An Algorithm Based Wavelet Entropy for Shadowing Effect of Human Detection Using Ultra-Wideband Bio-Radar

Area of Science:

• Radar Systems Engineering
• Signal Processing
• Search and Rescue Technology

Background:

• Ultra-wide band (UWB) radar is crucial for short-range human target detection in disaster rescue operations.
• Bistatic UWB radar struggles with detecting human targets at greater distances, impacting survivor localization.
• Existing methods face limitations in reliably distinguishing faint target echoes from background noise in shadowed regions.

Purpose of the Study:

• To develop and evaluate a novel algorithm for enhanced detection of multiple human targets using bistatic UWB radar.
• To address the challenge of reduced detection reliability for targets farther from radar antennas.
• To improve the accuracy and effectiveness of UWB radar in complex rescue scenarios.

Main Methods:

• Proposed an algorithm based on wavelet entropy to analyze echo signals from human targets.
• Leveraged the distinct frequency content differences between human target echoes and noise in shadowing regions.
• Compared the performance of wavelet entropy against adaptive filtering and energy spectrum methods.

Main Results:

• Wavelet entropy demonstrated significantly lower entropy values for human targets compared to noise.
• The proposed wavelet entropy algorithm accurately detected human targets located farther from the radar antennas.
• Achieved superior detection performance over adaptive filtering and energy spectrum techniques.

Conclusions:

• Wavelet entropy is an effective tool for improving the detection of distant human targets with bistatic UWB radar.
• The algorithm offers enhanced reliability for identifying multiple targets in challenging rescue environments.
• This method represents a valuable advancement for UWB radar applications in search and rescue missions.