(dǎo)航相關(guān)的模型進(jìn)行了介紹和推導(dǎo),其中包括慣導(dǎo)誤差方程、GNSS相關(guān)原理以及組合導(dǎo)航系統(tǒng)的構(gòu)建等。并且在模型推導(dǎo)時(shí),盡量不做簡(jiǎn)化處理,保留更完整的參數(shù)信息。其次,使用Allan方差和功率譜密度對(duì)光纖陀螺的實(shí)測(cè)數(shù)據(jù)進(jìn)行分析,并發(fā)現(xiàn)使用傳統(tǒng)的隨機(jī)數(shù)、相關(guān)噪聲加白噪聲的假設(shè)模型不足以精確描述其隨機(jī)誤差,而若使用ARMA模型則操作復(fù)雜且表現(xiàn)力不足。為此,根據(jù)分析所得,本文選用小波閾值去噪對(duì)陀螺進(jìn)行一步消噪處理。在小波閾值去噪時(shí),文中提供了一個(gè)求取分解層次參考值的方法并對(duì)比了各種閾值去噪規(guī)則的效果。仿真結(jié)果表明,在消噪后,陀螺的中、高頻噪聲被有效濾除。再次,針對(duì)去噪后殘留的陀螺隨機(jī)誤差具有低頻和相關(guān)性的特點(diǎn),使用對(duì)角神經(jīng)網(wǎng)絡(luò)進(jìn)行時(shí)序建模。為加速網(wǎng)絡(luò)的收斂,使用LM算法改進(jìn)了對(duì)角神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)算法;討論了對(duì)角神經(jīng)網(wǎng)絡(luò)時(shí)序建模的優(yōu)勢(shì),證明了使用對(duì)角神經(jīng)網(wǎng)絡(luò)更方便快捷。在接受模型檢驗(yàn)后,對(duì)角神經(jīng)網(wǎng)絡(luò)的模型殘差為零均值的白噪聲,這也將為組合導(dǎo)航濾波帶來了便利。仿真結(jié)果表明,使用對(duì)角神經(jīng)網(wǎng)絡(luò)對(duì)陀螺這些殘留隨機(jī)誤差建模取得了良好的效果。最后,根據(jù)文中所使用的方法和結(jié)論,提出一種小波閾值去噪和對(duì)角神經(jīng)網(wǎng)絡(luò)建模輔助下的組合導(dǎo)航系統(tǒng),并使用跑車實(shí)測(cè)數(shù)據(jù)進(jìn)行仿真驗(yàn)證。結(jié)果表明,在這種模型輔助下,基于偽距、偽距率的組合導(dǎo)航系統(tǒng)有更好的表現(xiàn)。
[Abstract]:In the field of engineering, no matter what form of object, information perception is an important subject. The perception of information can be to perceive oneself, to perceive the other party, or to perceive the environment. Navigation is a kind of information perception. No matter what kind of carrier, to reach the destination, we need navigation information to assist motion control. Therefore, the accuracy of navigation is very important. In order to improve the accuracy and reliability of the navigation system, the integrated navigation system is proposed, which can overcome the limitations of the subsystem and improve the accuracy by integrating different kinds of sub-navigation systems. In integrated navigation systems, inertial navigation systems are often dominant, and the accuracy of inertial navigation is determined by inertial components. At the same time, in order to fuse the data of integrated navigation, it is necessary to construct accurate mathematical models according to each subsystem, otherwise, the integrated navigation will have no advantage at all. Therefore, the accurate model of inertial element error is of great significance for error compensation or integrated navigation system construction. In this paper, from the point of view of combinatorial modeling, wavelet threshold denoising and diagonal neural network are used to model the random error of inertial elements. This model is used in the integrated navigation system based on pseudo-range and pseudo-range rate to prove its validity. Firstly, the related models of INS/GNSS integrated navigation are introduced and deduced, including the ins error equation and the related principle of GNSS, and the construction of integrated navigation system. And in the derivation of the model, as far as possible do not do simplification processing, retain more complete parameter information. Secondly, the Allan variance and power spectral density are used to analyze the measured data of fog, and it is found that the assumption model of correlation noise and white noise is not enough to describe the random error accurately. If the ARMA model is used, the operation will be complicated and the performance will be insufficient. Therefore, according to the analysis results, the wavelet threshold de-noising is used to remove the gyroscope. In the wavelet threshold denoising, a method to obtain the reference value of decomposition hierarchy is provided, and the effects of various threshold denoising rules are compared. The simulation results show that the high frequency noise of the gyroscope is effectively filtered after de-noising. Thirdly, aiming at the low frequency and correlation of the residual random error after denoising, the diagonal neural network is used to model the time series. In order to accelerate the convergence of the network, the learning algorithm of diagonal neural network is improved by using LM algorithm, and the advantage of time series modeling of diagonal neural network is discussed, which proves that using diagonal neural network is more convenient and fast. After the model is tested, the model residual of the diagonal neural network is white noise with zero mean value, which will also facilitate the integrated navigation filtering. The simulation results show that the diagonal neural network is used to model these residual random errors of gyroscope. Finally, according to the methods and conclusions used in this paper, a new integrated navigation system based on wavelet threshold denoising and diagonal neural network modeling is proposed. The results show that the integrated navigation system based on pseudo-range and pseudo-range rate has better performance under the aid of this model.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN96;TP183

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