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A Two-Stage Reconstruction Processor for Human Detection in Compressive Sensing CMOS Radar.

Kuei-Chi Tsao1, Ling Lee2, Ta-Shun Chu3

  • 1Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan. andrew000129@gmail.com.

Sensors (Basel, Switzerland)
|April 6, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-stage reconstruction algorithm for low-power complementary metal-oxide-semiconductor (CMOS) impulse radar systems. The new algorithm significantly reduces computational complexity and improves human detection accuracy in noisy environments for Internet-of-Things applications.

Keywords:
CMOS radarcompressive sensingranging

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Area of Science:

  • Electrical Engineering and Computer Science
  • Signal Processing
  • Internet of Things (IoT)

Background:

  • Complementary metal-oxide-semiconductor (CMOS) radar is attractive for low-power wireless sensing due to its small size and low power consumption.
  • Detecting humans and objects using CMOS impulse radar in home-care IoT systems faces challenges with weak human signals and high computational complexity.
  • Compressive sensing (CS) algorithms, like orthogonal matching pursuit (OMP), offer reduced computational costs for radar signal processing but can still be complex.

Purpose of the Study:

  • To develop a more computationally efficient and accurate signal processing algorithm for wireless CMOS impulse radar systems.
  • To address the limitations of existing compressive sensing radar algorithms, particularly the high complexity associated with high-resolution human respiration detection.
  • To enhance the performance of human and object detection in low-power wireless sensing applications, specifically within home-care IoT environments.

Main Methods:

  • Proposed a novel two-stage reconstruction algorithm for compressive sensing (CS) radar signal processing.
  • Compared the proposed algorithm against the orthogonal matching pursuit (OMP) algorithm in terms of computational complexity and positioning performance.
  • Implemented the algorithm on a Vertex-7 FPGA chip for real-time radar image processing and display.

Main Results:

  • The proposed two-stage reconstruction algorithm demonstrated a 75% reduction in computational complexity compared to the OMP algorithm.
  • Achieved superior positioning performance over the OMP algorithm, especially in environments with significant noise.
  • The FPGA implementation supported real-time 256x13 radar image display at a throughput of 28.2 frames per second.

Conclusions:

  • The developed two-stage reconstruction algorithm offers a significant improvement in efficiency and accuracy for CMOS impulse radar systems.
  • This advancement is crucial for enabling robust and low-power human and object detection in home-care IoT sensing applications.
  • The successful FPGA implementation validates the algorithm's capability for real-time processing and display in practical sensing systems.