近些年來，由於無線感測網路的發展，使室內無線定位的技術與應用成為當
今熱門的議題。針對這方面的研究，以往利用接收訊號強度，來實現定位的研究。
在實際的室內環境下，無線電訊號會被障礙物干擾、反射、散射、多重路徑因素
影響，導致訊號接收信號強度不是一個很穩定的精確值，致使造成定位的誤差。
因為這樣的原因，本論文利用相對位移資訊來輔助室內無線定位，並以非線性濾
波器進行整合的工作。
在論文中重點可分為兩大部份，第一部分為雷射測距儀原理以及相對位移的
解法，利用最近點疊代法本身為掃描對齊概念來求出相對位移的資訊，第二部份
為非線性濾波器整合相對位移資訊與無線定位的結果，並使用兩種非線性濾波器
做比較，分別利用擴展式卡爾曼濾波器以及粒子濾波器。
經由實驗的驗證，本論文將相對位移資訊與室內無線定位系統做整合，在定
位精度的效果上，的確比單純根據接收訊號強度與距離模型做曲線近似的定位方
法來的好。在粒子濾波器能得到比擴展式濾波器更良好的定位結果，有效降低室
內環境中接收訊號強度的不穩定性，來提升定位的精度。

In recent years, indoor wireless positioning technologies and applications of a
wireless sensor network have become a hot topic due to wireless sensor network had
been developed. Formerly RSSI is used for positioning. In actual environment, radio
signals are usually influenced with obstacle which produce reflection, scattering,
multipath effect. This results in RSSI unstable data which cause position error. In this
thesis take advantage of the relative displacement information to assist indoor
wireless positioning, and use nonlinear filter algorithms for WSN and relative
displacement information integration.
In the first part of the thesis, the principles of LASER RANGE FINDER as well
as relative displacement method are discussed, iterative closest point of scan matching
method find relative displacement information. In the second part of thesis,
integration of relative displacement information and wireless positioning. This thesis
use two kinds of nonlinear filters, compare with the extended Kalman filter and
particle filter respectively.
Based on the result of this study, integration of relative displacement information
and wireless positioning system can provide better positioning accuracy compared
with conventional curve fitting method to obtain receive signal strength/distance
model. Furthermore, Particle filter can more accurate positioning result than extended
Kalman filter, alse reduce the effect of unstable RSSI and improve positioning
accurate in the indoor environment.

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