An Improved BPNN Method Based on Probability Density for Indoor Location

• Published in: IEICE Transactions on Information and Systems Vol. E106.D; no. 5; pp. 773 - 785
• Main Authors: FEI, Rong, GUO, Yufan, LI, Junhuai, HU, Bo, YANG, Lu
• Format: Journal Article
• Language: English
• Published: Tokyo The Institute of Electronics, Information and Communication Engineers 01.05.2023; Japan Science and Technology Agency
• Subjects: Accuracy; Algorithms; Artificial neural networks; Back propagation; Back propagation networks; backpropagation neural network; cumulative distribution function; deep neural network; Distribution functions; Neural networks; Propagation; WiFi fingerprint
• ISSN: 0916-8532; 1745-1361
• DOI: 10.1587/transinf.2022DLP0073

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).