Multiple Range and Vital Sign Detection Based on Single-Conversion Self-Injection-Locked Hybrid Mode Radar With a Novel Frequency Estimation Algorithm
This article presents a single-conversion self-injection-locked (SIL) (SCSIL) hybrid mode radar with a novel frequency estimation algorithm, which improves the range resolution in a frequency-modulated continuous-wave (FMCW) mode and simultaneously detects the vital signs of multiple subjects in the...
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Published in | IEEE transactions on microwave theory and techniques Vol. 68; no. 5; pp. 1908 - 1920 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.05.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | This article presents a single-conversion self-injection-locked (SIL) (SCSIL) hybrid mode radar with a novel frequency estimation algorithm, which improves the range resolution in a frequency-modulated continuous-wave (FMCW) mode and simultaneously detects the vital signs of multiple subjects in the CW mode. The system architecture includes an SIL oscillator (SILO), a frequency converter, two antennas, and a noncoherent frequency demodulator. Due to the frequency-phase relationship in the spectrum of the beat signal in the FMCW mode, the distance to the closely spaced subjects can be determined. Moreover, the algorithm will identify the most dominant component and remove it from the input signal, and the steps are repeated until the residual-to-input energy ratio is less than the threshold value. This iterative method can thus distinguish multiple signals whose frequencies are close to each other and can support a superresolution analysis in both FMCW and CW modes. In an experiment, the radar with a 150-MHz bandwidth and a sensing period of 20 s can simultaneously provide accurate range and vital sign information of two adjacent subjects with radial and azimuth spacings of 60 cm and 5°, respectively. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 0018-9480 1557-9670 |
DOI: | 10.1109/TMTT.2020.2967372 |