本文選題：卡爾曼濾波 + 可觀測性��； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：通常情況下,目標(biāo)跟蹤問題被視為機動狀態(tài)的估計問題。卡爾曼濾波(KF)方法作為目標(biāo)跟蹤的核心技術(shù),其估計精度直接決定了機動目標(biāo)的跟蹤性能,而卡爾曼濾波的估計精度和速度則取決于系統(tǒng)可觀測性程度,因此系統(tǒng)及各狀態(tài)分量的高可觀測度(OD)是系統(tǒng)高濾波跟蹤性能的前提條件。在很多實際工程中,狀態(tài)模型、觀測模型或者是噪聲統(tǒng)計特性通常部分已知或未知,因此在直接使用該模型不可避免會導(dǎo)致濾波性能的降低或發(fā)散。雖然現(xiàn)有自適應(yīng)方法能有效解決該問題并抑制濾波發(fā)散,但是它并不能有效評估濾波性能,同時大多數(shù)自適應(yīng)濾波(AKF)方法較為主觀,無法確定系統(tǒng)自適應(yīng)濾波后的性能優(yōu)化程度。因此能用于刻畫系統(tǒng)濾波性能的系統(tǒng)可觀測度得以引入來選取自適應(yīng)調(diào)節(jié)因子,但是至今可觀測度與濾波性能間的相關(guān)性仍沒有統(tǒng)一解析關(guān)系,與此同時目前大部分的可觀測度定義都未考慮外界干擾的影響。因此為了解決上述問題,本文以當(dāng)前常用的可觀測度分析方法為理論基礎(chǔ),揭示了可觀測度與濾波性能間的內(nèi)在關(guān)系,并定義了基于卡爾曼濾波的可觀測度分析方法,最后結(jié)合濾波收斂定理定義了基于可觀測度分析的智能Kalman濾波方法。(1)可觀測度與濾波精度間的內(nèi)在關(guān)系揭示。本文從四種典型的可觀測度定義出發(fā),分別對其原理及特點進(jìn)行了剖析,并分別著眼于估計誤差協(xié)方差(EEC)法和奇異值分解(SVD)法,對該可觀測度與濾波性能間相關(guān)性進(jìn)行了解析論證。(2)基于卡爾曼濾波的可觀測度分析方法研究。本文從線性參數(shù)估計的角度出發(fā),采用加權(quán)最小二乘估計方法(WLS)構(gòu)建了基礎(chǔ)可觀測度判據(jù)矩陣,根據(jù)可觀測度與卡爾曼濾波估計精度間存在的內(nèi)在關(guān)系,改進(jìn)基礎(chǔ)可觀測度計算矩陣得到了最優(yōu)可觀測度判據(jù)矩陣,并重新定義了狀態(tài)可觀測度(LOD)和系統(tǒng)可觀測度(GOD)。(3)基于可觀測度分析的智能Kalman濾波研究。本文以自適應(yīng)反饋校正環(huán)節(jié)為整體濾波框架,以自適應(yīng)可觀測度理論為依據(jù),以濾波收斂定理為輔助條件并以迭代分析方法為研究手段對自適應(yīng)調(diào)節(jié)因子進(jìn)行優(yōu)化選取,有效地定義了基于可觀測度分析的智能Kalman濾波方法。
[Abstract]:In general, the target tracking problem is regarded as a maneuvering state estimation problem. As the core technology of target tracking, the estimation accuracy of Kalman filter (KF) method directly determines the tracking performance of maneuvering targets, while the estimation accuracy and speed of Kalman filter depend on the degree of observability of the system. Therefore, the high observability measure ODO of the system and each state component is a prerequisite for the high filtering tracking performance of the system. In many practical projects, the state model, the observational model or the statistical characteristics of noise are usually known or unknown, so the direct use of the model will inevitably lead to the degradation or divergence of filtering performance. Although the existing adaptive methods can effectively solve the problem and suppress the filtering divergence, it can not effectively evaluate the filtering performance, and most of the adaptive filtering AKF methods are more subjective. The degree of performance optimization after adaptive filtering can not be determined. So the observable measure which can be used to describe the filtering performance of the system can be introduced to select the adaptive adjustment factor, but the correlation between the observable measure and the filtering performance has not been unified analytic relation. At the same time, most definitions of observable measure do not consider the influence of external disturbance. Therefore, in order to solve the above problems, based on the commonly used observable measure analysis method, this paper reveals the inherent relationship between observable measure and filtering performance, and defines the observable measure analysis method based on Kalman filter. Finally, an intelligent Kalman filtering method based on observability analysis is defined by combining the filtering convergence theorem. The intrinsic relationship between the observable measure and the filtering accuracy is revealed. Based on the definition of four typical observable measures, this paper analyzes their principles and characteristics, and focuses on the estimation error covariance EECs method and singular value decomposition (SVD) method, respectively. The correlation between the observable measure and the filtering performance is analytically demonstrated. (2) the observable measure analysis method based on Kalman filter is studied. In this paper, from the point of view of linear parameter estimation, the criterion matrix of fundamental observable measure is constructed by using weighted least square estimation method. According to the inherent relationship between observable measure and Kalman filter estimation accuracy, The criterion matrix of optimal observable measure is obtained by improving the calculation matrix of fundamental observable measure, and the state observable measure LOD) and the observability of the system are redefined. The intelligent Kalman filter based on observability analysis is studied. In this paper, the adaptive feedback correction is taken as the global filtering framework, the adaptive observability measure theory is used as the basis, the filter convergence theorem is taken as the auxiliary condition and the iterative analysis method is used to optimize the selection of the adaptive adjustment factor. An intelligent Kalman filtering method based on observability analysis is effectively defined.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN713

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