本論文探討群組式感測器網路中的目標物定位追蹤議題，在大範圍的感測器網路中，為避免感測器在傳送資料的過程消耗過多能量及龐大資料量造成處理中心沉重的計算負擔，將感測器群組化是常見的解決方式。群組內的量測資料經由群組領頭(cluster head) 融合後再傳至處理中心，可有效解決上述兩問題，亦可減少資料產生碰撞的機會。目前大多數文獻探討的範圍為所有感測器皆隸屬於單一群組，也就是群組範圍沒有重疊的情況，如此可避免感測器的量測資料被重複處理，導致整體效能下降。部分文獻探討群組範圍重疊的狀況，並將位於重疊區域的感測器運用在協助群組之間的溝通與系統同步等方面，而量測資料同時傳送給多個群組的問題尚未有文獻討論過。本論文提出一個規則散佈的群組式感測器網路，群組範圍有部分重疊，並假設感測器網路由兩種不同的感測器組成： (1) 高效能感測器，此種感測器數量較少，負責擔任群組領頭，處理群組內部資料與對外溝通，鄰近的群組領頭可互相通訊； (2) 一般感測器，此種感測器數量眾多，只需具備基本的量測及通訊功能即可。我們對群組領頭及感測器分別設定幾種操作模式，使感測器網路能夠運作得更有效率，並在超寬頻(ultra-wideband, UWB) 環境中模擬，驗證感測器網路在此設定下擁有較佳的穩定性及效能。在本論文提出的架構中目標物可能出現在群組範圍重疊的區域，產生資料重複處理的問題，我們透過 Cholesky 分解法，轉換部分群組領頭的量測方程式，將原先存在相關性的量測資料轉換為不相關的等效資料以解決該問題。此外，我們修改擴展式資訊濾波器(extended information filter, EIF) ，使之能夠適應解相關後的量測資料，並應用在我們所提出的架構中。群組領頭透過此濾波器來進行目標物定位與追蹤，利用簡單的加法運算融合其他群組的資料，達到分散式定位的目標。本論文模擬經由解相關處理後的定位效能，並與未進行解相關的定位效能相比較，結果驗證經由解相關的步驟確實可有效提升群組式感測器網路的定位準確度。

We consider the topic of target tracking in this thesis. Target tracking is one of the applications in wireless sensor networks (WSNs). Clustering approach prolongs sensor’s lifetime and provides better data aggregation for WSNs. Most previous researches assumed that cluster regions are disjointed, while others assigned overlapping cluster regions, and utilized them in some applications, including inter-cluster routing and time synchronization. However, in overlapping clustering, processing of redundant sensing data may impair system performance. We present a regular distributed overlapping WSN in this thesis. The network is based on two kinds of sensors: (1) high-capability sensors, which are assigned as cluster heads (CHs), responsible for data processing and inter-cluster communication, (2) normal sensors, which are in a larger number when comparing with the high-capability sensors, the function of normal sensors are to provide data to the CHs. We define several operating modes of CHs and sensors. WSN works more efficient under the settings. Since a target may be located in the overlapping region, redundant data processing problem exists. To solve the problem, we utilize Cholesky decomposition to decorrelate the measurement noise covariance matrices. The correlation will be eliminated during the process. In addition, we modify extended information filter (EIF) and adapt to the decorrelated data. The CHs track the target, fuse the information from other CHs, and implement distributed positioning. The simulations are based on ultra-wideband (UWB) environment, we have verified that the proposed scheme works more efficient under the setting of different modes. The performance with decorrelated measurement is better than that with correlated ones.

誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
圖次. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
表次. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
1 緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 研究背景. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 無線感測器網路與定位追蹤法. . . . . . . . . . . . . . . . 4
2.1 時間應用定位法與距離量測模型. . . . . . . . . . . . 4
2.1.1 訊號抵達時間定位法. . . . . . . . . . . . . . . . . . . . . 4
2.1.2 訊號抵達時間差定位法. . . . . . . . . . . . . . . . . . . 6
2.2 超寬頻系統. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 用於目標物追蹤之估測器. . . . . . . . . . . . . . . . . . . 9
2.3.1 擴展式卡爾曼濾波器. . . . . . . . . . . . . . . . . . . . . 9
2.3.2 中心式與分散式架構. . . . . . . . . . . . . . . . . . . . 12
2.3.3 擴展式資訊濾波器. . . . . . . . . . . . . . . . . . . . . . 14
2.4 無線感測器網路. . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5 群組式感測器網路. . . . . . . . . . . . . . . . . . . . . . . . 19
2.5.1 群組領頭選定之探討. . . . . . . . . . . . . . . . . . . . 20
2.5.2 群組範圍重疊之探討. . . . . . . . . . . . . . . . . . . . 21
3 規則散佈感測器網路與雜訊解相關. . . . . . . . . . . . 23
3.1 規則散佈之群組式感測器網路. . . . . . . . . . . . . . 23
3.2 群組領頭與感測器之操作模式設定. . . . . . . . . . 26
3.3 量測雜訊解相關性. . . . . . . . . . . . . . . . . . . . . . . . 32
3.3.1 Cholesky分解法. . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.2 兩個群組之雜訊解相關探討. . . . . . . . . . . . . . 36
3.3.3 廣義反矩陣形式之擴展式資訊濾波器. . . . . . 41
3.3.4 三個群組之雜訊解相關探討. . . . . . . . . . . . . . 45
4 系統模擬及分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.1 群組領頭前置模式之模擬分析. . . . . . . . . . . . . . 53
4.2 量測雜訊解相關之模擬分析. . . . . . . . . . . . . . . . 58
4.2.1 兩個群組之定位效能. . . . . . . . . . . . . . . . . . . . 58
4.2.2 三個群組之定位效能. . . . . . . . . . . . . . . . . . . . 62
4.2.3 整體定位效能. . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.3 速度與取樣頻率之模擬分析. . . . . . . . . . . . . . . . 73
5 結論與展望. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.1 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.2 未來展望與建議. . . . . . . . . . . . . . . . . . . . . . . . . . 77
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
附錄A： TDOA 定位法之相關公式推導. . . . . . . . . 84
A.1 TDOA 線性化量測矩陣推導. . . . . . . . . . . . . . . . 85
A.2 TDOA 量測雜訊共變數矩陣推導. . . . . . . . . . . . 86
附錄B： Cholesky 分解法之實例說明. . . . . . . . . . 87

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