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Deep Learning for Counting People from UWB Channel Impulse Response Signals
Gun Lee1, Subin An1, Byung-Jun Jang1
1School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea.
Sensors (Basel, Switzerland)
|August 26, 2023
Summary
Ultra-wideband (UWB) signals, using higher frequencies, can estimate room occupancy. Deep neural networks applied to UWB channel impulse response (CIR) data accurately classify the number of people, achieving 99% efficiency.
Area of Science:
- Electrical Engineering
- Computer Science
- Signal Processing
Background:
- Ultra-wideband (UWB) technology utilizes higher frequency bands for precise localization.
- UWB signals' channel impulse response (CIR) waveform can potentially estimate the number of people in a room.
Purpose of the Study:
- To apply deep neural networks (DNNs) for estimating room occupancy using UWB CIR signals.
- To investigate various DNN architectures and ensemble configurations for single UWB CIR data classification.
Main Methods:
- Acquisition and preprocessing of UWB CIR data.
- Design and implementation of diverse DNN architectures and ensemble models.
- Comparative experimental evaluation of classification performance.
Main Results:
- DNNs accurately classify the number of people in a Line of Sight (LoS) environment.
- Achieved 99% performance and efficiency in terms of memory size and Floating Point Operations Per Second (FLOPs).
Conclusions:
- Deep neural networks are effective for non-intrusive people counting using UWB CIR data.
- The proposed method offers a highly efficient solution for real-time occupancy estimation.
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