Journal of The Korea Internet of Things Society
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- P-ISSN2799-4791
- KCI
Smoothed RSSI-Based Distance Estimation Using Deep Neural Network
Abstract
In this paper, we propose a smoothed received signal strength indicator (RSSI)-based distance estimation using deep neural network (DNN) for accurate distance estimation in an environment where a single receiver is used. The proposed scheme performs a data preprocessing consisting of data splitting, missing value imputation, and smoothing steps to improve distance estimation accuracy, thereby deriving the smoothed RSSI values. The derived smoothed RSSI values are used as input data of the Multi-Input Single-Output (MISO) DNN model, and are finally returned as an estimated distance in the output layer through input layer and hidden layer. To verify the superiority of the proposed scheme, we compared the performance of the proposed scheme with that of the linear regression-based distance estimation scheme. As a result, the proposed scheme showed 29.09% higher distance estimation accuracy than the linear regression-based distance estimation scheme.
- keywords
- Distance Estimation, BLE Beacon, DNN, Feedback Filter, Smoothed RSSI, 거리 추정, BLE 비콘, DNN, Feedback Filter, Smoothed RSSI
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