English  |  正體中文  |  简体中文  |  Items with full text/Total items : 28588/40619
Visitors : 4118573      Online Users : 65
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/17972

Title: 類神經網路動態補償於濾波器估測之誤差修正
Neural Networks-based Dynamic Compensation for Improving Filtering Estimation Accuracy
Authors: Chia-Hsin Lin
Contributors: NTOU:Department of Communications Navigation and Control Engineering
Keywords: 動態補償;卡爾曼濾波器;全球定位系統;類神經網路
Dynamic compensation;Kalman filter;GPS;neural network
Date: 2003
Issue Date: 2011-07-04
Abstract: 傳統卡爾曼濾波器(Kalman filter)估測目標的運動狀態,必須事先知道環境中雜訊的統計量及確切的系統狀態描述,才能使卡爾曼濾波器得以最佳化。一個以等速或PV模型描述的系統程序,卡爾曼濾波器足以精確的追蹤穩定速度之目標,然而對於突然具有加速度運動之機動目標,一般之追蹤技術皆無法測知進而加以補償。基於卡爾曼濾波器的限制,將利用類神經網路來補償卡爾曼濾波器於一個等速運動之目標,突然具有加速度運動之機動目標,而無法繼續鎖定追蹤,造成脫鎖(miss-tracking)之誤差補償修正,本論文使用時間延遲類神經網路(TDNN)與模糊化類神經網路(ANFIS)輔助卡爾曼濾波器於動態誤差補償上有不錯的效能。卡爾曼濾波器結合GPS求解定位,以模擬方式來驗證此方法的可行性,並與傳統的卡爾曼濾波器進行比較。 關鍵字:動態補償、卡爾曼濾波器、全球定位系統、類神經網路
The Kalman filtering theory plays an important role in the fields of navigation filter designs. For obtaining optimal (in the viewpoint of minimum mean square error) estimate of the system state vector, the designers are required to have exact knowledge on both dynamic process and measurement models, in addition to the assumption that both the process and measurement are corrupted by zero-mean Gaussian white noises. The standard GPS Kalman filter, when employing the constant velocity (CV) or Position-Velocity (PV) process model, will be able to track a target with constant speed adequately. However, when an abrupt maneuver occurs or when the acceleration of a maneuvering vehicle can not be ignored, the filtering solution will be very poor or even diverge. To avoid the limitation of the Kalman filter, the neural network can be incorporated into the filtering mechanism as dynamic model corrector. As a dynamic model corrector, neural network will identify the real-time nonlinear dynamics errors when the modeling of uncertainty is considered. The partially unknown part of the dynamics is identified by the neural network and the modeling error will be compensated. In this thesis, the Time-Delay Neural Networks and Adaptive Network-Based Fuzzy Inference System approaches will be employed for identifying the dynamics errors so as to aid the GPS Kalman filter and therefore reduce the tracking error during substantial maneuvering. Simulation is conducted and a comparative evaluation based on the neural network aided Kalman filter and conventional Kalman filter is provided. KEYWORDS 1.Dynamic compensation 2.Kalman filter 3.neural network 4.GPS
URI: http://ethesys.lib.ntou.edu.tw/cdrfb3/record/#G0M91670002
Appears in Collections:[通訊與導航工程學系] 博碩士論文

Files in This Item:

File Description SizeFormat

All items in NTOUR are protected by copyright, with all rights reserved.


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback