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Convolutional Model with a Time Series Feature Based on RSSI Analysis with the Markov Transition Field for
1Department of IT Media Engineering, Duksung Women's University, 33, Samyang-ro 144-gil, Dobong-gu, Seoul 01369, Republic of Korea.
Sensors (Basel, Switzerland)
|April 13, 2023
Summary
This study introduces an image transformation scheme to improve indoor location recognition using Received Signal Strength Indication (RSSI) with Convolutional Neural Networks (CNNs). The method enhances accuracy by addressing RSSI instability and time-series data characteristics.
Area of Science:
- Computer Science
- Electrical Engineering
- Signal Processing
Background:
- Received Signal Strength Indication (RSSI) is crucial for indoor location recognition but suffers from severe instability.
- Existing learning-based approaches, including recurrent neural networks, struggle with RSSI fluctuations.
- Convolutional Neural Networks (CNNs) show promise for indoor localization through feature extraction, but require optimized input data.
Purpose of the Study:
- To develop an image transformation scheme for enhancing CNN-based indoor location recognition using Received Signal Strength Indication (RSSI).
- To address the inherent instability of RSSI data and the characteristics of time-series data for improved localization accuracy.
- To present a robust learning model that leverages image transformation for practical indoor positioning.
Main Methods:
- An image transformation scheme was developed to process practical RSSI data, including artificial Gaussian noise injection.
- A novel learning model was designed to effectively handle the time-series nature of the transformed RSSI data.
- A dedicated testbed was constructed for evaluating the proposed scheme using real-world RSSI measurements.
Main Results:
- The proposed image transformation scheme integrated with CNNs demonstrated significant performance improvements for indoor localization.
- The method achieved approximately 46.2% enhancement in performance compared to conventional CNN-based approaches that do not use the image transformation.
- The learning model effectively mitigated the impact of RSSI instability and noise on location recognition accuracy.
Conclusions:
- The developed image transformation scheme offers a viable solution for improving the accuracy and stability of indoor location recognition systems.
- This approach effectively leverages CNNs by providing them with more robust and informative data representations derived from RSSI.
- The findings suggest a promising direction for future research in robust indoor positioning systems utilizing signal strength measurements.
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