【摘要】：常見的導(dǎo)航系統(tǒng),在導(dǎo)航衛(wèi)星或地面信標失效的環(huán)境中存在導(dǎo)航盲目。慣性導(dǎo)航系統(tǒng)作為一種自主式的導(dǎo)航系統(tǒng),具有良好的隱蔽性、穩(wěn)定性和抗干擾性,可以全天時、全天候、全地域地工作,近年來成為行人導(dǎo)航領(lǐng)域的一大研究熱點。然而,純慣性導(dǎo)航具有導(dǎo)航誤差不斷積累的特點,需設(shè)計行之有效的誤差校正方法來提高系統(tǒng)的長期導(dǎo)航精度。本文基于小尺寸、低成本的微慣性傳感器,結(jié)合足部運動的周期特性,開展了將零速修正輔助的慣性導(dǎo)航技術(shù)應(yīng)用于行人導(dǎo)航的相關(guān)算法研究。主要研究內(nèi)容如下：1、研究了靜基座情況下捷聯(lián)慣導(dǎo)系統(tǒng)的初始對準技術(shù)。初始對準是慣性導(dǎo)航系統(tǒng)的關(guān)鍵技術(shù)之一,其主要目的是確定系統(tǒng)的初始條件。本文基于歐拉平臺誤差角的概念描述了理論導(dǎo)航坐標系到計算導(dǎo)航坐標系之間的失準角,推導(dǎo)了捷聯(lián)慣導(dǎo)系統(tǒng)在不同失準角情況下的系統(tǒng)誤差模型,為文中誤差校正算法的研究奠定了理論基礎(chǔ)。2、研究了基于動態(tài)閾值和聚類分析的步態(tài)檢測方法�，F(xiàn)有的步態(tài)檢測方法由于沒有充分合理地考慮測量值波動對步態(tài)檢測的影響,大都存在檢測結(jié)果不準確和對檢測參數(shù)敏感等不足,進而影響后續(xù)零速修正方法的正確性和有效性。本文通過詳細分析各個檢測參數(shù)的作用和它們之間的耦合關(guān)系,提出了一種基于聚類分析的自適應(yīng)步態(tài)檢測方法,該方法是一種改進的平區(qū)檢測方法,能夠克服現(xiàn)有檢測方法的諸多不足,并擴大檢測方法可行的參數(shù)空間,進而提高步態(tài)檢測的精確性和可靠性。3、研究了基于卡爾曼濾波器的導(dǎo)航誤差估計方法。在進行零速修正時,卡爾曼濾波算法可以充分利用速度誤差與姿態(tài)誤差、位置誤差之間的耦合關(guān)系,估計和校正更多的導(dǎo)航誤差。本文為了降低濾波過程的計算量和高階截斷誤差、避免引入額外的建模誤差并減小濾波發(fā)散的可能性,利用間接濾波原理對原始系統(tǒng)方程進行了分解,然后結(jié)合行人導(dǎo)航的具體應(yīng)用,對分解后的誤差子系統(tǒng)進行了簡化,通過分析支撐相內(nèi)零速度觀測值的可靠性,濾波過程中未將傳感器誤差作為增廣的狀態(tài)向量進行建模和估計。4、研究了基于固定區(qū)間平滑器的導(dǎo)航誤差估計方法。卡爾曼濾波算法僅能對支撐相內(nèi)的導(dǎo)航誤差進行估計,容易在擺動相過渡到支撐相時引起導(dǎo)航信息的突變。固定區(qū)間平滑算法能夠?qū)φ麄�步態(tài)周期內(nèi)的導(dǎo)航誤差進行估計,實現(xiàn)步態(tài)時相的平穩(wěn)過渡,從而提高系統(tǒng)的精確性和穩(wěn)定性。為了使平滑算法滿足在線運行的需求,本文在不降低系統(tǒng)性能的情況下,設(shè)計了未對位置誤差和傳感器誤差進行建模和估計的降階平滑濾波算法,以步進的方式進行導(dǎo)航誤差的估計、平滑和校正,從而達到準實時的平滑估計效果。
[Abstract]:The common navigation system, in navigation satellite or ground beacon failure environment, navigation blind. Inertial navigation system, as an autonomous navigation system, has good concealment, stability and anti-interference. It can work all day, all weather, and all over the area. In recent years, inertial navigation system has become a research hotspot in the field of pedestrian navigation. However, pure inertial navigation has the characteristics of continuous accumulation of navigation errors, it is necessary to design an effective error correction method to improve the long-term navigation accuracy of the system. Based on the micro inertial sensor with small size and low cost, combined with the periodic characteristics of foot motion, this paper studies the application of zero-velocity correction assisted inertial navigation technology to pedestrian navigation. The main contents are as follows: 1. The initial alignment technology of strapdown inertial navigation system under static base is studied. Initial alignment is one of the key technologies of inertial navigation system, and its main purpose is to determine the initial conditions of the system. Based on the concept of error angle of Euler platform, this paper describes the misalignment angle between the theoretical navigation coordinate system and the computational navigation coordinate system, and deduces the systematic error model of the strapdown inertial navigation system under different misalignment angles. It lays a theoretical foundation for the research of error correction algorithm in this paper. 2. The gait detection method based on dynamic threshold and clustering analysis is studied. Because the existing gait detection methods do not fully and reasonably consider the influence of measurement value fluctuation on gait detection, most of the existing gait detection methods have some shortcomings, such as inaccurate detection results and sensitivity to detection parameters, etc. Furthermore, the correctness and effectiveness of the subsequent zero-speed correction method are affected. In this paper, an adaptive gait detection method based on clustering analysis is proposed by analyzing the function of each detection parameter and the coupling relationship between them in detail. This method is an improved flat area detection method. It can overcome the shortcomings of the existing detection methods and expand the feasible parameter space of the detection methods, and then improve the accuracy and reliability of gait detection. 3. A navigation error estimation method based on Kalman filter is studied. In the zero-velocity correction, the Kalman filtering algorithm can make full use of the coupling relationship between velocity error, attitude error and position error, and estimate and correct more navigation errors. In order to reduce the computational complexity and high order truncation error in the filtering process, to avoid the introduction of additional modeling errors and to reduce the possibility of filtering divergence, the original system equations are decomposed by indirect filtering principle. Then combining with the concrete application of pedestrian navigation, the decomposed error subsystem is simplified, and the reliability of zero velocity observation in support phase is analyzed. The sensor error is not modeled and estimated as an augmented state vector in the filtering process. 4 the navigation error estimation method based on fixed interval smoother is studied. The Kalman filtering algorithm can only estimate the navigation error in the support phase, and it is easy to cause the sudden change of navigation information when the swing phase is transitioned to the supporting phase. The fixed-interval smoothing algorithm can estimate the navigation error in the whole gait cycle and realize the steady transition of the gait phase, thus improving the accuracy and stability of the system. In order to make the smoothing algorithm meet the requirements of on-line operation, a reduced order smoothing filtering algorithm without modeling and estimating the position error and sensor error is designed without reducing the performance of the system. The error estimation, smoothing and correction of navigation error are carried out step by step to achieve the effect of quasi-real-time smoothing estimation.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：TN713

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