卡爾曼濾波器是一種最佳遞迴式狀態估測演算法，能從受雜訊影響的量測值中，有效的估測線性動態系統的狀態；理想的卡爾曼濾波器必須建立在準確的系統動態模型、動態模型為線性且雜訊必須是平均值為零之白雜訊，方可得到最佳的估測結果。然而真實環境中，系統表現往往為非線性，也就是系統狀態方程式以及量測方程式皆為非線性模型，此時卡爾曼濾波器已無法滿足高估測精度的需求，因此若要在真實動態系統中，得到精度較高的狀態估測值，則必須仰賴非線性濾波器理論來進行系統狀態估測。
本論文以三種目前已廣泛被使用的非線性濾波理論於動態的室內環境中，對無線感測器網路(WSN)整合方位推估法(DR)之室內定位進行模擬分析，包括了擴展型卡爾曼濾波器(EKF)、無跡卡爾曼濾波器(UKF)，以及粒子濾波器(Particle filter)。在系統架構方面是選以二座ZigBee無線基地台透過接收訊號強度指標(RSSI)來獲取定位物體的絕對位置訊息並以一陀螺儀及一加速度計來獲取定位物體的相對位移做為觀測量來進行移動物體真實位置、接收信號強度指標參數及其它相關狀態的估測。由模擬結果可得知，在動態的室內環境中，粒子濾波器能獲得比擴展型卡爾曼濾波器以及無跡卡爾曼濾波器更為優良的定位效果及狀態估測值，有效的降低室內動態環境中，因RSSI參數變動下，造成的觀測量不穩定之影響。

The Kalman filter is a recursive optimal filter that estimates the

States of a linear dynamics system, efficiently from a series of noisy

measurement. An ideal Kalman filter can lead to an optimal filter when

dynamic model of system is completely known, linear and noise must

be white with zero mean. However in reality, linear systems do no really

exist, that means system’s state space equation and measurement

equation are all nonlinear. In this case, Kalman filter can not suffice us

for precise positioning. If we want to resolve this kind of problem,

nonlinear filter estimation must to be used for higher localization

accuracy.

In this thesis, three methods of nonlinear estimation algorithm

including Extended/Unscented Kalman Filter and Particle Filter

are applied for simulating WSN/DR integration positioning in dynamic

indoor environment . In the framework of the system, two ZigBees and

two inertial navigation sensors were used to obtain measurement

informations for estimating the position of moving body, parameters

of ZigBee’s RSSI and other relating states.

Simulation results show that Particle Filter can estimate the states

of the system more accurate than EKF and UKF, efficiently reducing

the effect of unstable RSSI measurements in the dynamic indoor

environment.

[1] R. Want, A. Hopper, V. Falco and J. Gibbons,“The Active Badge Location
System,”ACM Transaction on Information Systems, Vol. 40, No.1, pp. 91-
102, January 1992
[2] A. Ward, A. Jones and A. Hoper,“A New Location Technique for the Active
Office, ”IEEE Personal Communications, Vol. 4, No. 5, pp. 42-47, October
1997
[3] R. Want, J. Hightower and G. Borriello, “SpotON：An Indoor 3D Location
Sensing Technology Based on RF Signal Strength,”UW CSE Technical Report
#2000－02－02, February 18, 2000
[4] S. Tennina, M. Di Renzo, F. Graziosi, and F. Santucci,“Locating ZigBee®
Nodes Using the TI®s CC2431 Location Engine：A Testbed Platform and New
Solutions for Positioning Estimations of WSN’s in Dynamic Indoor
Environments”in Proceedings of the first ACM international workshop on
Mobile entity localization and tracking in GPS-less environments, San
Francisco, pp. 37-42, 2008
[5] R. G. Brown and P. Y. C Hwang,“Introduction to Random Signals and
Applied Kalman Filtering,”John Wiley&Sons, New York, 3rd 1997
[6] D. Simon,“Optimal State Estimation, Kalman, H∞, and Nonlinear
Approaches, ”John Wiley & Sons, 2006
[7] S. J. Julier, J. K. Uhlmann,“A New Extension of the Kalman Filter to
Nonlinear Systems, ”In the Proceedings of AeroSence：The 11th
International Symposium on Aerospace/Defense Sensing, Simulation and
Controls, Multi Sensor Fusion, Tracking and Resources Management II,
SPIE, 1997
[8] E. A. Wan, R.V. D. Merwe,“The Unscented Kalman Filter for Nonlinear
Estimation, ”Adaptive Systems for Signal Proceeding, Communications, and
Control Symposium 2000. AS-SPCC. The IEEE 2000
[9] N. J. Gordon, D. J. Salmond, A. F. M. Smith,“Novel Approach to
Nonlinear / Non-Gaussian Bayesian State estimation, ”IEE Proceeding-F,
Vol. 140, No. 2, April 1993
[10] A. J. Haug, “A Tutorial on Bayesian Estimation and Tracking Techniques
Applications to Nonlinear and Non-Gaussian Process,”MITRE Technical
Report, January 2005
[11] A. Doucet, N. de. Freitas, N. Gordon,“Sequential Monte Carlo Methods in
Practice, ”Statistics for Engineering and Information Science, 2001
[12] K. Aamodt, “CC2431 Location Engine, ”Application Note ANO42, Chipcon
Products from Texas Instruments
[13] A. F. M. Smith, A. E. Gelfand,“Bayesian Statistics without Tear：A
Sampling-Resampling Perspective, ”Amer. Stat, 46. pp. 84-88, 1992.
[14] 許正忠，以Particle Filter實現分散式救援機器人之ZigBee訊號
定位系統，國立交通大學電機與控制工程學系碩士論文，2009
[15] 沈子貴，以可程式系統晶片發展平台實現無線網路定位之分析與應用，國立成功大學
電機工程研究所碩士論文，2005
[16] 邱永昌，相對位移感測器輔助室內無線定位系統，國立台灣科技大學機械工程研究所
碩士論文，2004
[17] 許巽堯，無線感測網路室內自動化定位系統研究，國立台灣師範大學機電科技研究所
碩士論文，2008
[18] 黃良吉，GPS 與感測器整合於三維地面車輛定位之應用，國立台灣科技大學機械工程
研究所碩士論文，2006
[19] 郭侃誠，結合GPS與INS之實驗船定位系統研究，國立台灣科技大學機械工程研究所碩
士論文，1996
[20] 劉建良，感測器於個人定位之步行分析與應用，國立台灣科技大學機械工程研究所碩
士論文，2008
[21] 張貽荏，非線性濾波器於GPS接收機追蹤迴路之設計，國立台灣海洋大學通訊與導航
工程學系碩士論文，2009
[22] 沈葦倫，GPS/INS整合系統於即時車輛導航之應用，國立交通大學電機與控制工程學
系碩士論文，2003
[24] 羅貽騂，利用UKF發展GPS/INS整合式定位演算法之評估，國立成功大學測量及空間資
訊學系碩士論文，2008
[25] 賴盈霖，結合GPS/重力感測器/陀螺儀 DR功能進駐導航裝置，新電子，2008，1月
[26] 陳倫斌，加速度計與電子羅般於行人定位之應用，台灣科技大學機械工程研究所碩士
論文，2009