Indoor location strategy based on the fusion of RSSI and RTT based on multilayer perceptron

Chenyu Yang; Rong Fei; Mingyue Li

doi:10.1117/12.3029387

6 May 2024 Indoor location strategy based on the fusion of RSSI and RTT based on multilayer perceptron

Chenyu Yang, Rong Fei, Mingyue Li

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 131072F (2024) https://doi.org/10.1117/12.3029387
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

In order to solve the problem of low positioning accuracy of single RSSI feature location algorithm, the multi-layer perceptron (MLP) fingerprint matching algorithm is enhanced through a combination with Round-Trip Time (RTT). The traditional MLP fingerprint matching algorithm based on multi-layer perceptron only uses the feature of RSSI, and the obstacles in the location space are easy to interfere with the RSSI signal and impact the accuracy of positioning. In order to solve the above problems, the MLP fingerprint matching algorithm based on multilayer perceptron is improved in this paper, RTT is added as the second eigenvalue, regression is used to predict the distance of unknown nodes from the Access Point (AP) as well as the input sequences of Received Signal Strength Indicator (RSSI) and Round-Trip Time (RTT). The predicted outcomes are then subjected to a weighted fusion process, following which the coordinates of the sample nodes are determined through mathematical calculations based on the sample output. The improved algorithm solves the interference problem at the same time. The location accuracy is further improved.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chenyu Yang, Rong Fei, and Mingyue Li "Indoor location strategy based on the fusion of RSSI and RTT based on multilayer perceptron", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 131072F (6 May 2024); https://doi.org/10.1117/12.3029387

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