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Title: 類神經網路於GPS接收機追蹤迴路設計
GPS Receiver Tracking Loop Designs using Neural Networks
Authors: Chi-Shui Chang
Contributors: NTOU:Department of Communications Navigation and Control Engineering
Keywords: 全球定位系統;卡爾曼濾波器;載波追蹤迴路;類神經網路;都卜勒效應
GPS;Kalman filter;Carrier tracking loop;Neural Network;Doppler effect
Date: 2003
Issue Date: 2011-06-27T07:40:46Z
Abstract: 本篇論文利用GPS接收機內部的鎖相迴路理論進行分析,並針對載波追蹤迴路濾波器之設計,以類神經網路輔助卡爾曼濾波器於動態誤差時的補償。卡爾曼濾波器是一個最佳的濾波器,其條件除要求系統狀態向量及量測值雜訊皆為零均值(zero-mean)之高斯白雜訊(Gaussian white noise)外,亦須掌握精確之系統數學模型以及雜訊之統計特性,方能獲得理想之濾波效果。當應用卡爾曼濾波器進行狀態估測時,必須明確掌握系統的狀態空間模型,而在許多現實的系統中,大部份皆屬非線性且難以明確地描述其狀態空間模型,同樣地,單純的GPS接收機載波追蹤迴路系統的數學模型之建立,無論濾波間隔選擇多少,系統的數學描述都應該是非線性的。在此部份借由類神經網路的適應性學習能力,以補償卡爾曼濾波器動態模型之不確定性,使得GPS訊號不會因都卜勒效應產生的動態誤差,而導致訊號失鎖。
The objective of this thesis is to utilize the neural networks aided Kalman filter on the design of GPS carrier tracking loop. The Neural Network is employed for compensating the dynamic modeling errors. In addition to the assumption that both the process and measurement are corrupted by zero-mean Gaussian white noises, the designers are required to have good knowledge on both dynamic process and measurement models to achieve good Kalman filtering solutions. Therefore, for obtaining good estimation on the state parameters the state space model needs to be clearly understood. In fact, most of the systems in the real world are non-linear and difficult to describe its state space model. In the meantime, the mathematical model of the carrier tracking loops in the GPS receiver should be non-linear no matter how small the sampling interval is selected. To resolve the problem mentioned above the adaptable learning ability based on the neural networks will be used. The well trained neural networks will be able to compensate the uncertainty of Kalman filter, and to prevent GPS signal being unlocked due to the dynamic modeling error caused by Doppler effect.
URI: http://ethesys.lib.ntou.edu.tw/cdrfb3/record/#G0M91670001
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