基于WiFi的定位引擎軟件的設(shè)計(jì)與實(shí)現(xiàn)
發(fā)布時(shí)間：2018-09-19 10:53
【摘要】：移動(dòng)設(shè)備的爆炸性增長(zhǎng)使得人們對(duì)移動(dòng)定位和導(dǎo)航的需求不斷增大,作為室外定位的“最后一公里”,室內(nèi)定位越來(lái)越受到人們的關(guān)注。但是因?yàn)槭覂?nèi)環(huán)境復(fù)雜,而且對(duì)定位精度有著比較嚴(yán)格的要求,所以目前還沒(méi)有比較完善的室內(nèi)定位技術(shù)可以很好的利用。因此,專家學(xué)者提出了多種室內(nèi)定位技術(shù)解決方案,每一種技術(shù)都有其應(yīng)用的場(chǎng)景和優(yōu)缺點(diǎn)。由于基于WiFi的室內(nèi)定位技術(shù)具有覆蓋范圍廣,信息傳輸速度快,實(shí)現(xiàn)成本低等優(yōu)點(diǎn)成為了人們研究和關(guān)注的熱點(diǎn)。 本論文研究分析了當(dāng)前WiFi室內(nèi)定位的關(guān)鍵技術(shù),并在此基礎(chǔ)上設(shè)計(jì)實(shí)現(xiàn)了基于WiFi的定位引擎軟件。目前基于WiFi的室內(nèi)定位技術(shù)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。基于傳播模型的定位方法主要通過(guò)尋找RSSI值與AP之間的某種傳播模型進(jìn)行定位計(jì)算。但是因?yàn)闊o(wú)線信號(hào)在傳播過(guò)程中會(huì)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(gè)確定的傳播模型,因此這種室內(nèi)定位方法并沒(méi)有獲得好的效果�；谥讣y匹配的定位方法需要預(yù)先在選定位區(qū)域中選取參考點(diǎn)采集射頻信號(hào)進(jìn)行訓(xùn)練,從而構(gòu)建信號(hào)強(qiáng)度與定位位置之間的映射關(guān)系。在定位的時(shí)候移動(dòng)終端實(shí)時(shí)采集周圍AP的射頻信號(hào)強(qiáng)度,構(gòu)建未知指紋,然后在離線階段中建立好的射頻指紋庫(kù)中,查找和該未知指紋最相似的指紋,該指紋對(duì)應(yīng)的位置就是終端的估計(jì)位置。目前基于指紋匹配的室內(nèi)定位算法基本上都依賴于具體的RSSI值,由于RSSI值的時(shí)變性以及設(shè)備異構(gòu)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(guò)建立AP和AP之間的某種關(guān)系進(jìn)行定位計(jì)算,一般來(lái)說(shuō)這種關(guān)系對(duì)不同的設(shè)備來(lái)說(shuō)都是比較固定的,所以這種定位算法很好的解決了設(shè)備異構(gòu)性問(wèn)題,具有很好的魯棒性�；谠撍惴�,設(shè)計(jì)并實(shí)現(xiàn)了具有魯棒性高精度的室內(nèi)WiFi定位引擎軟件。通過(guò)大量的實(shí)地測(cè)試證明,該定位引擎軟件的精度較好,達(dá)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(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN92;TP311.52
本文編號(hào)：2249924
[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(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN92;TP311.52
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 鄧中亮;王文杰;徐連明;;一種基于K-means算法的WLAN室內(nèi)定位樓層判別方法[J];軟件;2012年12期
2 盧恒惠;劉興川;張超;林孝康;;基于三角形與位置指紋識(shí)別算法的WiFi定位比較[J];移動(dòng)通信;2010年10期
，本文編號(hào)：2249924
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