Improving the Performance of Pseudo-Random Single-Photon Counting Ranging Lidar
<p>Schematic of pseudo-random ranging system.</p> "> Figure 2
<p>The schematic diagram of pseudo-random ranging principle. (<b>a</b>) The transmitted pseudo-random laser pulse sequence (reference signal); (<b>b</b>) The detected pulse sequence of the target response; (<b>c</b>) The auto-correlation function of the reference and the target response.</p> "> Figure 3
<p>The detection probability of each code in pseudo-random single-photon counting ranging (PSPCR) Lidar system and the cross-correlation function. (<b>a</b>,<b>d</b>) are the PSPCR method detection probabilities of theory derivation while the dead time is 0 and 45 ns, respectively; (<b>b</b>,<b>e</b>) are the PSPCR method detection probabilities of Monte Carlo simulation while the dead time is 0 and 45 ns, respectively; (<b>c</b>,<b>f</b>) are the normalized cross-correlations of the PSPCR method.</p> "> Figure 4
<p>The schematic diagram of the modulated pseudo-random sequence.</p> "> Figure 5
<p>The detection probability of the traditional PSPCR Lidar and modulation-encoded PSPCR Lidar at different primary photoelectron number.</p> "> Figure 6
<p>The signal photon detection efficiency of the traditional PSPCR Lidar and modulation-encoded PSPCR Lidar at different primary photoelectron number.</p> "> Figure 7
<p>Cross-correlation range images with three different levels of noise photoelectrons. The first column is the Monte Carlo simulation of the traditional pseudo-random sequence, while the second is the modulated pseudo-random sequence. The noise levels of (<b>a</b>–<b>c</b>) are represented by the mean number of photoelectron noise. They are <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math> per bit, respectively.</p> "> Figure 8
<p>Experiment platform for the modulation-encoded PSPCR Lidar and the traditional PSPCR Lidar.</p> "> Figure 9
<p>Cross-correlation range images with three different echo photon numbers for the traditional PSPCR Lidar and the modulation-encoded PSPCR Lidar. The first column is the traditional PSPCR Lidar, and the second column is the modulation-encoded PSPCR Lidar. The mean echo photon number per ‘1’ bit in (<b>a</b>), (<b>b</b>) and (<b>c</b>) is 1, 3, and 5, respectively.</p> "> Figure 9 Cont.
<p>Cross-correlation range images with three different echo photon numbers for the traditional PSPCR Lidar and the modulation-encoded PSPCR Lidar. The first column is the traditional PSPCR Lidar, and the second column is the modulation-encoded PSPCR Lidar. The mean echo photon number per ‘1’ bit in (<b>a</b>), (<b>b</b>) and (<b>c</b>) is 1, 3, and 5, respectively.</p> "> Figure 10
<p>The detection probability statistical results of the modulation-encoded PSPCR Lidar and the traditional PSPCR Lidar at different mean echo photon number.</p> ">
Abstract
:1. Introduction
2. The Theoretical Analysis
2.1. Pseudo-Random Ranging Theory
2.2. Dead Time Effects on Pseudo-Random Single Photon Ranging
3. Improved Pseudo-Random Coding Single-Photon Detection Method
3.1. The Modulated Pseudo-Random Sequence
3.2. Detection Performance Analysis
3.2.1. Detection Probability
3.2.2. Signal Photon Detection Efficiency
3.3. Monte Carlo Simulation
4. Experiment
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
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Bit width | 1 ns | Bits of M sequence | |
Mean signal photoelectrons per bit | 1 | Dead time | 45 ns |
Mean noise photoelectrons per bit |
Photon detection efficiency of GM-APD | 2% | Bits of M sequence | |
Bit width | 4 ns | Dead time | 40 ns |
Noise Count | 1Mcps | Time resolution of TCSPC module | 64 ps |
Wavelength | 1064 nm |
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Yu, Y.; Liu, B.; Chen, Z. Improving the Performance of Pseudo-Random Single-Photon Counting Ranging Lidar. Sensors 2019, 19, 3620. https://doi.org/10.3390/s19163620
Yu Y, Liu B, Chen Z. Improving the Performance of Pseudo-Random Single-Photon Counting Ranging Lidar. Sensors. 2019; 19(16):3620. https://doi.org/10.3390/s19163620
Chicago/Turabian StyleYu, Yang, Bo Liu, and Zhen Chen. 2019. "Improving the Performance of Pseudo-Random Single-Photon Counting Ranging Lidar" Sensors 19, no. 16: 3620. https://doi.org/10.3390/s19163620