本文選題：運載器組合導(dǎo)航 + 卡爾曼濾波　； 參考：《西北工業(yè)大學(xué)》2014年博士論文

【摘要】：隨著現(xiàn)代科學(xué)技術(shù)的快速發(fā)展,人們對運載器導(dǎo)航解算的實時性和快速性的要求越來越高。常用的運載器導(dǎo)航解算主要采用卡爾曼濾波方法,應(yīng)用卡爾曼濾波進(jìn)行導(dǎo)航解算時,要求動力學(xué)系統(tǒng)的數(shù)學(xué)模型必須為線性。當(dāng)系統(tǒng)模型為非線性時,若采用卡爾曼濾波算法進(jìn)行導(dǎo)航解算,將會引起較大的誤差,甚至導(dǎo)致濾波發(fā)散。為了提高運載器組合導(dǎo)航的解算精度,研究適用于運載器組合導(dǎo)航的高精度、非線性濾波算法,是交通信息工程與控制領(lǐng)域一項重要而又需要迫切研究的任務(wù)。 本文在認(rèn)真研究現(xiàn)有導(dǎo)航濾波算法的基礎(chǔ)上,提出了一套適合運載器組合導(dǎo)航的高性能濾波算法,包括非線性模型預(yù)測Unscented粒子濾波算法、衰減記憶平方根Unscented粒子濾波算法、模糊抗差自適應(yīng)Unscented粒子濾波算法、基于狀態(tài)相關(guān)系數(shù)的抗差自適應(yīng)濾波算法、非線性抗差自適應(yīng)狀態(tài)相關(guān)黎卡提方程濾波算法、以及動力學(xué)模型誤差的Sage隨機(jī)加權(quán)自適應(yīng)濾波算法。將提出的算法應(yīng)用到運載器組合導(dǎo)航系統(tǒng)中進(jìn)行仿真驗證,并與現(xiàn)有的濾波算法進(jìn)行比較,結(jié)果表明,提出的算法不但計算量小,而且濾波精度高,濾波性能明顯優(yōu)于現(xiàn)有的濾波算法。 論文主要研究內(nèi)容和創(chuàng)新性貢獻(xiàn)如下 (1)提出一種新的非線性模型預(yù)測Unscented粒子濾波算法。該算法在建立系統(tǒng)模型時顧及了模型誤差,利用估計出的模型誤差對含有誤差的非線性、非高斯系統(tǒng)模型進(jìn)行修正,再利用Unscented粒子濾波進(jìn)行解算。仿真結(jié)果表明,提出算法的濾波性能明顯優(yōu)于模型預(yù)測濾波和Unscented粒子濾波,提高了導(dǎo)航解算精度。 (2)在研究Unscented粒子濾波的基礎(chǔ)上,吸收了衰減記憶濾波和平方根濾波的優(yōu)點,提出一種新的衰減記憶平方根Unscented粒子濾波算法。在該算法中,通過衰減因子調(diào)節(jié)當(dāng)前量測信息對估計值的影響,減小歷史信息對濾波的作用。然后用協(xié)方差矩陣的平方根陣代替協(xié)方差矩陣進(jìn)行迭代計算,保證了協(xié)方差矩陣的對稱性和正定性。研究結(jié)果表明,提出的算法能有效改善濾波性能,提高了導(dǎo)航系統(tǒng)的解算精度。 (3)在研究模糊控制理論的基礎(chǔ)上,吸收了Unscented粒子濾波、自適應(yīng)濾波和抗差估計的優(yōu)點,提出一種新的模糊抗差自適應(yīng)Unscented粒子濾波算法。該算法顧及了量測量中的粗差對濾波的影響,基于模糊理論構(gòu)造等價權(quán)函數(shù),利用等價權(quán)函數(shù)和模糊抗差自適應(yīng)因子調(diào)節(jié)粗差對導(dǎo)航解的影響,有效地控制粗差對導(dǎo)航解的影響。將提出的算法應(yīng)用到組合導(dǎo)航系統(tǒng)中進(jìn)行仿真驗證,結(jié)果表明,提出算法不但實時性好,而且濾波精度明顯提高。 (4)提出一種新的基于狀態(tài)相關(guān)系數(shù)的抗差自適應(yīng)濾波算法。采用狀態(tài)相關(guān)系數(shù)將非線性系統(tǒng)轉(zhuǎn)換為狀態(tài)相關(guān)系統(tǒng),在處理非線性動力學(xué)模型與量測模型時不必進(jìn)行線性化,從而減小了由線性化系統(tǒng)模型所帶來的誤差。建立抗差自適應(yīng)濾波模型,利用等價權(quán)矩陣和自適應(yīng)因子進(jìn)行信息分配,從而控制動力學(xué)模型異常和觀測異常對導(dǎo)航解的影響。仿真結(jié)果表明,提出的算法不僅能夠有效地抑制動態(tài)系統(tǒng)模型狀態(tài)噪聲和觀測噪聲干擾,而且計算簡單,濾波精度明顯優(yōu)于EKF和UKF算法。 (5)提出一種非線性抗差自適應(yīng)狀態(tài)相關(guān)黎卡提方程濾波算法。該方法采用狀態(tài)相關(guān)系數(shù)法將非線性系統(tǒng)轉(zhuǎn)換成類似線性系統(tǒng)結(jié)構(gòu),減小了由線性化系統(tǒng)模型所帶來的誤差。在一定的條件下證明了該算法的穩(wěn)定性。仿真結(jié)果表明,提出的算法不僅能夠有效地抑制非線性系統(tǒng)模型狀態(tài)噪聲和觀測噪聲的干擾,而且濾波精度明顯優(yōu)于UKF和SDRE濾波算法。 (6)現(xiàn)有文獻(xiàn)研究中,對新息向量和觀測殘差向量的協(xié)方差陣采用算術(shù)平均值估計,其估計的觀測噪聲向量協(xié)方差陣中含有狀態(tài)預(yù)測值的誤差,若狀態(tài)預(yù)測值的誤差較大,預(yù)測殘差必然大,從而由預(yù)測殘差計算的新息向量和觀測殘差向量的協(xié)方差陣的估計精度就變差。為了克服這一缺陷,本文提出用一種新的隨機(jī)加權(quán)估計算法,對觀測噪聲協(xié)方差陣和狀態(tài)噪聲協(xié)方差陣進(jìn)行估計,以控制觀測異常和動態(tài)模型噪聲異常對狀態(tài)參數(shù)估值的影響。仿真結(jié)果表明,提出的算法不僅計算簡單,而且能提高動態(tài)導(dǎo)航解算得濾波精度。 (7)提出動力學(xué)模型誤差的Sage隨機(jī)加權(quán)自適應(yīng)估計方法。該方法利用Sage濾波的開窗平滑方法,求取觀測殘差向量和預(yù)測殘差向量的協(xié)方差陣,用隨機(jī)加權(quán)因子對觀測殘差和預(yù)測殘差進(jìn)行調(diào)節(jié),以控制觀測殘差和預(yù)測殘差對導(dǎo)航解算精度的影響。仿真結(jié)果證明,提出的算法對狀態(tài)擾動帶來的誤差具有較強的抑制能力。 本文所取得的研究成果對運載器組合導(dǎo)航濾波解算、多源信息融合、誤差估計和計算機(jī)仿真等領(lǐng)域的研究都有一定貢獻(xiàn)。研究結(jié)果不但可以應(yīng)用于軍用和民用領(lǐng)域運載器導(dǎo)航定位的濾波解算,而且經(jīng)過推廣,還可以用于航空航天領(lǐng)域其它飛行器導(dǎo)航定位的濾波解算。
[Abstract]:With the rapid development of modern science and technology, the demand for the real-time and rapidity of the navigation solution of the carrier is getting higher and higher. The Calman filtering method is mainly used in the navigation solution of the carrier. When the navigation is solved by the Calman filter, the mathematical model of the dynamic system must be linear. When the system model is non line In sex, if the Calman filter algorithm is used to calculate navigation, it will cause large error and even lead to filtering divergence. In order to improve the accuracy of the integrated navigation of the carrier, it is an important and urgent research to study the high precision and nonlinear filtering algorithm which is suitable for the integrated navigation of the carrier. The task of studying.
On the basis of studying the existing navigation filtering algorithm, a set of high performance filtering algorithm suitable for carrier integrated navigation is proposed, including nonlinear model prediction Unscented particle filter algorithm, attenuated memory square root Unscented particle filter algorithm, fuzzy adaptive adaptive Unscented particle filter algorithm and state correlation system. The robust adaptive filtering algorithm, the nonlinear adaptive state dependent Riccati equation filtering algorithm and the Sage random weighted adaptive filtering algorithm for the dynamic model error are applied to the carrier integrated navigation system to be simulated and verified, and compared with the existing filtering algorithms. The results show that the algorithm is proposed. The algorithm not only has a small amount of computation, but also has high filtering accuracy, and the filtering performance is much better than the existing filtering algorithm.
The main research content and innovative contribution of this paper are as follows
(1) a new nonlinear model prediction Unscented particle filter algorithm is proposed. The algorithm takes into account the model error when establishing the system model, uses the estimated model error to modify the nonlinear model containing errors, and then uses the Unscented particle filter to solve the model. The simulation results show that the algorithm is filtered. Wave performance is better than model predictive filtering and Unscented particle filtering, which improves navigation accuracy.
(2) on the basis of studying Unscented particle filter and absorbing the advantages of attenuated memory filter and square root filter, a new attenuation memory square root Unscented particle filter algorithm is proposed. In this algorithm, the effect of the historical information on the filter is reduced by the attenuation factor and the effect of historical information on the filtering is reduced. The square root matrix of variance matrix is used for iterative calculation instead of covariance matrix, which ensures the symmetry and positive stability of the covariance matrix. The results show that the proposed algorithm can effectively improve the filtering performance and improve the accuracy of the navigation system.
(3) on the basis of the study of fuzzy control theory and absorbing the advantages of Unscented particle filter, adaptive filtering and robust estimation, a new fuzzy adaptive Unscented particle filtering algorithm is proposed. The algorithm takes into account the influence of the gross error in the measurement on the filtering, constructs the equivalent weight function based on the fuzzy theory, and uses the equivalent weight function. The effect of gross error on navigation solution is regulated by number and fuzzy tolerance adaptive factor, and the effect of gross error on navigation solution is effectively controlled. The proposed algorithm is applied to the integrated navigation system for simulation verification. The results show that the proposed algorithm not only has good real-time performance, but also improves the filtering accuracy.
(4) a new adaptive filtering algorithm based on state correlation coefficient is proposed. Using the state correlation coefficient, the nonlinear system is converted into a state related system. The linearization is not necessary when dealing with the nonlinear dynamic model and the measurement model, thus reducing the error caused by the linearized system model. The simulation results show that the proposed algorithm can not only effectively suppress the state noise and the observation noise interference of the dynamic system model, but also the calculation is simple, and the filtering precision is obviously superior to the EK. F and UKF algorithms.
(5) a nonlinear differential adaptive state correlation Riccati equation filtering algorithm is proposed. The method uses the state correlation coefficient method to convert the nonlinear system into a similar linear system structure, and reduces the error caused by the linearized system model. The stability of the algorithm is proved under certain conditions. The simulation results show that the proposed method is proposed. The algorithm not only effectively suppresses the interference of the state noise and the observation noise of the nonlinear system model, but also has better filtering accuracy than the UKF and SDRE filtering algorithms.
(6) in the existing literature study, the covariance matrix of the new interest vector and the observed residual vector is estimated by arithmetic mean, and the estimated error of the estimated value of the state of the observed noise vector covariance is that if the error of the state prediction is larger and the predicted residual is inevitable, the new interest vector and the observation residual vector are calculated from the predicted residual. In order to overcome this defect, a new random weighting estimation algorithm is proposed to estimate the observation noise covariance matrix and the state noise covariance matrix to control the effect of abnormal observation and dynamic model noise on the state parameter estimation. The simulation results show that the proposed algorithm is proposed. It is not only simple in calculation but also improves the accuracy of filtering in dynamic navigation.
(7) the Sage random weighting adaptive estimation method for dynamic model error is proposed. This method uses the window smoothing method of Sage filtering to obtain the covariance matrix of the observation residual vector and the predicted residual vector, and adjusts the observation residual and the prediction residual by the random weighting factor, so as to control the observation residual and the prediction residual to the navigation solution precision. Simulation results show that the proposed algorithm has a strong ability to suppress the errors caused by state disturbance.
The research results obtained in this paper have some contribution to the research of carrier integrated navigation filtering, multi source information fusion, error estimation and computer simulation. The research results can be applied not only to the filtering and calculation of navigation and positioning of military and civil carrier, but also in the field of aviation and aerospace. The filtering of the navigation and positioning of the aircraft is calculated.
【學(xué)位授予單位】：西北工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN96.2

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