基于智能手機的WiFi的室內(nèi)定位研究
發(fā)布時間：2018-10-17 21:54
【摘要】：位置信息是連接物理世界和網(wǎng)絡空間的重要結(jié)合點,是物聯(lián)網(wǎng)時代中極其重要的因素,與人類的社會生活息息相關�，F(xiàn)在人們的生活方式已發(fā)生改變,每天80%的時間都活動在室內(nèi)環(huán)境下,然而在室內(nèi)環(huán)境下GPS技術無法取得令人滿意的定位精度。隨著WiFi網(wǎng)絡大面積部署于各種室內(nèi)場所,智能手機的使用已成“燎原”之勢,基于智能手機的WiFi室內(nèi)定位研究受到越來越多的關注。目前,基于智能手機的WiFi室內(nèi)定位算法主要分為兩種:基于無線測距的室內(nèi)定位算法和基于接收信號強度指示(RSSI)指紋的室內(nèi)定位算法。這兩種定位算法在實際定位中遇到很多挑戰(zhàn),基于測距的室內(nèi)定位算法是根據(jù)無線測距原理進行幾何約束定位,由于室內(nèi)環(huán)境的多徑效應導致信號強度值波動很大,從而使得定位精度很低。基于RSSI指紋定位算法的前提是構(gòu)建精確的指紋數(shù)據(jù)庫,然而構(gòu)建RSSI指紋數(shù)據(jù)需要花費大量的人工代價進行現(xiàn)場勘測采集。在針對基于測距和RSSI指紋室內(nèi)定位中遇到的挑戰(zhàn),本文分別進行如下兩個方面研究:(1)在針對無線測距誤差大導致定位誤差較大的問題,本文提出基于智能手機的信號自適應修正算法,采用修正因子來提高無線測距的精度,從而降低定位的誤差。(2)在針對基于RSSI指紋定位中人工采集指紋數(shù)據(jù)代價的問題,本文利用無線傳感器網(wǎng)絡進行指紋數(shù)據(jù)的采集,并提出面向稀疏采樣的無線指紋構(gòu)建算法,利用稀疏表示技術有效降低了無線傳感器網(wǎng)絡中數(shù)據(jù)傳輸代價。本文的研究取得非常不錯的效果。在針對測距定位中,基于智能手機的信號自適應修正算法將定位精度提高35.9%。在針對基于指紋數(shù)據(jù)定位中人工采集指紋成本的問題上,本文的面向稀疏采樣的無線指紋構(gòu)建算法在保證指紋數(shù)據(jù)精度的條件下,有效地降低了采集成本。
[Abstract]:Location information is an important link between the physical world and cyberspace, and is an extremely important factor in the age of the Internet of things, which is closely related to the social life of human beings. Nowadays, people's life style has changed, 80% of the time is in indoor environment. However, GPS technology can not achieve satisfactory positioning accuracy in indoor environment. With the WiFi network deployed in a wide range of indoor locations, the use of smart phones has become a "prairie fire" trend. More and more attention has been paid to the research of WiFi indoor positioning based on smart phones. At present, WiFi indoor location algorithm based on smart phone is mainly divided into two kinds: indoor location algorithm based on wireless ranging and indoor location algorithm based on received signal intensity indicating (RSSI) fingerprint. These two localization algorithms meet a lot of challenges in the actual localization. The indoor localization algorithm based on ranging is based on the principle of wireless ranging for geometric constraint localization. Because of the multipath effect of indoor environment, the signal intensity fluctuates greatly. Thus, the positioning accuracy is very low. The premise of fingerprint location algorithm based on RSSI is to build an accurate fingerprint database. However, the construction of RSSI fingerprint data requires a great deal of manual cost to carry out field survey and collection. In view of the challenges encountered in indoor location based on ranging and RSSI fingerprint, the following two aspects are studied in this paper: (1) aiming at the problem that the large error of wireless ranging leads to the large positioning error, In this paper, an adaptive signal correction algorithm based on smart phone is proposed. The correction factor is used to improve the accuracy of wireless ranging and reduce the positioning error. (2) aiming at the cost of manually collecting fingerprint data in fingerprint location based on RSSI. In this paper, we use wireless sensor networks to collect fingerprint data, and propose a sparse sampling oriented fingerprint construction algorithm. The sparse representation technology can effectively reduce the cost of data transmission in wireless sensor networks. The research in this paper has achieved very good results. In the localization of ranging, the signal adaptive correction algorithm based on smart phone can improve the precision of location by 35.9. Aiming at the cost of fingerprint acquisition based on fingerprint data location, the wireless fingerprint construction algorithm for sparse sampling can effectively reduce the cost of fingerprint acquisition under the condition of ensuring the precision of fingerprint data.
【學位授予單位】：安徽工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN92
本文編號：2278121
[Abstract]:Location information is an important link between the physical world and cyberspace, and is an extremely important factor in the age of the Internet of things, which is closely related to the social life of human beings. Nowadays, people's life style has changed, 80% of the time is in indoor environment. However, GPS technology can not achieve satisfactory positioning accuracy in indoor environment. With the WiFi network deployed in a wide range of indoor locations, the use of smart phones has become a "prairie fire" trend. More and more attention has been paid to the research of WiFi indoor positioning based on smart phones. At present, WiFi indoor location algorithm based on smart phone is mainly divided into two kinds: indoor location algorithm based on wireless ranging and indoor location algorithm based on received signal intensity indicating (RSSI) fingerprint. These two localization algorithms meet a lot of challenges in the actual localization. The indoor localization algorithm based on ranging is based on the principle of wireless ranging for geometric constraint localization. Because of the multipath effect of indoor environment, the signal intensity fluctuates greatly. Thus, the positioning accuracy is very low. The premise of fingerprint location algorithm based on RSSI is to build an accurate fingerprint database. However, the construction of RSSI fingerprint data requires a great deal of manual cost to carry out field survey and collection. In view of the challenges encountered in indoor location based on ranging and RSSI fingerprint, the following two aspects are studied in this paper: (1) aiming at the problem that the large error of wireless ranging leads to the large positioning error, In this paper, an adaptive signal correction algorithm based on smart phone is proposed. The correction factor is used to improve the accuracy of wireless ranging and reduce the positioning error. (2) aiming at the cost of manually collecting fingerprint data in fingerprint location based on RSSI. In this paper, we use wireless sensor networks to collect fingerprint data, and propose a sparse sampling oriented fingerprint construction algorithm. The sparse representation technology can effectively reduce the cost of data transmission in wireless sensor networks. The research in this paper has achieved very good results. In the localization of ranging, the signal adaptive correction algorithm based on smart phone can improve the precision of location by 35.9. Aiming at the cost of fingerprint acquisition based on fingerprint data location, the wireless fingerprint construction algorithm for sparse sampling can effectively reduce the cost of fingerprint acquisition under the condition of ensuring the precision of fingerprint data.
【學位授予單位】：安徽工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN92
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