應用於優化空間之特徵指紋定位技術 - 利用族群搜尋之方法

Location Fingerprinting in the Information-Refined Space - A Population-based Search Approach

陳震謙(Jen-Chian Chen)

指導教授 ： 林宗男

國立臺灣大學/電機資訊學院/電信工程學研究所/碩士(2009年)

https://doi.org/10.6342/NTU.2009.02470

摘要

無資料

關鍵字

特徵指紋定位技術 ； 粒子族群優化法 ； 多元區別分析 ； 轉換法 ； 族群搜尋方法

並列摘要

With the demand of location-based services (LBS) increasing, the
higher accurate positioning system is needed. Location Fingerprinting
(LF) plays an important role in localization, especially for indoor environments.
LF system is based on pattern matching and divided into
offline stage and online stage. Before constructing the offline model
of LF, the Received Signal Strength (RSS) measurements can be
picked or reorganized by some techniques. Besides the channel selection
method, the transformation method reduces the online computation
complexity and improves the positioning performance. The
transformations can reorganize the RSSs from wireless channels and
project RSSs into a space where the information is refined. This
thesis provides the the theoretical and experimental comparison between
two classical transformations, Multiple Discriminant Analysis
(MDA) and Principle Component Analysis (PCA). More, we adopted
an population-based approach to search the expected transformation
to be better than the others. The method Particle Swarm Optimization
(PSO) puts the transformations in the space in an evolutionary
way to search the optimum information. We conduct the experiments
in indoor and outdoor environments, which are the NTUEE building
and NTU campus based on WLAN networks and the GSM infrastructure ture respectively. The results show that MDA is better than PCA in
addition to theoretical analysis. The results show that the proposed
method reduces 20.09∼56.87% and 15.57∼56.54% of the mean error
and 67% circular error probable (CEP) for only six channels in
indoor environments, respectively, as compared to classical transformation
methods. More, the outdoor experimental results show that
it also reduces 7.23∼28.80% and 7.61∼29.10% of the mean error and
67% CEP.

並列關鍵字

Location fingerprinting ； particle swarm optimization ； Multiple Discriminant ； Analysis ； Transformation ； Population-based search

參考文獻

[1] K. Axel, Location-Based Services : Fundamentals and Operation. John Wiely & Sons, 2005.

[3] S. Zafer, G. Sinan, and G. Ismail, Ultra-wideband Positioning Systems: The-oretical Limits, Ranging Algorithms, and Protocols. Cambridge, 2008.

[4] J. Hightower and G. Borriello, “Location systems for ubiquitous computing,” Computer, vol. 34, no. 8, pp. 57–66, Aug 2001.

[5] F. Gustafsson and F. Gunnarsson, “Mobile positioning using wireless net-works: possibilities and fundamental limitations based on available wireless network measurements,” IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 41–53, 2005.

[6] M. B. Kjargaard, G. Treu, and C. Linnhoff-Popien, “Zone-based rss reporting for location fingerprinting.” vol. 4480, pp. 316–333, 2007.