Radio map updated method based on subscriber locations in indoor WLAN localization 被引量：1

Radio map updated method based on subscriber locations in indoor WLAN localization

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摘要 With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model （HMM）, is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm, the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort. With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model （HMM）, is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm, the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.

作者 Ying Xia Zhongzhao Zhang Lin Ma

机构地区 School of Electronics and Information Engineering School of Communication and Electronic Engineering

出处《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第6期1202-1209,共8页 系统工程与电子技术（英文版）

基金 supported by the National Natural Science Foundation of China(61571162) the Major National Science and Technology Project(2014ZX03004003-005)

关键词 subscriber location wireless local area network（WLAN） positioning accuracy calibration effort hidden Markovmodel （HMM）. subscriber location, wireless local area network（WLAN）, positioning accuracy, calibration effort, hidden Markovmodel （HMM）.

分类号 TN925.93 [电子电信—通信与信息系统]

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参考文献1

1林以明,罗海勇,李锦涛,赵方.基于动态Radio Map的粒子滤波室内无线定位算法[J].计算机研究与发展,2011,48(1):139-146. 被引量：20

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