【摘 要】
：
With the rapid growth of the demand for indoor location-based services(LBS),Wi-Fi re-ceived signal strength(RSS)fingerprints database has attracted significant attention because it is easy to ob-tain.The fingerprints algorithm based on convolution neural
【机 构】
：
School of Communication and Information Engineering,Chongqing University of Posts and Telecommunicat
论文部分内容阅读
With the rapid growth of the demand for indoor location-based services(LBS),Wi-Fi re-ceived signal strength(RSS)fingerprints database has attracted significant attention because it is easy to ob-tain.The fingerprints algorithm based on convolution neural network(CNN)is often used to improve in-door localization accuracy.However,the number of reference points used for position estimation has sig-nificant effects on the positioning accuracy.Mean-while,it is always selected arbitraily without any guid-ing standards.As a result,a novel location estima-tion method based on Jenks natural breaks algorithm(JNBA),which can adaptively choose more reasonable reference points,is proposed in this paper.The output of CNN is processed by JNBA,which can select the number of reference points according to different envi-ronments.Then,the location is estimated by weighted K-nearest neighbors(WKNN).Experimental results show that the proposed method has higher positioning accuracy without sacrificing more time cost than the existing indoor localization methods based on CNN.
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