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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/18192

Title: 基於新訊息為基礎之適應性導航濾波器
Innovation Based Adaptive Filter Designs For Navigation Applications
Authors: Jia-ming Huang
Contributors: NTOU:Department of Communications Navigation and Control Engineering
Keywords: 卡爾曼濾波器;自適應濾波器
Kalman;IAE;adaptive filter
Date: 2005
Issue Date: 2011-07-04
Abstract: 摘 要 使用卡爾曼濾波於動態位置和導航,需要建立觀測和動態模型。兩種功能之模型可包含全球及當地系統誤差。而自適應性濾波之方法可應用於改善觀測量和動態模型之系統誤差。 本論文簡明的描述自適應濾波觀測新訊息向量和動態修正量之分析。而自適應濾波器中的IAE(Innovation-based adaptive estimation)法即自適應濾波器利用先前訊息的平均來估測目前時刻的系統雜訊方差和觀測雜訊方差,從而使系統雜訊方差和觀測雜訊方差自適應於目前動態訊息和觀測訊息。由於自適應性濾波器之方法在於權重值取拮於量測值和狀態方程式之狀態參數,對於先前不穩定狀態之結果及不穩定的動態功能夠被穩定控制。藉此來改善卡爾曼濾波器之不足,但是IAE法無法有效的控制狀態異常時對估測值的影響,因此引用了能夠整體控制誤差協方差之方法於GPS及DME站台定位方面加以改善。
Abstract To use Kalman filtering for kinematic positioning and navigation, we have to deal with both observational and kinematic models. Both of the functional models may contain global or local systematic errors. The influence functions of the systematic errors on the estimates of kinematic states are derived. An adaptive fitting method for systematic error of the observations and kinematic model errors is presented. In this paper a brief review of adaptive filtering is followed by an analysis of the short comings of covariance matrices formed by windowing residual vectors, innovation vectors and correction vectors of the dynamic states. An adaptive filter usually applied in dynamic geodetic positioning is the IAE(Innovation-based adaptive estimation). It uses the residual or innovation vectors from historical epochs to evaluate the measurement precision of the present epoch, and the residuals from the predicted state parameters of historical epochs to estimate the precision of the predicted state parameters. Usually the IAE works well if the states and measurement errors are stable. In this case the windowing method can give reasonable covariance matrices of the measurement vectors and the predicted states. The authors introduced an adaptive factor to balance the weights between the measurements and the predicted state parameters from the state equations, by which the bad effects of the unstable prior states predicted by the dynamical function can be controlled. An initial weight matrix or covariance matrix of the predicted state at present epoch is needed for the adaptive filtering. Improve the deficiency of Kalman filter by this, to the influence estimating the examining value when but IAE is unable the effective state of a control is unusual, so quoting can control association's variance matrix of miscellaneous news of the trends and make the method that location improve in GPS and DME platform wholly.
URI: http://ethesys.lib.ntou.edu.tw/cdrfb3/record/#G0M93670008
Appears in Collections:[通訊與導航工程學系] 博碩士論文

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