利用卡曼濾波器整合全球定位系統及慣性量測單元之精簡模型研究

Experimental Study on Kalman Filter in a Reduced-Order Integrated GPS/IMU

黃昱傑(Yu-Chieh Huang)

指導教授 ： 成維華

國立交通大學/工學院/機械工程系所/碩士(2010年)

https://doi.org/10.6842/NCTU.2010.00710

摘要

全球定位系統(GPS)及慣性量測單元(IMU)通常以卡曼濾波器整合，而 其往往不能發揮期效益因為沒有辦法正確的建立誤差模型。長久以來，工程師試圖解析系統狀態以便找到最精確的系統估測方法，而大部分的案例都需要解析其每一個步驟並建構一個非常複雜的系統。而複雜的系統另一個缺點就是需要耗費大量的運算時間，不適合用在即時(Real-time)的應用。本論文之主旨在於建立一動態精簡模型，利用實驗的結果分析其誤差模型，並找出其最適合之變異係數及參數。從實驗結果得知，此精簡模型可有效的提高位置及速度之精確度，並可以在全球定位系統接收不良或遺失訊號時保持穩定運作，且大大減低系統成本並適合於即時系統的應用。

關鍵字

卡曼濾波器 ； 全球定位系統 ； 慣性量測單元

並列摘要

An integrated GPS/IMU system is often integrated by a Kalman filter which cannot work properly without a good error model being made. For decades, engineers have tried to decompose the system states, so as to find accurate system estimations. However, most of the case it is not easy to identify detail processing or measuring errors of individual sub modules. Another drawback of complex systems is that the high cost of computation time, it makes them not suitable for real-time applications. The aim of this article is to develop a scheme in which we can off-line identify the lump error model of reduced-order dynamic model until a minimum variance has been found in any desired situation, and then simply applying these results to Kalman filter. From experimental result, it shows that the position and velocity errors could be significantly reduced and controlled. This simple model is robust for tolerating data lose, and highly reduce the cost for real-time application.

並列關鍵字

Kalman Filter ； GPS ； IMU

參考文獻

[1] Lorga J. F. M., Rossum W. van, van Halsema E., Chu Q. P and Mulder J. A., “The Development of a SAR Dedicated Navigation System: From Scratch to the First Test Flight.”, IEEE Position and Location Symposium, Monterey Ca, USA, 2004.

Department of Transportation Office of Design, 2010.

[6] G. Welch and G. Bishop, “An Introduction to the Kalman Filter”, Department of Computer Science University of North Carolina at Chapel Hill, February 8, 2001.

[8] Shahram Rezaei, Raja Sengupta, “Kalman Filter-Based Integration of DGPS and Vehicle Sensors for Localization”, IEEE, Transactions on control systems technology, vol. 15, No. 6, November 2007.

[9] Domimick Andiusani, II, Ching-Fu Gau, “Estimation Using a Multirate Filter”, School of Aeronautics and Astronautics, Purdue University, IEEE, 1987.