WiFi Based Indoor Navigation System for Closed Buildings Using Smart Phones

Hushe, Gayatri

doi:10.5815/ijem.2021.03.04

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…signal source [4], mainly through WiFi [5], Bluetooth [6], UWB [7], and another external signal source to achieve positioning target; The second type is based on natural resources and relies on terminal sensors (including inertial navigation method [8], geomagnetic navigation method [9], etc.) to achieve positioning requirements.…”

Section: Geomagnetic Localizationmentioning

confidence: 99%

An Improved BPNN Method Based on Probability Density for Indoor Location

FEI

Guo

et al. 2023

IEICE Trans. Inf. & Syst.

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With the widespread use of indoor positioning technology, the need for high-precision positioning services is rising; nevertheless, there are several challenges, such as the difficulty of simulating the distribution of interior location data and the enormous inaccuracy of probability computation. As a result, this paper proposes three different neural network model comparisons for indoor location based on WiFi fingerprint -indoor location algorithm based on improved back propagation neural network model, RSSI indoor location algorithm based on neural network angle change, and RSSI indoor location algorithm based on depth neural network angle change -to raise accurately predict indoor location coordinates. Changing the action range of the activation function in the standard back-propagation neural network model achieves the goal of accurately predicting location coordinates. The revised back-propagation neural network model has strong stability and enhances indoor positioning accuracy based on experimental comparisons of loss rate (loss), accuracy rate (acc), and cumulative distribution function (CDF).

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