Journal
CHINA COMMUNICATIONS
Volume 19, Issue 4, Pages 291-301Publisher
CHINA INST COMMUNICATIONS
DOI: 10.23919/JCC.2022.04.021
Keywords
Convolutional neural networks; Location awareness; Training; Fingerprint recognition; Gray-scale; Data models; Estimation; indoor localization; convolution neural network (CNN); Wi-Fi fingerprints; Jenks natural breaks
Categories
Funding
- National Natural Science Foundation of China (NSFC) [62001238, 61901075]
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A novel location estimation method based on JNBA is proposed in this paper. It improves the positioning accuracy by adaptively selecting reasonable reference points, and estimates the location using the WKNN algorithm.
With the rapid growth of the demand for indoor location-based services (LBS), Wi-Fi received signal strength (RSS) fingerprints database has attracted significant attention because it is easy to obtain. The fingerprints algorithm based on convolution neural network (CNN) is often used to improve indoor localization accuracy. However, the number of reference points used for position estimation has significant effects on the positioning accuracy. Meanwhile, it is always selected arbitraily without any guiding standards. As a result, a novel location estimation 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 environments. 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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