A Photon Counting Lidar Signal Processing Method and System

Abstract

This application belongs to the field of three-dimensional imaging of underwater photon counting lidar, and specifically relates to a signal processing method and system for photon counting lidar, and proposes to use a multi-scale spatio-temporal correlation method to achieve high-precision and high-resolution three-dimensional imaging of underwater targets. By the macro pulse accumulation number and the macro time resolution, a macro histogram is established. Although a small part of the system resolution is sacrificed, the time-domain aggregation characteristics and spatial correlation of the target echo signal are effectively utilized, thereby improving the detection probability of the signal. Subsequently, a micro histogram is established using the micro time resolution and the micro pulse accumulation number, which will enhance the accuracy and spatial resolution of the three-dimensional imaging of underwater targets and achieve refined detection of underwater targets.

Description

Technical Field

[0001] This application belongs to the field of three-dimensional imaging of underwater photon counting lidar, and specifically relates to a method and system for processing photon counting lidar signals. Background Technique

[0002] Different from land-based systems, the complex underwater environment requires a more portable, smaller and lower-power underwater detection system. Photon counting detection technology effectively meets these requirements. Compared with traditional methods of increasing the receiving telescope aperture or laser energy, photon counting detection technology has single-photon detection sensitivity, enabling the miniaturization and low-power design of lidar systems. The core component of this lidar system is a single-photon avalanche diode (SPAD) detector. Due to the lack of photon number resolution ability of SPAD, photon counting detection technology usually combines the SPAD detector with time-correlated single-photon counting (TCSPC) technology to extract signals. This method establishes a statistical histogram by repeatedly recording photon counting events and the emission times of their corresponding laser pulses. In a point-by-point scanning photon counting lidar system, the time resolution of the histogram directly affects the ranging accuracy and detection probability. At the same time, the number of pulse accumulations in the histogram affects the spatial resolution of 3D imaging. To improve the time resolution, picosecond-level photon counting modules and picosecond pulse laser sources have been widely used.

[0003] In the underwater environment, the pulsed echo laser will be broadened due to the forward scattering effect of water. High time resolution makes the influence of detector jitter, timing module jitter and pulse broadening on the system timing error more significant. Therefore, the target echo photon counting events are discretely distributed in multiple time bins. To improve the detection probability of signals, usually the number of pulse accumulations per pixel is increased, however this will significantly affect the imaging speed of the system. Especially, in a scanning imaging system, if the scanning speed of the system remains constant, increasing the number of pulse accumulations per pixel will greatly reduce the spatial resolution. Summary of the Invention

[0004] Based on the above problems, this application provides a spatio-temporal resolution underwater photon counting lidar signal processing method and system that can improve the signal extraction accuracy with micro time resolution and improve the target spatial detection accuracy with micro pulse accumulations. Its technical solution is as follows:

[0005] A method for processing photon counting lidar signals, including the following steps:

[0006] S1. Conduct point-by-point scanning detection on an underwater target to obtain an echo signal;

[0007] S2. Make the underwater photon counting lidar complete a scan of α angles per row at a speed of v. When the pulse repetition frequency of the laser is F, each row has A pulsed laser emits, scanning a total of M rows;

[0008] S3. Uniformly divide all the pulsed laser echoes of each row with the macro-pulse accumulation number K to obtain pixel points;

[0009] S4. For each pixel point, use spatial correlation to extract the signal. A pixel block is composed of the pixel (i, j) and its neighboring pixels, and the pixel (i, j) is defined as the central pixel;

[0010] S5. Based on the macro-time resolution and the macro-pulse accumulation number, establish a macro-histogram for each pixel in the pixel block. Use spatial correlation to filter out noise and extract the target echo signal for the central pixel;

[0011] S6. Use the micro-pulse accumulation number to expand the central pixel into multiple pixels. Establish a micro-histogram with the micro-time resolution. Determine the target distance information for the signal echo of each micro-histogram through the maximum likelihood estimation method.

[0012] Preferably, for the N×M pixel points obtained by scanning the underwater photon counting lidar, perform edge pixel filling. All filled pixels are 0, so that the edge pixels obtained by scanning can perform spatial correlation processing.

[0013] Preferably, in step S5, set the macro-time resolution to the full width at half maximum of the system device response function. Most echo photons are concentrated within the 6σ range of the device response function, covering approximately 4 macro-time resolutions, where σ is the standard deviation of the device response function.

[0014] Preferably, in step S5, accumulate all the macro-histograms of the pixel block to obtain the pixel block macro-histogram. Take the peak position of the pixel block macro-histogram and its ±3 time intervals as the position of the central pixel target echo signal.

[0015] Preferably, in step S5, assign the peak position of the pixel block macro-histogram and its ±3 time gates as '1', and assign the remaining time gates as '0' to establish the macro-histogram template of the pixel block.

[0016] Preferably, in step S5, perform an 'AND' operation on the established pixel block macro-histogram template and the macro-histogram of the central pixel to extract the central pixel target echo.

[0017] Preferably, in step S6, the micro-time resolution is the smallest integer greater than the inherent time jitter of the single photon detector and the photon counting acquisition card.

[0018] A photon counting lidar signal processing system includes a signal acquisition module, a signal processing module, and a signal output module;

[0019] Signal acquisition module: The underwater photon counting lidar is used to perform point-by-point scanning detection on underwater targets, and the echo signal is collected and stored based on a photon counting acquisition card.

[0020] Signal processing module: A macro histogram is established through the macro pulse accumulation number and the macro time resolution; a micro histogram is established using the micro time resolution and the micro pulse accumulation number to enhance the accuracy and spatial resolution of underwater target three-dimensional imaging; for the signal echo of each micro histogram, the target distance information is determined by the maximum likelihood estimation method; Signal output module: Visualize and output the signal.

[0021] Preferably, the macro pulse accumulation number is comprehensively determined according to the detection probability, scanning speed and pulse repetition frequency of the system. The macro pulse accumulation number K = f(P, v, F), where P represents the detection probability of the expected macro pixel, v represents the scanning speed, and F represents the pulse repetition frequency; the order of magnitude of the micro pulse accumulation number is not greater than that of the macro pulse accumulation number with a smaller value.

[0022] Preferably, the signal processing module combines the macro time resolution and the macro pulse accumulation number with spatial correlation to improve the signal extraction detection probability and filter out noise counts; further, the micro time resolution and the micro pulse accumulation number are used to process the data after noise filtering.

[0023] Compared with the prior art, the beneficial effects of this application are as follows:

[0024] The algorithm designed by the present invention is for signal extraction of a point-by-point uniform scanning photon counting lidar. For a point-by-point uniform scanning photon counting lidar, if the detection probability needs to be improved by increasing the pulse accumulation number, the lateral resolution of the system will be correspondingly reduced, affecting the system's detection ability for target details. The existing system usually only completes the change of coarse and fine time resolutions on the time scale. On this basis, the present invention adds the change of macro and micro pulse accumulation numbers on the spatial scale. Combining the coarse time resolution and the coarse pulse accumulation number with spatial correlation to improve the signal extraction detection probability and filter out noise counts. Subsequently, the micro time resolution and the micro pulse accumulation number are further used to process the data after noise filtering, so that both the signal extraction accuracy can be improved with the micro time resolution, and the target space detection accuracy can be improved with the micro pulse accumulation number to obtain more detailed information. Description of the drawings

[0025] Figure 1 It is a schematic diagram of the processing flow of this application.

[0026] Figure 2 It is a schematic diagram of a pixel block and a central pixel.

[0027] Figure 3 It is an imaging effect diagram of a coral model, where (a) is the imaging effect of the macro histogram and (b) is the imaging effect of the micro histogram. Detailed implementation mode

[0028] The technical solution of the present application will be described in detail below through specific embodiments and the accompanying drawings. It should be understood that the specific features in the embodiments of the present application are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. The specific technical features can be combined with each other.

[0029] Figure 1-2 As shown, a method for processing photon counting lidar signals includes the following steps:

[0030] S1. Use an underwater photon counting lidar to perform point-by-point scanning detection on an underwater target, and collect and store the echo signal based on a photon counting acquisition card.

[0031] S2. Make the underwater photon counting lidar complete a scan of α angles per row at a speed of v. When the pulse repetition frequency of the laser is F, there are pulses of laser emission per row, and a total of M rows are scanned.

[0032] Perform edge pixel padding (padding) on the N×M pixel points obtained by scanning the underwater photon counting lidar, and all filled pixels are 0. The purpose is to enable spatial correlation processing of the edge pixels obtained by scanning.

[0033] S3. Uniformly divide the echo of all pulse lasers per row with the macro pulse accumulation number K to obtain pixel points; in this way, the underwater photon counting lidar completes the detection of N×M pixel points of the underwater target.

[0034] S4. Use spatial correlation to extract signals for each pixel point. A pixel block is composed of the pixel (i, j) and its neighboring pixels, and the pixel (i, j) is defined as the central pixel; as Figure 2 shown.

[0035] S5. Establish a macro histogram for each pixel in the pixel block based on the macro time resolution and the macro pulse accumulation number. On the basis of the macro histogram, use spatial correlation to filter out noise and extract the target echo signal for the central pixel. According to the statistical characteristics of the signal and noise, optimize the macro time gate size to make the signal concentrated and the noise not overly aggregated. In the present invention, the macro time resolution is set to the full width at half maximum of the system device response function. Most echo photons are concentrated within the 6σ range of the device response function, covering approximately 4 macro time resolutions, where σ is the standard deviation of the device response function.

[0036] Since the signals in the macro histogram will be concentrated within one or several time gates, while the noise is evenly distributed throughout the sampling period. At the same time, considering the distance differences between adjacent pixels, all the macro histograms of the pixel block are accumulated to obtain the macro histogram of the pixel block, and the peak position of the macro histogram of the pixel block and its ±3 time intervals are used as the position of the target echo signal of the central pixel.

[0037] S6. Use the micro-pulse accumulation number to expand the central pixel into multiple pixels, establish a micro histogram, and determine the target distance information for the signal echo of each micro histogram through the maximum likelihood estimation method.

[0038] The selection of the micro time resolution is determined by the inherent time jitter of the system's single-photon detector and photon-counting acquisition card, and the smallest integer greater than the inherent time jitter of the single-photon detector and photon-counting acquisition card is selected.

[0039] The macro-pulse accumulation number is comprehensively determined according to the detection probability, scanning speed, and pulse repetition frequency of the system. The macro-pulse accumulation number K = f(P, v, F), where P represents the detection probability of the expected macro pixel, v represents the scanning speed, and F represents the pulse repetition frequency; the order of magnitude of the micro-pulse accumulation number is not greater than that of the macro-pulse accumulation number with a smaller value. Improve the detection of target detail information.

[0040] An underwater photon-counting lidar signal processing system includes a signal acquisition module, a signal processing module, and a signal output module;

[0041] Signal acquisition module: The underwater photon-counting lidar is used to perform point-by-point scanning detection on underwater targets, and the echo signals are collected and stored based on a photon-counting acquisition card; the photon-counting lidar signals are extracted by point-by-point uniform scanning.

[0042] Signal processing module: Establish a macro histogram through the macro-pulse accumulation number and macro time resolution; establish a micro histogram using the micro time resolution and micro-pulse accumulation number to enhance the accuracy and spatial resolution of underwater target three-dimensional imaging; for the signal echo of each micro histogram, determine the target distance information through the maximum likelihood estimation method; the signal processing module combines the coarse time resolution, coarse pulse accumulation number with spatial correlation to improve the signal extraction detection probability and filter out noise counts; further process the noise-filtered data using the micro time resolution and micro-pulse accumulation number;

[0043] Signal output module: Visually output the signals.

[0044] Figure 3It is the imaging effect diagram of the coral model. Among them, (a) is the imaging effect of the macro histogram, and it can be found that only the rough outline of the coral can be observed, and the spatial resolution of the imaging is relatively low. While (b) is the imaging effect of the micro histogram. Obviously, the three-dimensional outline of the coral is clearer, and multiple branches of the coral can also be clearly distinguished, realizing more refined three-dimensional imaging.

[0045] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and deformations can be made, and these improvements and deformations should also be regarded as the protection scope of the present application.

Claims

1. A photon counting laser radar signal processing method, characterized in that: The following steps are involved: S1. Scan and detect underwater targets point by point to obtain echo signals; S2. Make the underwater photon counting laser radar complete the scanning of each line at an angle of α at a speed of v. When the pulse repetition frequency of the laser is F, each line has a total of F pulses of laser are emitted, scanning M lines in total; S3. Evenly divide all the pulse laser echoes in each row by the macro pulse accumulation number K to obtain pixels; S4. extracting signals from each pixel using spatial correlation, forming a pixel block with pixel (i, j) and its neighboring pixels, and defining pixel (i, j) as the center pixel; S5. Establish a macro histogram for each pixel in the pixel block based on the macro time resolution and the accumulated number of macro pulses, and use spatial correlation to filter out noise and extract the target echo signal for the central pixel; S6. The central pixel is expanded into multiple pixels using the accumulated number of micro-pulses, a micro-histogram is established with micro-time resolution, and the target distance information is determined by the maximum likelihood estimation method for the signal echo of each micro-histogram.

2. The photon counting laser radar signal processing method according to claim 1, characterized in that: The N×M pixel points obtained by the underwater photon counting lidar scan are filled with edge pixels, and all filled pixels are 0, so that the edge pixels obtained by the scan can be processed with spatial correlation.

3. The photon counting laser radar signal processing method according to claim 1, characterized in that: In step S5, the macro time resolution is set to the half-maximum full width of the system device response function, and most echo photons are concentrated within the 6σ range of the device response function, covering approximately 4 macro time resolutions, where σ is the standard deviation of the device response function.

4. The photon counting laser radar signal processing method according to claim 1, characterized in that: In step S5, all macrohistograms of the pixel block are accumulated to obtain a macrohistogram of the pixel block, and the peak position of the macrohistogram of the pixel block and its ±3 time intervals are used as the position of the target echo signal of the central pixel.

5. The photon counting laser radar signal processing method according to claim 1, characterized in that: In step S5, the peak position of the macrohistogram of the pixel block and its ±3 time gates are assigned values ​​of '1', and the remaining time gates are assigned values ​​of '0', so as to establish a macrohistogram template of the pixel block.

6. The photon counting laser radar signal processing method according to claim 1, characterized in that: In step S5, an AND operation is performed between the established macrohistogram template of the pixel block and the macrohistogram of the central pixel to extract the target echo of the central pixel.

7. The photon counting laser radar signal processing method according to claim 1, characterized in that: In step S6, the micro time resolution is greater than the minimum integer of the inherent time jitter of the single photon detector and the photon counting acquisition card.

8. A photon counting laser radar signal processing system, using the photon counting laser radar signal processing method according to any one of claims 1 to 7, characterized in that: It includes a signal acquisition module, a signal processing module and a signal output module; Signal acquisition module: Use underwater photon counting laser radar to scan and detect underwater targets point by point, and collect and store echo signals based on photon counting acquisition card; Signal processing module: establish macro histogram through macro pulse accumulation number and macro time resolution; Use micro-time resolution and micro-pulse accumulation number to establish micro-histogram to enhance the accuracy and spatial resolution of underwater target three-dimensional imaging; For each signal echo of the microhistogram, the target distance information is determined by the maximum likelihood estimation method; Signal output module: output the signal visually.

9. The photon counting laser radar signal processing system according to claim 8, characterized in that: The accumulated number of macro pulses is determined comprehensively according to the detection probability, scanning speed and pulse repetition frequency of the system. The accumulated number of macro pulses is K=f(P,v,F), where P represents the detection probability of the expected macro pixel, v represents the scanning speed, and F represents the pulse repetition frequency. The order of magnitude of the accumulated number of micro pulses is not greater than the order of magnitude of the accumulated number of macro pulses.

10. The photon counting laser radar signal processing system according to claim 8, characterized in that: The signal processing module combines macro time resolution and macro pulse accumulation number with spatial correlation to improve signal extraction detection probability and filter out noise counts; and further uses micro time resolution and micro pulse accumulation number to process the noise-filtered data.

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