IEICE TRANSACTIONS on Information
An Improved BPNN Method Based on Probability Density for Indoor Location
Summary :
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).
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.773-785
- Publication Date
- 2023/05/01
- Publicized
- 2022/12/23
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022DLP0073
- Type of Manuscript
- Special Section PAPER (Special Section on Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications)
- Category
- Positioning and Navigation
Authors
Rong FEI
Xi'an University of Technology
Yufan GUO
Xi'an University of Technology
Junhuai LI
Xi'an University of Technology
Bo HU
Hangzhou HollySys Automation Co., Ltd.
Lu YANG
Xi'an University of Technology
Keyword
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Rong FEI, Yufan GUO, Junhuai LI, Bo HU, Lu YANG, "An Improved BPNN Method Based on Probability Density for Indoor Location" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 773-785, May 2023, doi: 10.1587/transinf.2022DLP0073.
Abstract: 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).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DLP0073/_p
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@ARTICLE{e106-d_5_773,
author={Rong FEI, Yufan GUO, Junhuai LI, Bo HU, Lu YANG, },
journal={IEICE TRANSACTIONS on Information},
title={An Improved BPNN Method Based on Probability Density for Indoor Location},
year={2023},
volume={E106-D},
number={5},
pages={773-785},
abstract={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).},
keywords={},
doi={10.1587/transinf.2022DLP0073},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - An Improved BPNN Method Based on Probability Density for Indoor Location
T2 - IEICE TRANSACTIONS on Information
SP - 773
EP - 785
AU - Rong FEI
AU - Yufan GUO
AU - Junhuai LI
AU - Bo HU
AU - Lu YANG
PY - 2023
DO - 10.1587/transinf.2022DLP0073
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - 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).
ER -
English
