Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing
<p>Illustration of the bioradar measuring principle.</p> "> Figure 2
<p>Polar plot of two <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>[</mo> <mi>l</mi> <mo>]</mo> </mrow> </semantics></math> examples: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, odd. The comb has three tones. For <math display="inline"><semantics> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> and 2, these tones equally distribute on the unit circle, and their complex sum is zero. For <math display="inline"><semantics> <mrow> <mi>l</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, the three tones overlap at −j. (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, even. The comb has four tones. For <math display="inline"><semantics> <mrow> <mi>l</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, the four tones overlap at j.</p> "> Figure 3
<p>For transmitting: (<b>a</b>) The pre-defined waveform in sample time domain. Samples <math display="inline"><semantics> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> <mo>…</mo> <mn>128</mn> </mrow> </semantics></math> built one complete wave. (<b>b</b>) The spectrum of the waveform <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>[</mo> <mi>l</mi> <mo>]</mo> </mrow> </semantics></math> with a length <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>4096</mn> </mrow> </semantics></math>.</p> "> Figure 4
<p>Illustration of a bioradar operation scenario. A person is trapped underneath rubble piles. A bioradar is on top of the rubble pile. All objects in the environment reflect the transmitted radar signal. A non-trapped person and a vibrating tree are out of the range window.</p> "> Figure 5
<p>Block diagram of the software-defined radio bioradar. The SDR block is modified from [<a href="#B40-sensors-23-01335" class="html-bibr">40</a>]. Next Section shows a detailed version of the signal processing process.</p> "> Figure 6
<p>Signal processing procedure. Step 1, pre-processing, includes four sub-steps: <span class="html-italic">N</span> times FFT, <span class="html-italic">N</span> times IFFT, decompose the signal into <span class="html-italic">M</span> range intervals and keep the first <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math> sub-signals, and highpass filter them. In step 2, another FFT is applied to these sub-signals, respectively. The result of the second step is a plot and three values. Step 3 calculates the time-frequency distribution of the sub-signal from <math display="inline"><semantics> <msub> <mi>R</mi> <mrow> <mi>p</mi> <mi>e</mi> <mi>r</mi> <mi>s</mi> <mi>o</mi> <mi>n</mi> </mrow> </msub> </semantics></math> and results in a diagram and two values. With the results from steps 2 and 3, the decision about whether life is detected can be determined in Step 4.</p> "> Figure 7
<p>(<b>a</b>) Photograph of the Bioradar (adrv9364-z7020) with two Vivaldi antennas. (<b>b</b>) A test person sits in front of the test setup.</p> "> Figure 8
<p>For receiving: (<b>a</b>) One frame of the received signal. (<b>b</b>) The spectrum of this frame.</p> "> Figure 9
<p>(<b>a</b>) The 32 points of raw data of one frame, linear scale of <a href="#sensors-23-01335-f008" class="html-fig">Figure 8</a>b. (<b>b</b>) The IFFT of the magnitude in (<b>a</b>).</p> "> Figure 10
<p>(<b>a</b>) The real part of the time domain decomposed signal for the first three ranges of the measurement shown in <a href="#sensors-23-01335-f007" class="html-fig">Figure 7</a>. Range-1 <math display="inline"><semantics> <mrow> <mo>≈</mo> <mn>0</mn> <mspace width="4pt"/> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>2.5</mn> </mrow> </semantics></math> m, range-2 <math display="inline"><semantics> <mrow> <mo>≈</mo> <mn>2.5</mn> <mspace width="4pt"/> </mrow> </semantics></math> to 5 m, range-3 <math display="inline"><semantics> <mrow> <mo>≈</mo> <mn>5</mn> <mspace width="4pt"/> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>7.5</mn> </mrow> </semantics></math> m. (<b>b</b>) The corresponding filtered signals.</p> "> Figure 11
<p>(<b>a</b>) FFT of the filtered signals of the first three range bins shown in <a href="#sensors-23-01335-f010" class="html-fig">Figure 10</a>; (<b>b</b>) The breathing frequency–range plot.</p> "> Figure 12
<p>The CWT time-frequency distribution of the range-2 signal in <a href="#sensors-23-01335-f010" class="html-fig">Figure 10</a>b. (<b>a</b>) The distribution for frequency <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>∈</mo> </mrow> </semantics></math> [0, 1 Hz]. (<b>b</b>) The 1D representation of (<b>a</b>): the strongest peak of each time point. The mode value of the curve is marked in red.</p> "> Figure 13
<p>The CWT time–frequency distribution of the range-1 signal in <a href="#sensors-23-01335-f010" class="html-fig">Figure 10</a>b. (<b>a</b>) The distribution for frequency <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>∈</mo> </mrow> </semantics></math> [0, 1 Hz]. (<b>b</b>) The 1D representation of (<b>a</b>): The strongest peak of each time point. The mode value of the curve is marked in red.</p> "> Figure 14
<p>A bioradar experiment with two people. (<b>a</b>) Photograph of the experiment setup. Person 1 sits in the first range interval, and person 2 sits in the third range interval. (<b>b</b>) The breathing frequency–distance plot.</p> "> Figure 15
<p>Block diagram of the prototype bioradar system. Relevant software used on the Raspberry Pi is illustrated with white blocks.</p> "> Figure 16
<p>Measurement scenarios: (<b>a</b>) A tunnel covered with a wooden plate. (<b>b</b>) A concrete tube. (<b>c</b>) A building with wooden floors. The test person lay on the ground floor. The bioradar was placed on the first floor. (<b>d</b>) A building with reinforced concrete floors. The test person lay on the ground floor. The bioradar was placed on the first floor.</p> "> Figure A1
<p>GNU radio flow graph.</p> "> Figure A2
<p>Measurement Nr.1 in scenario shown in <a href="#sensors-23-01335-f016" class="html-fig">Figure 16</a>a: (<b>a</b>) top: preprocessed time-domain signal of the first range bin; bottom: FFT of the signal in the upper plot. (<b>b</b>) CWT of the signal in (<b>a</b>).</p> "> Figure A3
<p>Measurement Nr.3 in scenario shown in <a href="#sensors-23-01335-f016" class="html-fig">Figure 16</a>b: (<b>a</b>) top: preprocessed time-domain signal of the first range bin; bottom: FFT of the signal in the upper plot. (<b>b</b>) CWT of the signal in (<b>a</b>).</p> "> Figure A4
<p>Measurement Nr.5 in scenario shown in <a href="#sensors-23-01335-f016" class="html-fig">Figure 16</a>c: (<b>a</b>) top: preprocessed time-domain signal of the first range bin; bottom: FFT of the signal in the upper plot. (<b>b</b>) CWT of the signal in (<b>a</b>).</p> "> Figure A5
<p>Measurement Nr.7 in scenario shown in <a href="#sensors-23-01335-f016" class="html-fig">Figure 16</a>d: (<b>a</b>) top: preprocessed time-domain signal of the first range bin; bottom: FFT of the signal in the upper plot. (<b>b</b>) CWT of the signal in (<b>a</b>).</p> ">
Abstract
:1. Introduction
2. Basics
2.1. Bioradar Measuring Principle
2.2. Frequency Comb CW Radar
- Requirement 1:
- Requirement 2:
2.3. Ground-Penetrating Bioradar
3. System Description: SDR-Based Bioradar
4. Signal Processing
4.1. Pre-Processing: Radar Frequency Domain to Spatial Domain
4.2. FFT: Time Domain to Breathing Frequency Domain
4.3. Continuous Wavelet Transform (CWT): Time–Frequency Analysis
5. Experiment with Two Persons
6. Field Measurement
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. GNU Radio Flow Graph
Appendix B. Field Measurement Plots
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Parameter | Symbol | Value |
---|---|---|
sampling frequency | MHz | |
number of tones in the frequency comb | M | 32 |
number of samples in waveform | L | 4096 |
bandwidth | MHz | |
start frequency | MHz | |
stop frequency | MHz |
Parameter | Symbol | Value |
---|---|---|
sampling frequency | MHz | |
LO-frequency of the receiver | GHz | |
LO-frequency of the transmitter | GHz | |
transmitting waveform | see Equation (5) and Table 1 | |
number of samples per receiving frame | L | 4096 |
number of frames | N | 20k |
frame rate | 225 Hz | |
measurement duration | about 86 s |
Nr. | Scenario | Life Detected | ||||
---|---|---|---|---|---|---|
1 | (a) | Hz | Hz | dB | yes | |
2 | (a) | Hz | Hz | dB | yes | |
3 | (b) | Hz | Hz | dB | yes | |
4 | (b) | Hz | Hz | dB | no | |
5 | (c) | Hz | Hz | dB | yes | |
6 | (c) | Hz | Hz | dB | yes | |
7 | (d) | Hz | Hz | dB | no | |
8 | (d) | Hz | Hz | dB | no |
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Shi, D.; Gidion, G.; Aftab, T.; Reindl, L.M.; Rupitsch, S.J. Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing. Sensors 2023, 23, 1335. https://doi.org/10.3390/s23031335
Shi D, Gidion G, Aftab T, Reindl LM, Rupitsch SJ. Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing. Sensors. 2023; 23(3):1335. https://doi.org/10.3390/s23031335
Chicago/Turabian StyleShi, Di, Gunnar Gidion, Taimur Aftab, Leonhard M. Reindl, and Stefan J. Rupitsch. 2023. "Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing" Sensors 23, no. 3: 1335. https://doi.org/10.3390/s23031335