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Macro/sub-pulse coded photon counting LiDAR
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Abstract
Photon counting LiDAR plays an important role in the long-distance target measurement because of the high detection sensitivity. For the targets with high radial velocity and long distance, ordinary photon counting LiDAR could not recover the useful echo information simply by statistical histogram. In order to solve this problem, a method based on macro/sub-pulse coded photon counting LiDAR is proposed. The flight time of the subpulses is extracted by time shift pulse accumulation and the target distance information is obtained in one macro pulse. In this paper, the theoretical model of macro/sub-pulse coded photon counting LiDAR is established, and the influence of false alarm probability and detection probability is analyzed. The effectiveness of the LiDAR is verified by Monte Carlo simulation and actual experiments.-
Keywords:
- macro/sub-pulse /
- LiDAR /
- photon counting /
- target measurement
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References
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Overview
Overview: The photon counting LiDAR plays an important role in the long distance measurement because of the high sensitivity to a single photon and the ability of providing accurate photon arrival time. It uses statistical sampling technology which needs to accumulate enough photon events to establish a statistical histogram and extract echo information through the histogram. However, the process will greatly reduce the measurement speed of the system. If there is a relative movement between the system and target, the laser pulses of multiple cycles will have different flight time. Then it can be difficult to extract the distance of the target as the echo signals are difficult to reflect the clustering characteristics in time. In order to solve this problem, a macro/sub-pulse coded photon counting LiDAR is proposed. The measurement speed of the macro/sub-pulse method is determined by the total time of all sub-pulses in the period. Compared with pulse accumulation, the macro/sub-pulse method can realize fast measurement. In the system, the emitting pulse is divided into two parts by a proportional beam splitter, one part is directly detected by PIN and used as the transmitting reference signal, and the other part is used to detect targets. Echo signals scattered by the target are received by optical system and detected by GM-APD (Geiger-mode avalanche photodiode). It should be pointed out that in the macro/sub-pulse LiDAR system, any two sub-pulses have different pulse intervals, which can effectively avoid distance blur. In this paper, the theoretical model of macro/sub-pulse coded photon counting LiDAR is established. To obtain the distance of the target, a method which accumulates the sub-pulses with different time shift operations was proposed in this article. For the time-shifted pulse accumulation method, there is no special requirement for the received signal, but the sub-pulse interval of the transmitted signal needs to be known in advance. To meet this requirement, a PIN detector is used to record the transmitting sequence. Within a period, the echo signals detected by GM-APD detector are shifted sequentially according to the interval of sub-pulses, and the sequentially shifted echo signals are accumulated. The position of the cumulative peak corresponds to the flight time of the sub-pulse. Also, in the third part of this article, the influence of false alarm probability and detection probability were analyzed. The effectiveness of macro/sub-pulse coded photon counting LiDAR is verified by Monte Carlo simulation and experiment.
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Figure 1.
Structure of macro/sub-pulse coded photon counting LiDAR system
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Figure 2.
Time shift pulse accumulation flight time extraction method
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Figure 3.
The effect of target motion on the pulse accumulation effect in the case of fine time gate (a) and coarse time gate (b)
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Figure 4.
Influence of noise level on detection probability (N=20, td=25 ns, tbin=20 ns)
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Figure 5.
Influence of time door width on the false alarm probability (N=20, td=25 ns, ψn=1 Mcps)
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Figure 6.
Influence of signal recognition threshold on detection probability (N=20, td=25 ns, ψn=1 Mcps)
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Figure 7.
Accumulation histogram of macro/sub-pulse method
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Figure 8.
Detecting simulation of macro/sub-pulse method under different echo signal intensity. (a) Monopulse detection probability of 30%; (b) Monopulse detection probability of 40%; (c) Monopulse detection probability 50%
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Figure 9.
Experimental schematic diagram of high-speed moving target detection
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Figure 10.
Equivalent long-distance high-speed moving target experimental platform
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Figure 11.
Experimental results of macro/sub-pulse method under different echo signal intensity. (a) Monopulse detection probability of 30%; (b) Monopulse detection probability of 40%; (c) Monopulse detection probability of 50%
