Indoor localization using multi-frequency RSS

Skoglund, Martin A.; Hendeby, Gustaf; Nygårds, Jonas; Rantakokko, Jouni; Eriksson, Gunnar

doi:10.1109/plans.2016.7479774

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…After experimental verification, this solution does not lead to a significant decrease in positioning accuracy. Second, the acquisition frequency can be increased to more than 100 Hz to provide enough samples [36]. The following will focus on the experimental verification of the first solution.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression

Zhang

Wang

Chen

et al. 2019

Sensors

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This paper presents a localization model employing convolutional neural network (CNN) and Gaussian process regression (GPR) based on Wi-Fi received signal strength indication (RSSI) fingerprinting data. In the proposed scheme, the CNN model is trained by a training dataset. The trained model adapts to complex scenes with multipath effects or many access points (APs). More specifically, the pre-processing algorithm makes the RSSI vector which is formed by considerable RSSI values from different APs readable by the CNN algorithm. The trained CNN model improves the positioning performance by taking a series of RSSI vectors into account and extracting local features. In this design, however, the performance is to be further improved by applying the GPR algorithm to adjust the coordinates of target points and offset the over-fitting problem of CNN. After implementing the hybrid model, the model is experimented with a public database that was collected from a library of Jaume I University in Spain. The results show that the hybrid model has outperformed the model using k-nearest neighbor (KNN) by 61.8%. While the CNN model improves the performance by 45.8%, the GPR algorithm further enhances the localization accuracy. In addition, the paper has also experimented with the three kernel functions, all of which have been demonstrated to have positive effects on GPR.

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Section: Experiments Results and Discussionmentioning

confidence: 99%