以無線網路訊號量測強度為基礎之增加空間向度的定位演算法

Abstract

近年來行動裝置(Mobile Station)的定位已經日漸備受注目，而且重要性與日俱增。而建構在行動裝置和基地台 (Base Station) 網路架構上的定位演算法已經被廣泛應用到各個層面。傳統上的兩步求最小平方法 (Two-Step Least Square) 之定位演算法，對於定位行動裝置來說，提供了一個很有效率的解法。而另一個以幾何限制為輔助之定位 (Geometry-Assisted Location Estimation) 演算法，則將非直線路徑(None-Line-of-Sight)雜訊造成的行動裝置和基地台之相對陳列關係加入了考量。後者針對了超視距雜訊，在以前者為基礎上，多附加了幾何上的限制，增加了定位上的精確度。無論如何，前述兩者都是以到達時間 (Time-of-Arrival) 作為量測基礎，而制定的演算法。很少有人以訊號量測強度 (Received Signal Strength) ，這種很容易被今日各種行動裝置取得的訊號來源，為基礎而來設計演算法。我們因而提出了一個以寬頻無線網路訊號量測強度為基礎之增加多樣性的定位演算法 (A Diversity-Augmented Location Estimation Algorithm for RSS-Based Wireless Networks)。此一演算法同時也考慮了路徑損耗指數(Path Loss Exponent)對於訊號量測強度轉換到估測距離所造成的影響，因而採取機制去修正它。我們所提出的演算法同時保留了兩步求最小平方法的優點，也透過了訊號量測強度來做出良好而精確的定位估算。我們也提供了許多模擬結果來證明所提演算法的效能的確能勝過許多已經存在的定位演算法。

Mobile location estimation has attracted a significant amount of attention in recent years. The network-based location estimation schemes have been widely adopted based on the radio signals between the mobile device and the base stations. The two-step least square (TSLS) method has been studied in related research to provide efficient location estimation of the mobile devices. In order to enhance the precision of location estimate, the geometry-assisted location estimation (GALE) scheme is designed to incorporate the geometric constraints within the formulation of TSLS method. However, these two algorithms are mainly designed based on the time-of-arrival (TOA) measurements. There is not much effort that has been dedicated in location estimation based on received signal strength (RSS) measurements, which can be easily obtained by mobile devices nowadays. A diversity-augmented location estimation (DALE) algorithm is proposed in this thesis with additional spatial assistance based on the RSS measurements. This algorithm also considers and corrects the effect of incorrect path loss exponent (PLE). The proposed DALE scheme can both preserve the computational efficiency from the TSLS algorithm and obtain precise location estimation based on RSS measurements. Numerical results demonstrate that the proposed DALE algorithm can achieve better accuracy, comparing with other existing schemes, in mobile location estimation.