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An improved convolutional neural network based indoor localization by using Jenks natural breaks algorithm
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AI
DOI：10.23919/JCC.2022.04.021.png)
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Abstract
En 中文
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.
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
Journal
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