本文選題：WIFI定位 + 接收信號強度值��； 參考：《西南交通大學》2017年碩士論文

【摘要】：隨著當代信息技術(shù)的發(fā)展,位置信息的獲取應用在人們生活的各個方面,基于位置的服務(LBS)逐漸由室外轉(zhuǎn)向了室內(nèi)。近年來,無線局域網(wǎng)技術(shù)得到了快速的發(fā)展,同時用戶終端設(shè)備趨于智能化,無線定位成為了室內(nèi)定位的主要方式。無線信號室內(nèi)空間傳播具有很高的復雜性,傳統(tǒng)交會定位方式并不適用,因此,本文主要研究內(nèi)容為基于位置指紋的WIFI室內(nèi)定位。本文分析了 RSSI(Received Signal Strength Indication)空間分布特性,建立基于RSSI空間分布的Radio Map,并通過位置指紋定位算法分析Radio Map的可靠性。利用線性回歸修正異構(gòu)設(shè)備RSSI差異問題,對校正定位前后進行了精度分析與評定。在RSSI空間建模階段,首先,通過室內(nèi)環(huán)境下RSSI采集試驗,驗證了復雜環(huán)境下RSSI概率分布特性與空間可區(qū)分性。然后,分析了 RSSI特征提取與離散指紋庫建立兩個階段噪聲,針對RSSI觀測噪聲采用奇異值剔除和高斯濾波,提高了 RSSI信號信噪比和可用性,利用鄰域均值算法對RSSI位置指紋庫進行濾波,提高了 RSSI與空間的匹配程度。接著,利用三次曲面插值算法,對離散型位置指紋庫進行了內(nèi)插,建立連續(xù)型RSSI空間模型Radio Map,提高了離散指紋庫建立的工作效率。最后,分別采用確定型與概率型指紋識別算法進行了定位試驗,分析了兩者定位精度,并驗證了Radio Map建立的可靠性與正確性。在定位階段,利用室內(nèi)環(huán)境下異構(gòu)設(shè)備動態(tài)與靜態(tài)RSSI觀測序列,分析了異構(gòu)設(shè)備之間存在的RSSI偏差,說明了其大小。對異構(gòu)設(shè)備同步動態(tài)RSSI序列進行了相關(guān)性分析,論證了異構(gòu)設(shè)備RSSI之間存在正相關(guān)的線性關(guān)系,利用線性回歸對異構(gòu)設(shè)備RSSI之間進行了線性關(guān)系建模,并標定了模型參數(shù)。通過RSSI修正前后的定位試驗,說明了異構(gòu)設(shè)備RSSI修正方法的正確性。
[Abstract]:With the development of modern information technology, location information acquisition and application in all aspects of people's lives, location-based services gradually shifted from outdoor to indoor. In recent years, wireless local area network (WLAN) technology has been developed rapidly. At the same time, user terminal equipment tends to be intelligent, wireless positioning has become the main way of indoor positioning. Wireless signal propagation in indoor space has a high complexity, traditional rendezvous location method is not applicable, therefore, the main content of this paper is WIFI indoor location based on location fingerprint. In this paper, the spatial distribution characteristics of RSSI(Received Signal Strength indication are analyzed, the Radio map based on RSSI spatial distribution is established, and the reliability of Radio Map is analyzed by the location fingerprint location algorithm. Using linear regression to correct the RSSI difference of heterogeneous equipment, the accuracy analysis and evaluation are carried out before and after calibration. In the stage of RSSI spatial modeling, the characteristics of RSSI probability distribution and spatial separability in complex environment are verified by the RSSI collection experiment in the indoor environment. Then, the RSSI feature extraction and the establishment of discrete fingerprint database are analyzed. The singular value elimination and Gao Si filter are used to improve the signal-to-noise ratio and availability of RSSI signal. The neighborhood mean algorithm is used to filter the RSSI location fingerprint database, which improves the matching degree between RSSI and space. Then, the interpolation algorithm of cubic surface is used to interpolate the discrete position fingerprint database, and the continuous RSSI spatial model Radio map is established, which improves the working efficiency of the discrete fingerprint database. Finally, the localization experiments are carried out by using the deterministic and probabilistic fingerprint identification algorithms, and the accuracy of the two algorithms is analyzed, and the reliability and correctness of the Radio Map are verified. In the positioning stage, using the dynamic and static RSSI observation sequences of heterogeneous devices in indoor environment, the RSSI deviation between heterogeneous devices is analyzed and its size is explained. The correlation analysis of synchronous dynamic RSSI sequences of heterogeneous devices is carried out, and the positive linear relationship between RSSI of heterogeneous devices is demonstrated. The linear relationship between heterogeneous devices RSSI is modeled by linear regression, and the model parameters are calibrated. The correctness of the RSSI correction method for heterogeneous equipment is demonstrated by the positioning test before and after RSSI correction.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN925.93

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本文編號：1781738