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研究生:陳信文
研究生(外文):Chen Shien-Wen
論文名稱:以粒子群最佳化演算法加速之Zigbee Access Point 布置規劃
論文名稱(外文):Zigbee Access Point Placement Planning Acceleration by Particle Swarm Optimization
指導教授:石志雄
指導教授(外文):Chihhsiong Shih
口試委員:熊博安孔崇旭
口試委員(外文):Pao-Ann HsiungC.S. Koong
口試日期:2013-01-15
學位類別:碩士
校院名稱:東海大學
系所名稱:資訊工程學系
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:2013
畢業學年度:101
語文別:中文
論文頁數:74
中文關鍵詞:粒子群最佳化演算法Zigbee室內定位
外文關鍵詞:Particle Swarm Optimization,PSOZigbeeIndoor Positioning
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利用無線電波的隨著傳輸距離而有不同程度能量衰減的特性,來達到定位目的,為室內定位可行性方法之一,而此定位參數即為已知的接收信號強度指標(Received Signal Strength Indicator, RSSI) 。
地區特徵指紋定位法是常用的Zigbee室內定位程序。其原理是預先量測生活場地中之RSSI,並建成一個指紋資料庫。當上線使用時,由AP之RSSI值,比對資料庫之每點位置之訊號強度,進而找到其真實位置。
然而AP擺放位置,對地區特徵指紋定位法比對之精確度有很大影響,本研究探討AP位置選定,使用粒子群最佳化演算法(Particle Swarm Optimization,PSO)找出最佳位置,分別從一維理論延伸至二維平面AP之分布,來取得最佳的覆蓋率以及辨識率。
在本文中,提出三種不同的PSO一維設計方法,方法一是一次移動一個AP,計算每個位置的值後再利用PSO找到最佳位置,方法二是一次移動一個AP,不計算各個位置,直接利用PSO找到最佳位置,方法三是直接將AP分布,利用PSO直接尋找最佳位置,並將其中兩種方法擴展到二維,用以加速電腦模擬AP最佳化的執行時間。
最後,將兩種方法與原來的方法相比較,方法三方法的確能大幅縮短電腦模擬AP最佳化的執行時間,使這定位系統趨近於成熟,並且依據其模擬結果於電腦教室佈置定位系統,進行實際定位,未來將可使用於大眾生活上,讓科技生活變得更便利。

Indoor localization have been a major issue among the many popular mobile applications, such as personnel or cargo tracking. One of the feasible methods is to utilize the attenuation characteristics of radio wave with the transmission distance to achieve the positioning purpose. This positioning parameter is the well known receive signal strength indicator (Received Signal Strength Indicator, RSSI) of Zigbee.
The location fingerprinting is commonly used in the Zigbee indoor positioning method. The concept is through pre-measured footprints of the RSSI values at preset locations constituting a fingerprint database. When in on-line user mode, the user’s true locations can be acquired through comparing the ratio of the signal strength of different access point(AP) with those in the database.
However, in the real world of radio transmission, the signal strength values (RSSI) are easily subjected to noises from surrounding environments, thus affecting the differentiating rate of finger print method. The locations of Access Points(APs) will affect the accuracy of location fingerprinting. One extreme is to place all APs in one location and causing all the signals to be the same, and target hard to track. So, this study was to investigate the AP position selections using particle swarm optimization algorithm (Particle Swarm Optimization, PSO) to find the optimal positions. The study starts from one-dimensional theory, then extends to the AP distribution on the two-dimensional plane to achieve the best coverage and recognition rate.
In this paper, three one-dimensional PSO design methods are proposed. The first method is to move one AP at a time, then calculate the value of each grid position, then find the best position by use PSO. And the second method is to move one AP at one time, but find the best position by using PSO directly. The third method is through using PSO for direct distribution of all APs, to find the best position combinations of all the APs. Due to the excellent performance of the third method to accelerate the optimized execution time of simulation, the third method is chosen to extend from one-dimension to the two-dimension AP distribution.
Third method is indeed able to substantially shorten the computer simulation time to optimize the execution time of AP. A computer classroom is selected for testing the simulation results. The positioning system based on the simulation results have shown a reduced position estimation error in both average and total scales. Our method is thus proved feasible and can be used in real world localization applications.

目錄
摘要 II
Abstract IV
致謝 VI
致謝 VI
圖錄 3
論文架構 5
第一章 緒論 6
1.1.研究動機: 6
1.2.擬探討解決之問題描述 : 7
第二章 文獻探討 8
2.1.ZigBee室內定位 8
2.2.粒子群最佳化演算法(Particle Swarm Optimization) 9
2.3.高斯混合模型(Gaussian Mixtured Model, GMM) 12
2.4.最佳化室內定位 13
第三章 研究方法 15
3.1.接收信號強度指標(Received Signal Strength Indicator, RSSI)運算定義 16
3.2.一維原始空間定位演算法 18
3.3.粒子群最佳化演算法(Particle Swarm Optimization,PSO) 21
3.4.方法一 (利用PSO尋找矩陣最佳值) : 22
3.4.1.粒子學習方法 : 23
3.4.2. AP分布設計 : 23
3.4.3. AP分布設計(3個AP說明) : 23
3.4.4.適應函數(val) : 23
3.4.5.演算法描述 : 23
3.4.6.演算法流程圖 : 24
3.5.方法二(區域最佳) 設計: 26
3.5.1.粒子學習方法 : 27
3.5.2. AP分布設計 : 27
3.5.3. AP分布設計(3個AP說明) : 27
3.5.4.適應函數(val) : 28
3.5.5.適應函數(3個AP說明) : 28
3.5.6.演算法描述 : 29
3.5.7.方法二執行過程圖 : 31
3.6.方法三(AP排列組合分布) 設計 33
3.6.1.粒子學習方法 : 33
3.6.2.各個AP分布設計 : 33
3.6.3.各個AP分布設計(3個AP說明) : 34
3.6.4.適應函數(val) : 35
3.6.5.適應函數(3個AP說明) : 35
3.6.6.演算法描述 : 36
3.6.7. 方法三執行過程圖 : 37
3.7.四種程式複雜度與執行結果比較 40
3.7.1.四種程式複雜度比較表 40
3.7.2. 電腦模擬執行結果比較. 41
3.8.二維原始空間定位演算法 47
3.9.將一維的方法二擴展到二維 48
3.9.1.演算法描述: 49
3.10.將一維的方法三擴展到二維 50
3.10.1.演算法描述 : 50
4.1.模擬結果 52
4.2.實作結果 56
4.2.1.硬體設備 56
4.2.2. 建立指紋資料庫 57
4.2.3. 最佳化前定位實驗 60
4.2.4.最佳化後定位實驗 61
第五章結論 63
使用符號說明 65
參考文獻 66


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