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Through-Wall UWB Radar Based on Sparse Deconvolution with Arctangent Regularization for Locating Human Subjects.

Artit Rittiplang¹, Pattarapong Phasukkit¹

¹School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.

Sensors (Basel, Switzerland)

|April 30, 2021

Summary

This study introduces a novel sparse deconvolution method using arctangent regularization for through-wall ultrawideband (UWB) radar. The technique accurately identifies human positions behind concrete walls, overcoming signal attenuation and noise challenges.

Keywords:

UWB radar arctangent regularization majorization–minimization (MM) algorithm sparse deconvolution through-wall radar

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Area of Science:

Radar Systems Engineering
Signal Processing
Non-convex Optimization

Background:

Through-wall radar systems face challenges with signal attenuation and environmental noise.
Reflected radar signals are a convolution of wavelets and object time series, complicating analysis.
Accurate object detection behind walls requires robust signal extraction methods.

Purpose of the Study:

To develop a method for extracting object time series from noisy through-wall ultrawideband (UWB) radar signals.
To apply sparse deconvolution with arctangent regularization for improved human position detection.
To evaluate the effectiveness of the proposed method in challenging through-wall scenarios.

Main Methods:

Utilizing sparse deconvolution based on arctangent regularization, a non-convex approach.
Employing an iterative majorization-minimization (MM) algorithm for efficient problem-solving.
Conducting experiments with an S-band UWB radar system to validate the technique.

Main Results:

The proposed sparse deconvolution method successfully identified human positions from noisy through-wall UWB radar signals.
Arctangent regularization provided more accurate and reliable solutions compared to convex regularizations.
The method demonstrated effectiveness in detecting human presence behind concrete walls.

Conclusions:

Sparse deconvolution with arctangent regularization is a promising technique for through-wall human detection.
The MM-based iterative approach enables efficient and accurate signal extraction.
This method enhances the capabilities of UWB radar for security and surveillance applications.