Indoor positioning fingerprint generation method based on cGAN-SAE

Home > Archive>Volume 47, Issue 14, 2024 >57-63

Indoor positioning fingerprint generation method based on cGAN-SAE
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                        Liu WeiLiu Wei
1.School of Physics and Electronic Information Engineering, Guilin University of Technology，Guilin 541006, China； 3.Guangxi Key Laboratory of Embedded Technology and Intelligent Systems, Guilin University of Technology，Guilin 541006, China
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Wang ZhihaoWang Zhihao
2.School of Computer Science and Engineering, Guilin University of Technology，Guilin 541006, China； 3.Guangxi Key Laboratory of Embedded Technology and Intelligent Systems, Guilin University of Technology，Guilin 541006, China
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Li ZhuoLi Zhuo
1.School of Physics and Electronic Information Engineering, Guilin University of Technology，Guilin 541006, China； 3.Guangxi Key Laboratory of Embedded Technology and Intelligent Systems, Guilin University of Technology，Guilin 541006, China
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Wei JiahengWei Jiaheng
2.School of Computer Science and Engineering, Guilin University of Technology，Guilin 541006, China； 3.Guangxi Key Laboratory of Embedded Technology and Intelligent Systems, Guilin University of Technology，Guilin 541006, China
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Affiliation:1.School of Physics and Electronic Information Engineering, Guilin University of Technology，Guilin 541006, China; 2.School of Computer Science and Engineering, Guilin University of Technology，Guilin 541006, China; 3.Guangxi Key Laboratory of Embedded Technology and Intelligent Systems, Guilin University of Technology，Guilin 541006, China
Clc Number:TN92
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Abstract:

To address the issues of high fingerprint collection costs and the difficulty of constructing datasets in indoor positioning, a method for indoor positioning fingerprint generation based on a conditional sparse autoencoder generative adversarial network is proposed. This method enhances the feature extraction capability by adding hidden and output layers to the autoencoder, guiding the generator to learn and generate key features of fingerprint data. A fingerprint selection algorithm is used to filter out the most relevant fingerprint data, which is then added to the fingerprint database and used to train a convolutional long shortterm memory network model for online performance evaluation. Experimental results show that the conditional sparse autoencoder generative adversarial network improves the accuracy of indoor positioning in multi-building, multi-floor environments without increasing the number of collected samples. Compared to the original conditional generative adversarial network model, the positioning error in predictions on the UJIIndoorLoc dataset is reduced by 6%, and in practical applications, the positioning error is reduced by 14%.

Key words:indoor positioning;sparse autoencoder;fingerprint database;conditional generative adversarial network;convolutional long short-term memory network

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Online: November 22,2024
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