在本篇論文中，我們研究如何在無線感測器網路的環境下，建造出一個可以估測高斯馬可夫行動模型參數的估測器。我們提出了一個可以線上更新的估測器GMPE，可以在少量的訊息傳輸下估測參數。我們在電腦模擬中將GMPE應用在物體位置預估上與另外兩種只需少量訊息傳輸的物體位置預估方法做比較，這兩種方法分別是instant和exponential。結果顯示應用GMPE來做位置預估的方法比起其他的方法有較高的準確率。

In this thesis, we consider the problem of developing a Gauss-Markov parameter estimator in wireless sensor network. To this purpose, we propose an on-line method (GMPE) to estimate Gauss-Markov parameters with little requirement in terms of communication overhead of messages. We compared by simulations the performance of GMPE prediction model with the instant and the exponential average prediction models. Simulations showed that GMPE prediction model achieves more accurate prediction on the location of the object in Gauss-Markov mobility model as compared to the instant and the exponential average prediction models.

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