本文選題：組合導(dǎo)航 + 抗差自適應(yīng)模型��； 參考：《中國礦業(yè)大學(xué)》2014年博士論文

【摘要】：慣性導(dǎo)航系統(tǒng)是一種無源導(dǎo)航設(shè)備，相對(duì)于其他導(dǎo)航系統(tǒng)而言，具有自主性強(qiáng)、短時(shí)精度高，可以連續(xù)輸出導(dǎo)航信息，在軍事、民用中都有巨大應(yīng)用價(jià)值。本文圍繞無縫定位的慣性導(dǎo)航模型改正方法中的關(guān)鍵技術(shù)開展研究，重點(diǎn)涵蓋慣導(dǎo)元件隨機(jī)誤差辨識(shí)、異常檢測與改正抗差自適應(yīng)濾波模型、機(jī)器學(xué)習(xí)輔助遮蔽區(qū)智能導(dǎo)航算法，慣性輔助的行人航跡推算與零速修正室內(nèi)導(dǎo)航定位，主要研究成果如下： (1)針對(duì)常規(guī)Allan方差計(jì)算量龐大，基于最小二乘擬合隨機(jī)誤差參數(shù)時(shí)無法修正系數(shù)矩陣，提出一種基于WTLS的Allan方差簡化估計(jì)算法。經(jīng)實(shí)測數(shù)據(jù)驗(yàn)證，表明該算法可實(shí)現(xiàn)大幅降低計(jì)算量、加快運(yùn)算速度并保持Allan方差分析的準(zhǔn)確性。 (2)針對(duì)室外遮蔽區(qū)衛(wèi)星失鎖，提出一種改進(jìn)徑向基神經(jīng)網(wǎng)絡(luò)結(jié)合自適應(yīng)濾波輔助的組合系統(tǒng)導(dǎo)航模型。采用遺傳算法參數(shù)尋優(yōu)和最近鄰聚類學(xué)習(xí)算法改進(jìn)徑向基神經(jīng)網(wǎng)絡(luò)，通過預(yù)測出偽觀測值與其對(duì)應(yīng)的協(xié)方差，實(shí)現(xiàn)了衛(wèi)星失鎖情況下短時(shí)可靠的導(dǎo)航算法。 (3)提出一種改進(jìn)抗差非線性濾波模型，通過判斷矩陣病態(tài)性自主選取抗差策略；針對(duì)松組合系統(tǒng)觀測無冗余，無法區(qū)分觀測異常和狀態(tài)異常，提出一種支持向量回歸輔助的組合導(dǎo)航抗差自適應(yīng)模型，實(shí)現(xiàn)智能區(qū)分觀測值異常和動(dòng)力學(xué)模型異常，保證組合導(dǎo)航精度，實(shí)現(xiàn)抗差精度與可靠度的統(tǒng)一。 (4)提出一種LS-SVR輔助的改進(jìn)多重漸消自適應(yīng)SVD-UKF算法，利用奇異值分解抑制UKF中先驗(yàn)協(xié)方差矩陣負(fù)定性變化，采用LS-SVR算法削弱觀測異常對(duì)殘差序列高斯白噪聲分布特性的影響，拓展了多重漸消因子的應(yīng)用范圍，為多變量復(fù)雜系統(tǒng)提供了一種可行的先進(jìn)濾波模型。 (5)基于時(shí)頻變換分析慣性傳感器用于室內(nèi)定位的噪聲特征，提出了基于FIR設(shè)計(jì)濾波器、磁力計(jì)數(shù)據(jù)改進(jìn)步態(tài)檢測和姿態(tài)計(jì)算的行人航跡推算；采用廣義似然比辨識(shí)零速時(shí)刻，，基于可靠觀測構(gòu)建自適應(yīng)濾波模型的零速度修正導(dǎo)航模型。削弱觀測值中的噪聲信息，提高定向穩(wěn)定性，增強(qiáng)零速檢測的可靠性，改進(jìn)慣性輔助室內(nèi)行人導(dǎo)航精度。
[Abstract]:Inertial navigation system is a kind of passive navigation equipment. Compared with other navigation systems, inertial navigation system has strong autonomy, high accuracy in short time, and can continuously output navigation information. It has great application value in military and civilian applications. This paper focuses on the key techniques of the inertial navigation model correction method for seamless positioning, focusing on the random error identification of inertial navigation elements, anomaly detection and correction robust adaptive filtering model. Machine learning aided shelter area intelligent navigation algorithm, inertial aided footpath reckoning and zero speed correction indoor navigation positioning. The main research results are as follows: In view of the large amount of calculation of Allan variance and the inability to modify the coefficient matrix when least square fitting random error parameters, a simplified Allan variance estimation algorithm based on WTLS is proposed. The experimental results show that the algorithm can greatly reduce the computational complexity, speed up the calculation and maintain the accuracy of Allan variance analysis. A new integrated system navigation model based on improved radial basis function neural network and adaptive filter is proposed. The radial basis function neural network is improved by using genetic algorithm parameter optimization and nearest neighbor clustering learning algorithm. By predicting the pseudo-observation value and its corresponding covariance, the short-time and reliable navigation algorithm is realized in the case of satellite lost lock. (3) an improved robust nonlinear filtering model is proposed, in which the robust strategy is chosen by the ill-conditioned judgment matrix, and the observation of the loose composite system is not redundant, so it is impossible to distinguish the observed anomalies from the state anomalies. An adaptive robust model of integrated navigation aided by support vector regression is proposed, which can intelligently distinguish the anomaly of observation value from the anomaly of dynamic model, ensure the accuracy of integrated navigation, and realize the unity of robust accuracy and reliability. In this paper, an improved multi-fading adaptive SVD-UKF algorithm assisted by LS-SVR is proposed. The singular value decomposition (SVD) is used to suppress the negative qualitative change of the prior covariance matrix in UKF, and the LS-SVR algorithm is used to weaken the influence of the observed anomalies on the white noise distribution of the residual Gao Si sequence. The application range of multiple fading factors is extended and a feasible advanced filtering model for multivariable complex systems is provided. Based on time-frequency transform (TFT) analysis of noise characteristics of inertial sensors for indoor positioning, a new method is proposed, which is based on FIR filter, magnetometer data to improve gait detection and attitude calculation, and the generalized likelihood ratio is used to identify the zero-speed time. A zero-velocity modified navigation model based on reliable observation is constructed for adaptive filtering model. The noise information in observation value is weakened, the directional stability is improved, the reliability of zero velocity detection is enhanced, and the precision of inertial assistant indoor pedestrian navigation is improved.
【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN96

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本文編號(hào)：1980606