【摘要】：移動設備的爆炸性增長使得人們對移動定位和導航的需求不斷增大,作為室外定位的“最后一公里”,室內定位越來越受到人們的關注。但是因為室內環(huán)境復雜,而且對定位精度有著比較嚴格的要求,所以目前還沒有比較完善的室內定位技術可以很好的利用。因此,專家學者提出了多種室內定位技術解決方案,每一種技術都有其應用的場景和優(yōu)缺點。由于基于WiFi的室內定位技術具有覆蓋范圍廣,信息傳輸速度快,實現成本低等優(yōu)點成為了人們研究和關注的熱點。 本論文研究分析了當前WiFi室內定位的關鍵技術,并在此基礎上設計實現了基于WiFi的定位引擎軟件。目前基于WiFi的室內定位技術分為基于傳播模型的定位方法和基于指紋匹配的定位方法兩種�；趥鞑ツＰ偷亩ㄎ环椒ㄖ饕ㄟ^尋找RSSI值與AP之間的某種傳播模型進行定位計算。但是因為無線信號在傳播過程中會受到多徑傳播以及障礙物的阻擋等影響,RSSI值與AP之間并不存在一個確定的傳播模型,因此這種室內定位方法并沒有獲得好的效果�；谥讣y匹配的定位方法需要預先在選定位區(qū)域中選取參考點采集射頻信號進行訓練,從而構建信號強度與定位位置之間的映射關系。在定位的時候移動終端實時采集周圍AP的射頻信號強度,構建未知指紋,然后在離線階段中建立好的射頻指紋庫中,查找和該未知指紋最相似的指紋,該指紋對應的位置就是終端的估計位置。目前基于指紋匹配的室內定位算法基本上都依賴于具體的RSSI值,由于RSSI值的時變性以及設備異構性,所以這種定位方法的定位精度仍然不是很理想。 鑒于以上原因,本文介紹了另一種基于指紋匹配的定位算法,該算法不再依賴于具體的RSSI值,而是通過建立AP和AP之間的某種關系進行定位計算,一般來說這種關系對不同的設備來說都是比較固定的,所以這種定位算法很好的解決了設備異構性問題,具有很好的魯棒性�；谠撍惴�,設計并實現了具有魯棒性高精度的室內WiFi定位引擎軟件。通過大量的實地測試證明,該定位引擎軟件的精度較好,達到了3米左右。
[Abstract]:With the explosive growth of mobile devices, the demand for mobile positioning and navigation is increasing. As the last kilometer of outdoor positioning, indoor positioning has attracted more and more attention. However, due to the complex indoor environment and the strict requirements of positioning accuracy, there is no perfect indoor positioning technology can be used. Therefore, experts and scholars put forward a variety of indoor positioning technology solutions, each technology has its application scenarios and advantages and disadvantages. Because of the advantages of indoor positioning technology based on WiFi, such as wide coverage, fast information transmission and low cost, it has become a hot topic of research and attention. In this paper, the key technologies of WiFi indoor positioning are analyzed, and the software of positioning engine based on WiFi is designed and implemented. At present, the indoor localization technology based on WiFi is divided into two kinds: one is based on propagation model and the other is based on fingerprint matching. The localization method based on propagation model is mainly based on finding a certain propagation model between RSSI value and AP. However, due to the influence of multipath propagation and obstacle blocking on wireless signal propagation, there is not a definite propagation model between RSSI and AP, so this indoor localization method has not achieved good results. The location method based on fingerprint matching needs to select a reference point in the selected location area to collect RF signals for training in order to construct the mapping relationship between the signal strength and the location position. At the time of location, the mobile terminal collects the radio frequency signal intensity of the surrounding AP in real time, constructs the unknown fingerprint, and then in the off-line stage establishes the RF fingerprint database, looks for the fingerprint which is the most similar to the unknown fingerprint. The corresponding position of the fingerprint is the estimated position of the terminal. At present, the indoor location algorithms based on fingerprint matching basically depend on the specific RSSI value. Because of the time-varying of RSSI value and the heterogeneity of equipment, the localization accuracy of this method is still not very good. In view of the above reasons, this paper introduces another location algorithm based on fingerprint matching, which no longer depends on the specific RSSI value, but by establishing a certain relationship between AP and AP. Generally speaking, this relationship is relatively fixed for different devices, so this location algorithm solves the problem of device heterogeneity very well and has good robustness. Based on this algorithm, a robust indoor WiFi positioning engine software is designed and implemented. Through a lot of field tests, it is proved that the accuracy of the positioning engine software is good, reaching about 3 meters.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN92;TP311.52

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本文編號：2249924