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研究生:吳建樟
研究生(外文):Jack
論文名稱:利用追蹤演算法與K-平均分群法在群組式感測網路下之目標追蹤
論文名稱(外文):Synthesis of Target Tracking with K-means Clustering Algorithm in Clusters of Sensor Networks
指導教授:王冠智王冠智引用關係
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:中文
論文頁數:63
外文關鍵詞:Sensor networkHandoff schemeParticle filterK-means clusteringNLOS
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本文提出以群組式感測網路作動態目標之追蹤,此方法為降低感測網路其感測器的耗能性,並維持位置追蹤之精確性兩者兼得之技術;此技術是以RSSI 及TOA兩種測量方法為基礎所產生之架構。本文的目標追蹤技術是利用粒子濾波器(Particle filter)進行最佳路徑之估算,其所需之測量值,則透過感測網路得知。然而,考慮到非視線傳播路徑(Non-Line-of-Sight)及測量誤差對於測量方法所帶來之影響,其效應會產生嚴重的測量誤差,進而導致目標路徑的估算錯誤之情形。因此,本文利用K-means分群方法對測量結果作篩選之行為,將此篩選結果配合粒子濾波器,得到較佳的目標追蹤效果。而群組式感測網路之應用是搭配換手機置的技術,將感測網路作群組式的切換,以達到有效利用感測器之目的。模擬結果顯示其所使用的方法對於路徑狀態的估算具有良好的收斂性、穩定性及感測器有效利用之要求,最重要的是降低了非視線傳播路徑對於路徑追蹤之影響。
Generally speaking, sensor network has the problems of system burden, complexity, energy consumption and accuracy of the location estimation. For these reasons, this thesis proposed a technique for moving target tracking and the handoffs between clusters of sensor network. The proposed architecture can track trajectories of target precisely, and use sensors more efficiently, even achieving low energy consumption; the technique is based on the received strength indication (RSSI) and time of arrival (TOA) measurements. This paper utilizes the particle filter (PF) to estimate the trajectories of moving targets by collecting the information from sensors measurements. However, consider the effect of non-line-of-sight (NLOS) which causes a serious error in trajectories estimation, the k-means clustering is a simple method to be utilized during measurements selection stage. The handoff decision is implemented on clusters of sensor networks, being switching through RSSI measurements. Through analysis it demonstrates that the proposed algorithm can make use of sensor efficacy and enhance the accuracy of target tracking. Furthermore, the method of k-means clustering can mitigate the notorious effect of NLOS propagation error in location estimation.
摘 要
ABSTRACT
致 謝
CONTENTS
LIST OF TABLES
LIST OF FIGURES
CHAPTER 1 INTRODUCTION
CHAPTER 2 FUNDAMENTAL CONCEPTS
2.1 Introduction of Sensor Networks
2.2 Location Estimation
2.2.1 Localization
2.2.2 Tracking
2.3 Source of Estimation Error
2.3.1 Multipath Propagation
2.3.2 Non-Line-of-Sight Propagation
2.4 Optimal Estimation
2.4.1 Recursive Bayesian Filter
2.4.2 Kalman Filter
2.4.3 Extended Kalman Filter
CHAPTER 3 TARGET TRACKING ALGORITHM
3.1 State Assignment
3.1.1 Target Model
3.2 Tracking Algorithm
3.2.1 Particle Filter
3.2.2 Clusters of Sensor Network
3.3 Tracking Algorithm with Clustering Method
3.3.1 K-means Clustering
3.3.2 Particle Filter with K-means Clustering
CHAPTER 5 SUMMARY AND CONCLUSION
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