本文關(guān)鍵詞： 慣性導(dǎo)航系統(tǒng) 誤差校正與補(bǔ)償 姿態(tài)與位置解算 數(shù)據(jù)融合濾波　出處：《東南大學(xué)》2015年博士論文　論文類型：學(xué)位論文

【摘要】：隨著水下潛器技術(shù)的日益成熟,水下滑翔器作為一種新型且重要的水下潛器受到越來越多的關(guān)注。水下滑翔器在海洋工程應(yīng)用方面發(fā)揮著重要作用,尤其是低功耗、長(zhǎng)航時(shí)、小體積等特點(diǎn)更使其成為目前研究的熱點(diǎn)。準(zhǔn)確的位姿信息對(duì)滑翔器長(zhǎng)時(shí)間水下作業(yè)起著必不可少的作用,因此持續(xù)高精度導(dǎo)航定位是水下滑翔器研究的關(guān)鍵技術(shù)之一借助水流和自身調(diào)節(jié)作用順?biāo)?幾乎不需外界提供能源是水下滑翔器有別于其它水下潛器的重要特點(diǎn),故其具有重要的應(yīng)用領(lǐng)域和實(shí)用價(jià)值。低功耗使得滑翔器本體設(shè)計(jì)簡(jiǎn)單且體積小,所搭載的導(dǎo)航裝置數(shù)量盡量少。作為陸地成熟且定位精度很高的全球定位系統(tǒng)(Global Positioning System, GPS)不能應(yīng)用于水下,有自主導(dǎo)航解算能力的慣性導(dǎo)航系統(tǒng)(Inertial Navigation System, INS)成為替代GPS的較優(yōu)選擇。INS通過自身的陀螺儀、加速度計(jì)等傳感器測(cè)量載體當(dāng)前時(shí)刻的旋轉(zhuǎn)角速度和線加速度,測(cè)得的數(shù)據(jù)通過積分等運(yùn)算得到載體的姿態(tài)、速度及位置,但I(xiàn)NS的測(cè)量誤差會(huì)隨著時(shí)問而積累,單獨(dú)長(zhǎng)時(shí)間工作會(huì)嚴(yán)重降低導(dǎo)航精度。應(yīng)用于水下滑翔器的慣性導(dǎo)航系統(tǒng),因成本和體積的限制只能選用微機(jī)電系統(tǒng)(Micro-Electro-Mechanical System, MEMS)慣性測(cè)量單元(Inertial Measurement Unit, IMU),該慣性測(cè)量單元的誤差和隨機(jī)漂移更加明顯。有低功耗、長(zhǎng)航時(shí)、小體積及低成本等條件的制約,加之導(dǎo)航傳感器精度低且外界輔助導(dǎo)航少,要實(shí)現(xiàn)高精度高可靠性的水下導(dǎo)航與定位是目前研究的重點(diǎn)也是難點(diǎn)。在參考國(guó)內(nèi)外大量低精度慣性導(dǎo)航元件實(shí)現(xiàn)高精度長(zhǎng)航時(shí)導(dǎo)航文獻(xiàn)的基礎(chǔ)上,對(duì)水下滑翔器運(yùn)動(dòng)模型進(jìn)行分析,建立航位推算(Dead Reckoning, DR)模型并與慣性導(dǎo)航組合,設(shè)計(jì)出應(yīng)用于水下滑翔器的導(dǎo)航系統(tǒng),對(duì)慣性測(cè)量單元誤差模型深入分析并針對(duì)不同的導(dǎo)航傳感器進(jìn)行誤差校正與補(bǔ)償,提出應(yīng)用于水下環(huán)境的高精度高可靠性高效的數(shù)據(jù)融合算法,使該系統(tǒng)及算法更適合實(shí)際工程應(yīng)用。論文的主要工作和創(chuàng)新點(diǎn)如下：(1)對(duì)水下滑翔器運(yùn)動(dòng)模型進(jìn)行分析,建立航位推算模型并與慣導(dǎo)系統(tǒng)組合,完成用于水下滑翔器的導(dǎo)航系統(tǒng)設(shè)計(jì)。針對(duì)水下實(shí)際應(yīng)用環(huán)境,結(jié)合滑翔器自身特點(diǎn),詳細(xì)分析水下滑翔器運(yùn)動(dòng)模型,進(jìn)行航位推算并與慣性導(dǎo)航系統(tǒng)組合,對(duì)慣性導(dǎo)航進(jìn)行輔助及校正。以低功耗MEMS慣性元件為主體設(shè)計(jì)出適用于水下滑翔器的小體積低成本導(dǎo)航系統(tǒng),滿足水下長(zhǎng)航時(shí)、低功耗的工作要求,并能提供高精度高可靠性的導(dǎo)航與定位信息。另外,在水下工作一段時(shí)間后滑翔器浮出水面,該系統(tǒng)能智能接收GPS或其它衛(wèi)星信號(hào)對(duì)導(dǎo)航信息進(jìn)行更新。(2)對(duì)MEMS慣性傳感器及磁傳感器誤差進(jìn)行建模,并針對(duì)不同的傳感器誤差模型提出相應(yīng)的校正與補(bǔ)償方法。MEMS慣性測(cè)量單元的零位漂移、刻度因子和安裝誤差等是準(zhǔn)確建立誤差模型的重要參數(shù)。設(shè)計(jì)靜態(tài)八位置實(shí)驗(yàn),利用多位置對(duì)稱測(cè)量原理確定陀螺儀和加速度計(jì)的零位漂移,通過陀螺儀速率實(shí)驗(yàn)計(jì)算陀螺儀各軸刻度因子及安裝誤差。本論文提出加速度計(jì)動(dòng)態(tài)測(cè)量方法,相比傳統(tǒng)的靜態(tài)多位置法更實(shí)時(shí)有效方便地計(jì)算出加速度計(jì)的刻度因子和安裝誤差。由系統(tǒng)誤差模型推導(dǎo)誤差補(bǔ)償模型,代入相關(guān)誤差參數(shù)分別對(duì)陀螺儀和加速度計(jì)誤差進(jìn)行補(bǔ)償。處理磁傳感器采集的磁場(chǎng)信息是將原始含有軟硬磁等干擾信息的磁場(chǎng)去噪后擬合三維橢球磁場(chǎng),得到擬合參數(shù)對(duì)磁傳感器輸出進(jìn)行校正與補(bǔ)償,提高地磁場(chǎng)的測(cè)量精度。(3)針對(duì)實(shí)際非線性模型,為了提高導(dǎo)航信息的估計(jì)精度提出將擴(kuò)展卡爾曼和龍格庫塔法融合的濾波算法,在此基礎(chǔ)上為進(jìn)一步提高精度及適用范圍,提出平滑參數(shù)的無跡卡爾曼濾波算法�；杵髟谝欢ㄉ疃鹊乃谢�,水環(huán)境相對(duì)穩(wěn)定,在較長(zhǎng)的滑翔時(shí)間內(nèi)可將非線性模型劃分成若干線性模型處理,基于這樣的思想可簡(jiǎn)化建模,在不太增加算法復(fù)雜性及計(jì)算量的情況下達(dá)到提高精度的目的。本文提出將擴(kuò)展卡爾曼(Extended Kalman Fiter, EKF)與龍格庫塔法(Runge-Kutta,RK4)相結(jié)合的EKF/RK4濾波算法并應(yīng)用于實(shí)際水下導(dǎo)航系統(tǒng),EKF在非線性程度不太高的系統(tǒng)中優(yōu)勢(shì)較明顯,再結(jié)合高精度數(shù)值計(jì)算方法(龍格庫塔法),實(shí)驗(yàn)表明比起傳統(tǒng)的EKF和UKF,EKF/RK4數(shù)據(jù)融合算法能有效減小誤差,姿態(tài)角和位置估計(jì)精度也顯著提高。為了擴(kuò)大算法的適用范圍并進(jìn)一步提高估計(jì)精度,鑒于無跡卡爾曼濾波(Unscented Kalman Filter, UKF)在更多的非線性模型中的性能優(yōu)勢(shì)提出平滑參數(shù)的無跡卡爾曼濾波算法(Smooth Variable Unscented Kalman Filter, SVUKF)�；赨KF進(jìn)行參數(shù)平滑處理使非線性系統(tǒng)的最優(yōu)估計(jì)更加精確,經(jīng)實(shí)驗(yàn)驗(yàn)證,算法的估計(jì)精度及穩(wěn)定性得到有效提高,并且算法的魯棒性也有所增強(qiáng)。(4)對(duì)較復(fù)雜的系統(tǒng)模型提出改進(jìn)高斯混合粒子濾波算法(Improved Gaussian Mixture Particle Filter, IGMPF)并用實(shí)驗(yàn)對(duì)其性能進(jìn)行驗(yàn)證：以解決實(shí)際問題為目的,權(quán)衡精度計(jì)算速度及可靠性等多項(xiàng)評(píng)估標(biāo)準(zhǔn),綜合比較本論文提出的所有算法,提出高效高精度回溯解耦自適應(yīng)擴(kuò)展卡爾曼濾波算法(Back Decoupling and Adaptive Extended Kalman Filter, BD-AEKF)。水下滑翔器在滑翔過程中不排除在個(gè)別時(shí)刻或區(qū)域出現(xiàn)環(huán)境突變且含有非高斯噪聲的情況,這種較復(fù)雜的環(huán)境可能會(huì)使UKF算法表現(xiàn)欠佳,本文基于粒子濾波對(duì)系統(tǒng)進(jìn)行混合高斯建模并提出改進(jìn)高斯混合粒子濾波算法,通過和其它算法對(duì)比可得,IGMPF算法的姿態(tài)角和位置估計(jì)精度有所提高,但實(shí)時(shí)性變差計(jì)算速度下降,以此為代價(jià)來提高精度在實(shí)際應(yīng)用中可能并不能廣泛使用。另外,對(duì)于慣性元件,其安裝軸和參考軸之間的安裝誤差是不可避免的,這種固有誤差將導(dǎo)致三個(gè)姿態(tài)角(航向角、俯仰角和橫滾角)之間存在交叉耦合,造成姿態(tài)角解算不準(zhǔn)甚至錯(cuò)誤,當(dāng)俯仰角或橫滾角發(fā)生變化時(shí),這種現(xiàn)象變得更加明顯。俯仰或橫滾運(yùn)動(dòng)在滑翔器滑翔過程中很常見,交叉耦合導(dǎo)致的姿態(tài)角解算誤差將不斷出現(xiàn)并積累。針對(duì)這一實(shí)際問題提出BD-AEKF算法,用回溯解耦算法判斷解算錯(cuò)誤的節(jié)點(diǎn),然后消除姿態(tài)角之間的交叉耦合；自適應(yīng)擴(kuò)展卡爾曼濾波實(shí)時(shí)自適應(yīng)調(diào)節(jié)導(dǎo)航參數(shù),對(duì)濾波輸出進(jìn)行平滑,最終達(dá)到消除耦合提高解算精度及穩(wěn)定輸出的目的。對(duì)于實(shí)際應(yīng)用問題,需要權(quán)衡估算精度、計(jì)算速度、算法復(fù)雜度、魯棒性及可靠性等多項(xiàng)評(píng)估標(biāo)準(zhǔn)來選擇較優(yōu)的算法,綜合比較本論文提出的所有算法,可看出BD-AEKF估算精度比較高且解算速度并不慢,是實(shí)際應(yīng)用中較可行的高效高精度算法。
[Abstract]:With the underwater vehicle technology has become more sophisticated, underwater glider is a new and important underwater vehicle has attracted more and more attention. The underwater glider plays an important role in ocean engineering applications, especially for low power consumption, long endurance, small size and other characteristics has become a hot topic at present. Accurate position information plays an essential role in the glider long time underwater, so continuous high precision navigation is one of the key technologies of underwater gliders with water and their own regulation of smooth and slippery, almost without external energy is glider has an important characteristic different from other underwater vehicles under the water, so it has important applications in the field of low power consumption and practical value. The glider body has the advantages of simple design and small size, the number of navigation devices are equipped with as little as possible. As the land is mature and positioning precision The global positioning system is very high (Global Positioning System, GPS) can not be applied to underwater inertial navigation system of autonomous navigation capability (Inertial Navigation System, INS GPS) as an alternative to the optimal choice of.INS through its own gyroscope, accelerometer and other sensors for measuring the angular velocity and the vector of the current line the acceleration, the measured data obtained by the carrier's attitude integral calculations, speed and position, but the measurement error of INS with time and the accumulation of long time working alone would seriously reduce the navigation accuracy. The inertial navigation system is applied to the underwater glider, due to limited cost and volume selection of MEMS (Micro-Electro-Mechanical System MEMS) inertial measurement unit (Inertial Measurement Unit, IMU), the error of the inertial measurement unit and the random drift is more obvious. With low power consumption, long endurance, body Restrictive conditions of product and low cost, and low precision navigation sensor and navigation outside less, to achieve high precision and high reliability of the underwater navigation and positioning are the focus of the present study is difficult. On the basis of a large number of domestic and foreign low precision inertial navigation components to achieve high precision long voyage navigation on the literature of gliding the motion model of underwater is analyzed, the establishment of the dead reckoning (Dead Reckoning, DR) model and combined with inertial navigation, design a navigation system for underwater glider, and according to the in-depth analysis of different navigation sensors for error correction and compensation of the error model of inertial measurement unit, the data of high precision and high reliability and efficient used in underwater environment fusion algorithm, the system and the algorithm is more suitable for practical engineering application. The main work and innovation are as follows: (1) the movement model of underwater glider Type analysis, the establishment of the dead reckoning model and inertial navigation system, complete the design of navigation system for underwater gliders. The underwater glider combined with the actual application environment, its own characteristics, the glider motion model is analyzed in detail under water, and for dead reckoning and inertial navigation system combination, auxiliary and correction of inertia navigation. With low power consumption MEMS inertial components as the main design of small size and low cost navigation system for underwater glider, meet the water under long endurance, low power requirements, and can provide navigation and positioning information of high precision and high reliability. In addition, work for a period of time in the underwater glider surfaced, the system can receive GPS or other intelligent satellite signal of the navigation information is updated. (2) to model the error of MEMS inertial sensors and magnetic sensor, and different sensor error model is proposed The corresponding correction of zero drift and compensation method of.MEMS inertial measurement unit, scale factor and installation error is an important parameter for accurate error model is established. The eight experimental design of static position, determine the zero drift of gyroscope and accelerometer using the principle of symmetrical position measurement, through the experimental calculation of each axis rate gyro gyroscope scale factor and installation error. This thesis puts forward the dynamic measurement method of accelerometer, compared with many traditional static method is more effective in real time convenient to calculate the scale factor and installation error of accelerometer. The system error model error compensation model, substituted the relevant parameters on the error of gyro and accelerometer error compensation. The magnetic field information acquisition and processing of magnetic sensor is the original with the soft and hard magnetic interference information of the magnetic field after denoising fitting ellipsoid fitting parameters on the magnetic field. The output of the magnetic sensor calibration and compensation, improve the measurement accuracy of the geomagnetic field. (3) according to the actual nonlinear model, in order to improve the estimation accuracy of the navigation information of the extended Kalman filtering fusion algorithm Calman and Runge Kutta method, on this basis to further improve the accuracy and applicability of the proposed filtering algorithm, unscented Calman smoothing parameters. The glider gliding in certain depth of water, the water environment is relatively stable, in a long period of time can be gliding nonlinear model is divided into several linear models, this idea can be simplified based on the modeling, to improve the precision of algorithm complexity and increase in less computation time. In this paper, the extended Calman (Extended Kalman Fiter, EKF) and Runge Kutta method (Runge-Kutta, RK4) combined with EKF/RK4 filtering algorithm and applied to the underwater navigation system, EKF in nonlinear The system is not too high in the obvious advantages, combined with high precision numerical method (Runge Kutta method), experiments show that compared with the traditional EKF and UKF, EKF/RK4 data fusion algorithm can effectively reduce error, attitude angle and position estimation accuracy is significantly improved. In order to expand the scope and improve the estimation algorithm in view of the unscented filtering precision, Calman (Unscented Kalman Filter, UKF Calman) unscented filtering algorithm performance advantages in nonlinear model more in the smoothing parameter (Smooth Variable Unscented Kalman Filter, SVUKF UKF). The optimal smoothing parameter estimation for nonlinear system based on more accurate and verified by experiment, and the estimation accuracy the stability of the algorithm effectively improve the robustness of the algorithm, and has also been enhanced. (4) proposed Gauss hybrid particle filter algorithm for the system model is complex (Improved Gaussian Mixture Particle Filter, IGMPF) and its performance is demonstrated by experiments: in order to solve practical problems for the purpose of balancing precision calculation speed and reliability evaluation standard, all the algorithms proposed in this paper, a comprehensive comparison, put forward a high precision backtracking adaptive decoupling extended Calman filter algorithm (Back Decoupling and Adaptive Extended Kalman Filter, BD-AEKF). The underwater glider does not exclude the environmental catastrophe and contains non Gauss noise in the individual time or area in the gliding process, the complicated environment may cause the poor performance of the UKF algorithm, the particle filter for mixed Gauss model of the system and put forward the improved Gauss hybrid particle filter algorithm based on the available by comparing with other algorithms, IGMPF algorithm's position and attitude estimation accuracy is improved, but the real-time variation calculation This rate of decline, in order to increase the accuracy in practical application and can be widely used. In addition, the inertia element, its installation installation error between the shaft and the reference axis is inevitable, this error will lead to three attitude angle (heading angle, pitch angle and roll angle) cross coupling between. Not even cause the attitude calculation error, when the pitch or roll angle changes, this phenomenon becomes more and more obvious. The pitch or roll movement is very common in the glider gliding process, cross coupling leads to the attitude angle calculation error will appear and accumulation. BD-AEKF algorithm is proposed to solve this problem. Backtracking algorithm to determine the error decoupling solution node, and then eliminate the cross coupling between attitude angle; adaptive extended Calman filter adaptive real-time navigation parameters, smoothing filter output, the most The final solution to eliminate the coupling to improve the accuracy and stability of output. For practical problems, need to weigh the estimation accuracy, computing speed, complexity, robustness and reliability evaluation criteria to select the optimal algorithm, the algorithm proposed in this paper is a comprehensive comparison, shows relatively high estimation accuracy and BD-AEKF the calculating speed is not slow, is a high precision algorithm is feasible in practical application.

【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：U666.1

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