臺灣博碩士論文加值系統

全球定位系統(GPS)，是最成功的室外環境定位系統，但在衛星信號覆蓋率較差的環境無法保持視線距離，會降低其準確性，因此無線感測定位近年來是一門相當熱門的研究項目，其定位技術分別為接收訊號強度(RSS)、到達時間(TOA)、到達時間差(TDOA)、到達角度(AOA)，而在其定位性能上，因為有非視線及多重路徑的關係，所以要以定位演算法來降低其誤差和提高精準度。
因RSS不需要時間同步，且較容易實現的定位估測方法，在實際建置成本上，較其他定位方法低，直接透過訊號強度衰減來轉換距離，同時定位出目標物。因此，本研究運用接收訊號強度(RSS)定位技術，透過比較定位演算法探討較精準的演算法與訊號模型。本研究使用最佳線性非偏權估測 (BULE) 為訊號模型，以最小平方演算法(LS)，加權最小平方演算法(WLS)與 Blind Minimax Estimation (BME) 均方誤差演算法，來進行比較，其模擬結果顯示Blind MiniMax Estimation (BME) 均方誤差的性能比最小平方法(LS)與加權最小平方法(WLS)較佳。

The Global Positioning System (GPS) is a successful and best satellite navigation system that provides mobile location in all environments of outdoor. But it cannot keep the best line of sight, and the accuracy of location will be degraded in the environment of poor satellite signal. Thus, the wireless sensor network is a popular research project in recent years. The location technologies include received signal strength (RSS), time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA). However, the non-line of sight and the multiple path will effect positioning performance, the positioning algorithm is necessary to reduce the errors and improve the estimation accuracy.

However, the received signal strength (RSS) does not require time synchronization. Therefore, we can easy to get the positioning estimation. In establish and development costs, RSS is lower than other positioning methods; and it through the signal strength to transfer the distance, and the location of the target can be obtained. This study is based on RSS positioning technology to discuss the accurate algorithms and signal model by positioning algorithms. This study uses the best linear unbiased estimator (BLUE) for the signal model. By comparing the positioning performance, we use the least square (LS) method, the weighted least squares (WLS) method, and blind minimax estimation (BME) mean-square-error method. Finally, the simulation results show that the performance of blind minimax estimation (BME) mean-square-error method is better than the least square (LS) method and the weighted least squares (WLS) method.

摘要 I
ABSTRACT II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
符號說明與縮寫 VIII
1. 緒論 1
1.1研究背景 1
1.2定位技術的評估標準 2
1.3研究動機與目的 2
1.4論文架構 3
2. 文獻探討與相關研究 5
2.1無線感測網路（Wireless Sensor Network）介紹 5
2.2蜂巢式定位 5
2.3無線定位技術 6
2.3.1 RSS (Received Signal Strength) 7
2.3.2 TOA (Time of Arrival) 7
2.3.3 TDOA（Time Different of Arrival） 8
2.3.4 AOA/DOA （Angle of Arrival /Direction of Arrival） 9
2.3.5 混合TOA/AOA模型 10
2.4定位精準度分析 11
2.5影響無線感測網路定位因素 12
3. 研究方法 16
3.1接收訊號強度定位法 16
3.2最小平方演算法(Least Squares , LS) 17
3.3最佳線性非偏權估測-最小平方校準演算法 18
3.4最佳線性非偏權估測-最小平方演算法 20
3.5最小最大均方誤差演算法 21
3.6 Blind MiniMax Estimation 均方誤差演算法 22
4. 實驗模擬與比較 25
5. 結論 33
參考文獻 34

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