慣性測量單元其體積小且價格便宜，已成為室內定位不可或缺的一部份。為了提供精度準確且能供室內定位使用的服務，行人航位推算是目前多數人致力於研究的技術，然而，此方法不僅容易受到環境及噪聲影響造成誤差累積，也受限於許多變數，例如使用者身高或是手機攜帶方式，目前仍沒有有效的技術克服此缺陷。
為了解決這些問題，本論文提出一個慣性深度神經網路之架構，基於長短期記憶網路結合自注意力機制，並且使用不確定性加權優化模型，藉由平移與旋轉的相對姿態估計行人軌跡。最重要的是，該方法不需要使用者個人信息，也不受限於設備攜帶方式，便可以直接利用慣性測量單元獲得之六維數據，重建準確且可靠的運動軌跡。並且，它不只能用於週期性運動模式，也能用於非週期性運動軌跡。

The inertial measurement units have become an indispensable part of indoor positioning owning to their characteristics of smaller size and cheaper price. To fulfill the objective of making indoor positioning more accurate, pedestrian dead reckoning recently has become a technology that most people are researching. However, this method is susceptible to cumulative error caused by numerous variables such as environmental impacts, noise effects, even the user's height or the ways to carry cellphone. Thus, currently there is still no efficacious technique to overcome this shortcoming.
In order to ameliorate this situation, this thesis proposes an inertial deep neural network architecture, based on a Long Short-Term Memory combined with a self-attention mechanism, uses an uncertainty-weighting optimization model to estimate pedestrian trajectory by the relative posture of translation and rotation. Above all, this method does not require user's personal information, nor be limited to the mobile phone the way users carry. Therefore, not only both periodic and non-periodic can be used, but it can directly apply the six-dimensional data obtained by the inertial measurement units to reconstruct an accurate and reliable movement trajectory.

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