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博碩士論文 etd-0831111-202102 詳細資訊
Title page for etd-0831111-202102
論文名稱
Title
行動感測網路中考量覆蓋範圍之目標追蹤適應性群聚演算法
Adaptive Flocking Algorithm with Range Coverage for Target Tracking in Mobile Sensor Networks
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
60
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2011-08-16
繳交日期
Date of Submission
2011-08-31
關鍵字
Keywords
卡爾曼一致性資訊濾波器、代價函數、網路覆蓋範圍、目標物追蹤、群聚演算法
cost function, Kalman-consensus information filter, flocking algorithm, target tracking, network coverage
統計
Statistics
本論文已被瀏覽 5651 次,被下載 643
The thesis/dissertation has been browsed 5651 times, has been downloaded 643 times.
中文摘要
行動感測器形成群聚並對目標物進行定位追蹤的議題上,目標物的定位精準度與感測網路的面積覆蓋範圍是兩個互相影響的因素,當一群聚之移動感測器網路對監測環境擁有較大的覆蓋面積時,可以增加量測目標物位置狀態與環境資訊的範圍,同時可以將資訊傳給不屬於此群聚之感測器或是需要此目標物狀態資訊的請求源;然而當感測器離目標物較遠時,目標物位置量測值易受距離因素影響。本論文探討基於群聚覆蓋面積與目標物位置估測精準度的群聚演算法。群聚演算法的靈感來自於自然界動物間的行為,每一個個體皆與鄰居個體互相溝通分享資訊,為了達成整個群體的目標,進而形成由許\多個體構成的一個大群聚。群聚演算法在目標物追蹤之應用上,通常將移動式感測器或自走式載具作為群聚裡的個體,對目標物進行追蹤;在探討每個行動感測器藉由與鄰居感測器合作交換訊息的方式,達成群聚且彼此間保持適當距離的前提下,設計移動式感測器分散式的控制方法,並適當給予每個行動感測器移動速度,使此群聚的感測器能夠追上目標物且保持群聚形狀。本論文基於目標物位置之估測精準度與網路面積覆蓋,建立這兩個因素考量下的代價函數 (cost function),每個感測器根據此代價函數適應性地調整與通訊範圍內所有鄰居感測器之間距,成為結合資訊量與鄰間距離改變量之適應性群聚演算法。如此一來,離目標物較近之感測器將拉近與目標物以及周圍之鄰居感測器的距離,使這些感測器能夠得到較好的量測值,由卡爾曼一致性資訊濾波器 (Kalman-consensus information filter) 進而使整體網路對目標物之位置估測精準度能夠有所提升。另一方面,在外圍的感測器則是負責拉大整體的網路面積,實現能同時兼顧定位效能與網路面積覆蓋的群聚演算法。本論文最後以程式模擬,在距離-角度之量測模型下,使用卡爾曼一致性資訊濾波器在每個行動感測器上對目標物進行位置狀態估測,在約略相同的網路覆蓋面積下,由定位效能優劣判斷之均方根誤差值的比較,驗證本論文提出之方法相較於其他群聚演算法,能夠有較好的定位效能。
Abstract
The accuracy of target location and the coverage range of sensor network are two factors that affect each other in target tracking. When the flocking sensor network has a larger coverage area, it can increase the range of detecting target and the scope of environmental information. The network can also pass the information to a query source or other sensors which do not belong to the flocking network. However, the accuracy of measurements at sensors may be affected by the distances between the target and the sensors. We use mobile sensors as agents in flocking algorithm for target tracking. Every mobile sensor exchanges information with its neighbors, and keeps an appropriate separation distance with neighbors to maintain flocking. Flocking algorithm is a distributed control method for mobile sensor which can catch up the target and maintain flocking formation. In the thesis, we derive the cost function based on the accuracy of target positioning and range coverage. The proposed adaptive flocking algorithm combines the amount of information and the distance changes between neighbors based on the cost function. Each mobile sensor adaptively adjusts distance separation with all its neighbors within communication range. Sensors closer to the target shortens the separation distance between neighbors, therefore they will move toward the target and obtain better measurement. Kalman-consensus information filter is used for target positioning. The accuracy of target position can therefore be improved in the overall network. On the other hand, the sensors located far from the target will widen the distance separation between neighbors to expand the overall network area. In the thesis, we use Kalman-consensus information filter to estimate the state of a target, and use adaptive flocking algorithm for maintaining the formation of mobile sensors. Simulations show that adaptive flocking algorithm effectively improves location accuracy while maintaining approximate generally same coverage area when compared with other methods.
目次 Table of Contents
誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
圖次. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
表次. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
符號說明. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
1 緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 研究背景. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 行動感測網路與群聚演算法. . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1 行動感測網路. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 群聚演算法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2.1 行動感測器運動模型. . . . . . . . . . . . . . . . . . . . . . . 6
2.2.2 鄰接感測器集合與群聚幾何圖形. . . . . . . . . . . . . . . . . 7
2.2.3 平滑鄰接矩陣. . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.4 集體位場函數. . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.5 群聚演算法. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 群聚演算法應用於目標追蹤議題. . . . . . . . . . . . . . . . . . . . . 12
2.3.1 目標物過程模型與感測器觀察模型. . . . . . . . . . . . . . . . 12
2.3.2 卡爾曼濾波器. . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.3 卡爾曼一致性資訊濾波器. . . . . . . . . . . . . . . . . . . . . 16
3 基於定位精準度與網路覆蓋範圍考量之適應性群聚演算法. . . . . . . . . . . 19
3.1 基於量測值之感測器觀察模型. . . . . . . . . . . . . . . . . . . . . . 19
3.2 基於定位精準度之代價函數. . . . . . . . . . . . . . . . . . . . . . . 20
3.3 基於網路覆蓋面積考量下之代價係數設計. . . . . . . . . . . . . . . . 24
3.4 整體群聚感測網路之代價. . . . . . . . . . . . . . . . . . . . . . . . . 27
3.5 適應性群聚演算法流程. . . . . . . . . . . . . . . . . . . . . . . . . . 30
4 群聚感測網路模擬及分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.1 模擬環境與參數. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2 定位效能與感測網路覆蓋面積範圍比較. . . . . . . . . . . . . . . . . 35
5 結論與建議. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.1 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2 建議. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
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