利用感測器輔助可適應環境變因的無線網路室內定位系統

Abstract

利用環境中既有的無線網路設備來推算使用者位置座標的室內定位系統，經過這幾年的研究，已充份顯示其低成本、高準確率的優勢。然而，仍然有二個主要的技術問題未被解決。第一個是，該系統的定位準確率容易受到環境變因的變化而影響；第二個則是佈建及調校該系統需要花費大量的時間。為了解決這二個問題，本論文找出了三項在室內定位系統中容易干擾定位準確度的環境因素(包括了門的開關、溼度、以及人群的聚集)並提出了一個以感測器輔助之環境調適方法。該方法利用環境感測器及接近感測器使室內定位系統能夠自動適應環境變因的改變。除此之外，我們提出了一個協同演算法，利用鄰近的使用者彼此的資訊來增加在人群聚集處的定位準確率。實驗結果顯示，當環境變因不斷改變時，比起不具環境調適性及協同演算法的一般方式，經由我們演算法改進後，系統可增加43.7% ~ 236.6%定位準確率。

Wi-Fi based indoor location systems have been shown to be both cost-effective and accurate, since they can attain meter-level positional accuracy by using existing Wi-Fi infrastructure in the environment. However, two major technical challenges persist for current Wi-Fi based location systems: instability in positional accuracy due to changing environment dynamics, and the need for manual online calibration during site survey. To address these two challenges, three environment factors (doors, humidity, and human cluster) that can interfere with radio signals and cause positional inaccuracy in the Wi-Fi location systems are identified. Then, we propose a sensor-assisted adaptation method that employs environment and proximity sensors to adapt the location systems automatically to the changing environment dynamics. In addition, a collaborative method is applied to leverage more accurate location information from nearby neighbor nodes to enhance the positional accuracy of a human cluster. Experiments were performed on the sensor-assisted adaptation and collaboration methods. The experimental results show that our enhancement can avoid adverse reduction (43.7% ~ 236.6%) in positional accuracy that can often occur in conventional non-adaptive & non-collaborative methods under changing environment dynamics.