IPDA laser radar detection model construction method and device

Abstract

The invention provides an IPDA laser radar detection model construction method and device. The method provided by the invention comprises the following steps: simulating laser pulse emission based on a state matrix to obtain a first laser pulse; simulating radiation energy loss of the first laser pulse in the transmission process to obtain a second laser pulse; establishing a detector instrument model, and taking the sum of the performance degradation degree and the detection error as the system error of the detector; calculating first noise detected by the detector, calculating detection noise and signal processing noise of the detector, and calculating total noise of the detector based on the sum of the first noise, the detection noise and the signal processing noise; receiving the second laser pulse based on the response curve to obtain a predicted response; and correcting the prediction response according to the detection error and the total noise to obtain an output result of the detector. According to the IPDA laser radar detection model construction method and device provided by the invention, the performance of an IPDA laser radar detection system can be analyzed more comprehensively.

Description

Technical Field

[0001] The present application relates to the technical field of remote sensing monitoring of atmospheric environment, and in particular to a method and device for constructing an IPDA laser radar detection model. Background Art

[0002] The atmosphere contains a variety of gases, among which carbon dioxide is the most important greenhouse gas in the atmosphere. Its rising concentration and strong positive radiative forcing effect seriously affect the earth's energy balance, leading to the continuous intensification of the global greenhouse effect. Therefore, high-precision and high-accuracy real-time monitoring of carbon dioxide is required.

[0003] At present, the main means of monitoring carbon dioxide are in-situ monitoring, passive remote sensing and active remote sensing. Compared with in-situ instruments, remote sensing technology has the advantages of wide field of view, high automation, high efficiency of information acquisition, comprehensive parameters, etc. It is also convenient for data standardization and networking of observations. Carrying remote sensing technology on a satellite platform can carry out large-scale sampling and increase the amount of carbon dioxide data obtained. The performance evaluation study of the satellite-borne integrated path differential absorption (IPDA) lidar shows that it can monitor atmospheric CO2 on a global scale. 2 The IPDA laser radar can detect carbon content with an accuracy better than sub-ppm, and is listed as the first choice for the next generation of "sniffing carbon" satellite payloads by aerospace agencies around the world. At present, the research on IPDA laser radar mainly focuses on sensitivity testing and error analysis of single or multiple laser parameters and environmental parameters, lacks bionic analysis of the whole system parameters, and cannot deeply understand CO 2 Its role in climate, meteorology, etc. Summary of the invention

[0004] In view of this, the present application provides a method and device for constructing an IPDA laser radar detection model to solve the problem of insufficient system analytical performance of the currently constructed IPDA laser radar simulation model.

[0005] Specifically, the present application is implemented through the following technical solutions:

[0006] The first aspect of the present application provides a method for constructing an IPDA laser radar detection model, the method comprising:

[0007] Simulate laser pulse emission based on the state matrix to obtain a first laser pulse;

[0008] Simulating the radiation energy loss of the first laser pulse during transmission based on molecular absorption, particle scattering and surface reflection to obtain a second laser pulse;

[0009] Establishing a detector instrument model, calculating the performance degradation degree of the detector based on the detector instrument model parameters, calculating the detection error of the detector under various working scene state parameters in the state of performance degradation, and taking the sum of the performance degradation degree and the detection error as the system error of the detector;

[0010] Calculating a first noise detected by the detector, where the first noise is affected by a radiation source; calculating a detection noise and a signal processing noise of the detector, and calculating a total noise of the detector based on the sum of the first noise, the detection noise and the signal processing noise;

[0011] Establishing a response curve of the detector, receiving the second laser pulse based on the response curve, and obtaining a predicted response;

[0012] The predicted response is corrected according to the detection error and the total noise to obtain the output result of the detector.

[0013] The second aspect of the present application provides an IPDA laser radar detection model construction device, the device is used to implement the method provided in the first aspect of the present application, the device includes a transmitting module, a transmission module and a receiving module;

[0014] The transmitting module is used to simulate the emission of laser pulses based on the state matrix to obtain a first laser pulse;

[0015] The transmission module is used to simulate the radiation energy loss of the first laser pulse during the transmission process based on molecular absorption, particle scattering and surface reflection to obtain a second laser pulse;

[0016] The transmission module is further used to establish a detector instrument model, calculate the performance degradation degree of the detector based on the detector instrument model parameters, calculate the detection error of the detector under various working scene state parameters when the detector is in a state of performance degradation, and use the sum of the performance degradation degree and the detection error as the system error of the detector;

[0017] The transmission module is further used to calculate a first noise detected by the detector, the first noise being affected by a radiation source; calculate a detection noise and a signal processing noise of the detector, and calculate a total noise of the detector based on the sum of the first noise, the detection noise and the signal processing noise;

[0018] The receiving module is used to establish a response curve of the detector, receive the second laser pulse based on the response curve, and obtain a predicted response;

[0019] The receiving module is further used to correct the predicted response according to the detection error and the total noise to obtain the output result of the detector.

[0020] The IPDA laser radar detection model construction method and device provided in this application obtains a first laser pulse by simulating the laser pulse emission based on the state matrix, and then considers molecular absorption, particle scattering and surface reflection to simulate the radiation energy loss during its transmission process to obtain a second laser pulse, which can highly restore the propagation of the laser in the actual atmospheric environment. By establishing a detector instrument model, calculating the degree of performance degradation and detection error, and taking the sum of the two as the system error, the performance changes of the detector under long-term use and different working scenarios can be comprehensively considered. This helps to promptly discover possible problems with the detector, take measures in advance for maintenance or calibration, and ensure the reliability and stability of the detector. Secondly, the first noise (affected by the radiation source), detection noise and signal processing noise are calculated respectively, and the total noise is summed to obtain the source and composition of the detector noise. This helps to take targeted measures to reduce the impact of noise, such as by optimizing the detector design, shielding the radiation source or improving the signal processing algorithm, improving the signal-to-noise ratio of the detector, receiving the second laser pulse based on the detector response curve to obtain a predicted response, and then correcting the predicted response according to the detection error and total noise, which can effectively reduce the impact of errors and noise on the detector output results. This makes the final output result closer to the true value and improves the measurement accuracy of the entire lidar system. In practical applications, such as high-precision monitoring of greenhouse gas concentrations in the atmosphere, accurate detector output results can provide reliable data for climate change research, environmental assessment, etc. In addition, accurate measurement results can also be used to verify and improve atmospheric models, further promoting scientific research and technological development in related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of the first embodiment of the IPDA laser radar detection model construction method provided in this application;

[0022] Figure 2 This is a structural schematic diagram of Example 1 of the IPDA laser radar detection model building device provided in this application. DETAILED DESCRIPTION

[0023] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0024] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0025] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0026] Specific embodiments are given below to introduce the technical solution of the present application in detail.

[0027] Figure 1 This is a flowchart of the first embodiment of the IPDA laser radar detection model construction method provided in this application. Please refer to Figure 1 , the method provided in this embodiment may include:

[0028] S101, simulating laser pulse emission based on a state matrix to obtain a first laser pulse.

[0029] It should be noted that IPDA (Intergrated Path Differential Absorption) LiDAR is an advanced LiDAR technology used in fields such as atmospheric detection. It emits two or more laser pulses with very close wavelengths, one of which is located at the center of the absorption line of the detected gas, and the other one or more wavelengths are located at the edge of the absorption line or far away from the absorption line. By measuring the echo energy of these laser pulses with different wavelengths and using the difference in gas absorption of lasers with different wavelengths, the concentration of the target gas in the atmosphere can be inverted.

[0030] Specifically, the specific implementation steps of simulating the laser pulse emission based on the state matrix to obtain the first laser pulse include:

[0031] (1) Calculate the state matrix of the detector based on the detector system model.

[0032] Specifically, the detector system model mainly describes the emission state of the pulsed laser, the response characteristics of the detector, and the sensitivity of the system to parameter degradation, which can be used to characterize the process of laser emission and detector response in forward simulation. The detector state matrix is ​​a 3*1 matrix, each row of which represents the laser emission system, receiving system and working scene state parameters of the IPDA lidar. The laser emission system parameters include laser wavelength, laser energy, laser line width, etc. The receiving system parameters include detector response time, detector bandwidth, detector noise equivalent power, etc. The working scene state parameters include temperature, humidity, pressure profile, etc.

[0033] Furthermore, the state matrix of the detector is constructed by organizing the relevant parameters in the detector system model into a matrix.

[0034] (2) Calculating the detector's transmit response curve and receive response curve based on the state matrix.

[0035] Specifically, the detector emission response curve describes the relationship between the output signal and the input signal when the detector emits a signal. Through the parameters in the state matrix, the emission output characteristics of the detector under different input conditions can be calculated to obtain the emission response curve.

[0036] Furthermore, the receiving response curve of the detector represents the relationship between the output signal and the input signal when the detector receives a signal. The receiving response curve can be calculated based on the parameters in the state matrix, such as the photoelectric conversion efficiency and sensitivity of the detector.

[0037] (3) Calculating the time domain characteristics and frequency domain characteristics of the emitted laser according to the emission response curve.

[0038] Specifically, the time domain characteristics of the emitted laser can be calculated according to the following formula:

[0039]

[0040] Among them, t is the time variable;

[0041] a=1 / 4(1 / τ r -1 / τ f );

[0042] b = 1 / 4(1 / τ r +1 / τ f );

[0043] τ r is the rise time of the laser pulse;

[0044] τ f is the fall time of the laser pulse.

[0045] Furthermore, the frequency domain characteristics of the emitted laser can be obtained by performing Fourier transform on the time domain characteristic function of the emitted laser and integrating the time t in the time domain characteristic function from negative infinity to positive infinity.

[0046] (4) Calculating the frequency half-width value of the emitted laser based on the time domain characteristics and frequency characteristics.

[0047] Specifically, the frequency half-maximum full width of the emitted laser can be calculated by the following formula:

[0048]

[0049] in

[0050] (5) The laser pulse emission process is simulated based on the frequency half-width value to obtain a first laser pulse as a transmission module of the laser radar detection model.

[0051] Specifically, the calculated frequency half-width value and other relevant parameters (such as the wavelength and energy of the laser) are used to simulate the emission process of the laser pulse by establishing a mathematical model or using simulation software to obtain a laser pulse that conforms to the actual emission situation. For the sake of convenience, this laser pulse is recorded as the first laser pulse.

[0052] S102 , simulating the radiation energy loss of the first laser pulse during transmission based on molecular absorption, particle scattering and surface reflection to obtain a second laser pulse.

[0053] Specifically, simulating the radiation energy loss of the first laser pulse during the transmission process based on molecular absorption includes:

[0054] (1) Calculating the average velocity of atmospheric molecular motion based on the Gaussian line shape, and obtaining Doppler broadening based on the average velocity of atmospheric molecular motion.

[0055] Specifically, the Gaussian line shape is used to describe the broadening of the spectral line caused by the thermal motion of molecules. The width of the Gaussian line shape is related to the speed of molecular motion. When the light emitted by the first laser pulse interacts with the moving atmospheric molecules, the frequency of the light received by the molecules will change due to the Doppler effect, resulting in the broadening of the spectral line, which is the Doppler broadening. The average speed of atmospheric molecular motion is one of the key factors in calculating Doppler broadening. The size of the Doppler broadening is proportional to the average speed of molecular motion. The greater the speed, the more obvious the Doppler broadening.

[0056] Furthermore, for the specific implementation steps of calculating the average velocity of atmospheric molecules based on the Gaussian line shape and obtaining Doppler broadening based on the average velocity of atmospheric molecules, please refer to the description in the relevant technology, which will not be repeated here.

[0057] (2) Calculate the pressure broadening based on the phase mutation caused by the collision.

[0058] Specifically, in the atmosphere, molecules will constantly collide with each other. These collisions cause phase mutations in the energy levels of the molecules, thereby affecting the molecules' absorption and emission characteristics of light, and further causing the broadening of the spectral lines. By analyzing the physical model of the molecular collision process and considering factors such as the collision frequency and the collision cross section, the influence of the phase mutation caused by the collision on the spectral lines can be calculated, thereby obtaining the magnitude of the pressure broadening. Furthermore, the pressure broadening is closely related to the atmospheric pressure. The higher the pressure, the more frequent the molecular collisions, and the greater the pressure broadening. For the specific implementation process of calculating the pressure broadening, please refer to the description in the relevant technology, which will not be repeated here.

[0059] (3) Calculate the equivalent thickness of atmospheric molecular absorption based on the Voigt convolution of Doppler broadening and spectral line broadening.

[0060] Specifically, the atmospheric molecular line broadening can be calculated by the following formula:

[0061] f(v,v ij ,p,T)=f G (v,v ij ,p,T)×f D (v,v ij ,p,T);

[0062] Among them, f G (v,v ij ,p,T) is the line broadening;

[0063] f D (v,v ij ,p,T) is Doppler broadening;

[0064] Furthermore, the equivalent thickness of atmospheric molecule absorption can be calculated by the following formula:

[0065]

[0066] Where l is the height of the top of the atmosphere;

[0067] α(v) is the atmospheric molecular absorption density, which describes the light absorption capacity of molecules per unit length in the atmosphere;

[0068] n(z) is the atmospheric molecular density;

[0069] z is the height of the atmospheric molecules.

[0070] (4) Simulating the process of atmospheric molecules absorbing light sources based on the atmospheric molecule absorption equivalent thickness.

[0071] Specifically, after obtaining the atmospheric molecular absorption equivalent thickness, it can be substituted into the physical model of light transmission to simulate the radiation energy loss caused by atmospheric molecular absorption when the first laser pulse is transmitted in the atmosphere. Specifically, according to the Beer-Lambert law, combined with the atmospheric molecular absorption equivalent thickness, the attenuation of light intensity after light is transmitted through a certain distance of atmosphere can be calculated, thereby simulating the process of atmospheric molecules absorbing light sources.

[0072] Specifically, the intensity of light after it has traveled a certain distance through the atmosphere can be calculated using the following formula:

[0073] I(v)=I 0 exp(-τ)

[0074] Among them, I 0 is the intensity of the first laser pulse;

[0075] τ is the equivalent thickness of atmospheric molecular absorption.

[0076] By calculating the change in light intensity before and after atmospheric transmission, the radiation energy loss of the first laser pulse during transmission can be calculated.

[0077] Further, simulating the radiation energy loss of the first laser pulse during transmission based on particle scattering includes:

[0078] (1) Calculate Mie scattering characteristics based on cloud scattering characteristics.

[0079] Specifically, clouds are complex media composed of numerous tiny particles (such as water droplets, ice crystals, etc.). The scattering characteristics of clouds depend on factors such as the size, shape, distribution of the particles and the thickness of the clouds. The study of the scattering characteristics of clouds requires considering the effects of these factors on light scattering.

[0080] Furthermore, Mie scattering theory is used to describe the scattering phenomenon when light interacts with particles whose size is comparable to or larger than the wavelength of light. When calculating Mie scattering characteristics, it is necessary to use the relevant formulas and models in Mie scattering theory. These formulas usually involve parameters such as the complex refractive index of the particles (related to the material composition of the particles) and the particle size parameter (defined as the ratio of the particle circumference to the wavelength of light). According to the actual situation of the particles in the cloud layer (such as the particle size distribution and complex refractive index obtained by measurement or estimation), the characteristic quantities of Mie scattering such as the scattering cross section and scattering phase function are calculated. The scattering cross section describes the ratio of the total power of the light scattered by the particles per unit time to the incident light intensity, which reflects the scattering ability of the particles to light; the scattering phase function describes the distribution of scattered light in different directions, that is, the law of change of scattered light intensity with scattering angle.

[0081] (2) Calculate the aerosol equivalent thickness at each location based on the Mie scattering and molecular density.

[0082] Specifically, in the atmosphere, in addition to the scattering of cloud particles, there are also processes such as scattering and absorption of atmospheric molecules. The distribution of molecular density affects the transmission characteristics of light in the atmosphere, so the influence of molecular density needs to be considered when calculating the equivalent thickness of aerosols. The molecular density of the atmosphere usually varies with altitude, and the molecular density values ​​at different altitudes can be obtained through atmospheric models or actual measurements.

[0083] Furthermore, the aerosol equivalent thickness at each location can be calculated by the following formula:

[0084]

[0085] Among them, σ m is the scattering cross section of atmospheric molecules;

[0086] β s (z) is the aerosol scattering coefficient;

[0087] n m (z) is the molecular density of the atmosphere;

[0088] l is the height of the top of the atmosphere;

[0089] z is the height of the atmospheric molecules.

[0090] (3) Simulating the cloud scattering light source process based on the aerosol equivalent thickness.

[0091] Specifically, a light transmission model that takes into account the influence of aerosol equivalent thickness can be established. The light transmission model is usually based on the radiation transmission equation and describes the change in light intensity caused by scattering and absorption when light is transmitted in the atmosphere. In the light transmission model, the aerosol equivalent thickness is used as an important parameter to calculate the attenuation and scattering of light during transmission. The calculated aerosol equivalent thickness is substituted into the light transmission model, and the scattered light intensity distribution at different positions and directions is calculated by combining the initial characteristics of the first laser pulse (such as light intensity, wavelength, pulse shape, etc.) and the scattering phase function of Mie scattering (where is the position vector from the light source and is the scattering angle). By calculating the scattered light intensity distribution, it is possible to understand the energy redistribution caused by scattering when the first laser pulse is transmitted in the cloud layer, that is, which directions have stronger scattered light intensity and which directions have weaker scattered light intensity, thereby simulating the scattering effect of the cloud layer on the laser pulse. Furthermore, according to the calculated scattered light intensity distribution, it is compared with the initial light intensity of the first laser pulse to analyze the radiation energy loss caused by cloud scattering during the transmission of the first laser pulse. The attenuation of light intensity at different locations can be calculated.

[0092] Furthermore, the simulation of the radiation energy loss of the first laser pulse during the transmission process based on the surface reflection includes:

[0093] (1) Determine the surface reflectivity based on the surface type and status information.

[0094] Specifically, since different types of surfaces have different reflective properties, the surface types may include the ground, ocean, and forest, etc., and the reflective properties of the same type of surface in different states are also different, for example, the roughness (terrain undulation), humidity (dryness or wetness) of the land surface, the vegetation growth stage (such as seedling stage, mature stage), vegetation density, etc. of the vegetation coverage area. For the ocean surface, the salinity of the seawater, the surface wind and wave conditions, etc. will also affect its reflective properties. These surface type and state information are obtained through various means such as field measurements, satellite remote sensing data, geographic information system (GIS) data, and empirical models to determine the initial reflectivity parameters under different surface conditions. For example, the spectral reflectance data of the surface is obtained using a multi-spectral sensor carried by a satellite, and the reflectance values ​​under different surface types and states are obtained by calibration in combination with the ground measured data.

[0095] Furthermore, a surface reflectivity model is established based on a large amount of surface type and state information. The surface type and state information are input into the surface reflectivity model, and the surface reflectivity model will directly output the corresponding reflectivity value.

[0096] (2) Calculating the reflected light energy based on the surface reflectivity.

[0097] Specifically, the characteristics of the first laser pulse, including laser wavelength, pulse energy, incident angle, etc., and the determined surface reflectivity are known, and the reflected light energy is calculated according to the law of reflection of light.

[0098] (3) Simulating the process of ground surface reflection of light source based on the reflected light energy.

[0099] Specifically, the radiation energy loss is calculated by comparing the incident light energy of the first laser pulse with the calculated reflected light energy. By calculating the radiation energy loss, the energy loss of the laser pulse during the reflection process on the ground surface can be understood.

[0100] Furthermore, the molecular absorption loss, scattering loss and reflection loss obtained in the above three steps are added together to obtain the total radiation energy loss of the first laser pulse during the transmission process, and the laser pulse obtained by subtracting the total radiation energy loss from the first laser pulse is recorded as the second laser pulse.

[0101] S103. Establish a detector instrument model, calculate the performance degradation degree of the detector based on the detector instrument model parameters, calculate the detection error of the detector under the state parameters of each working scene when the detector is in a state of performance degradation, and use the sum of the performance degradation degree and the detection error as the system error of the detector.

[0102] Specifically, the detector instrument model includes various characteristics and parameters of the detector, such as the quantum efficiency, response time, dark current, etc. of the detector. Based on the detector instrument model parameters, the performance degradation of the detector that may occur under long-term use or under the influence of environmental factors is analyzed, and the degree of performance degradation of the detector is calculated. Performance degradation may manifest as reduced sensitivity of the detector to light, slower response speed, increased noise, etc. The detection error of the detector under different working scene state parameters (such as temperature, humidity, air pressure, etc.) is further calculated when the detector is in a state of performance degradation. Because changes in working scene state parameters will affect the performance of the detector, resulting in deviations in the detection results, these detection errors are calculated by establishing corresponding mathematical relationships, and finally the degree of performance degradation of the detector and the detection error are added to obtain the system error of the detector. The system error reflects the overall measurement error of the detector due to its own performance degradation and the influence of the working environment.

[0103] Furthermore, after determining the system error, it also includes:

[0104] (1) Determining a disturbance factor based on the performance degradation degree and the detection error.

[0105] Specifically, the disturbance factor is determined based on the performance degradation degree and detection error of the detector. The performance degradation degree reflects the performance degradation of the detector due to long-term use, environmental factors, etc., such as reduced sensitivity and longer response time of the detector. The detection error is the deviation between the measurement result and the true value of the detector under different working scene state parameters. Through the comprehensive analysis of these two factors, a quantity that can characterize the degree to which the current state of the detector deviates from the ideal state can be obtained, which is the disturbance factor.

[0106] Furthermore, the disturbance factor may be calculated by a weighted sum, product or other functional relationship between the performance degradation degree and the detection error, which is not limited in this embodiment.

[0107] (2) Optimizing the system error according to the disturbance factor to obtain an optimized system error.

[0108] Specifically, through a predetermined optimization strategy, the system error is optimized using the disturbance factor according to the optimization strategy, and the various parts of the system are gradually adjusted according to the size and direction of the disturbance factor to achieve the purpose of reducing the system error, and finally the optimized system error is obtained.

[0109] (3) Compare the optimized system error with the error threshold. If the optimized system error is less than the error threshold, use the optimized system error as the system error. If the optimized system error is greater than or equal to the error threshold, redetermine the disturbance factor and optimize the system error until the optimized system error is less than the error threshold.

[0110] Specifically, the optimized system error is compared with the error threshold. The error threshold is a pre-set acceptable upper limit of the error, which depends on the accuracy requirements and application scenarios of the laser radar system. If the optimized system error is less than the error threshold, it means that the system error of the detector after optimization is already within an acceptable range. At this time, the optimized system error can be used as the final system error for subsequent data processing and analysis. If the optimized system error is greater than or equal to the error threshold, it is necessary to redetermine the disturbance factor, which means that the previous optimization measures may not be effective enough, or the state of the detector has changed further. After redetermining the disturbance factor, the system error is optimized again, and this process is repeated until the optimized system error is less than the error threshold. Through this cyclic optimization method, the state and parameters of the detector are continuously adjusted to ensure that the system error is always kept within an acceptable range, thereby improving the measurement accuracy and reliability of the laser radar system. It should be noted that the error threshold is set according to actual needs and is not limited in this embodiment.

[0111] S104, calculating a first noise detected by the detector, where the first noise is affected by a radiation source; calculating a detection noise and a signal processing noise of the detector, and calculating a total noise of the detector based on the sum of the first noise, the detection noise and the signal processing noise.

[0112] Specifically, the first noise is mainly caused by the influence of radiation sources (such as solar radiation, other light sources, etc.), the detection noise is the inherent noise generated by the random motion of electrons and the characteristics of the detector material during the working process of the detector; the signal processing noise is the noise introduced during the amplification, filtering, digitization and other processing of the detector output signal. The first noise, detection noise and signal processing noise are added together to obtain the total noise of the detector. The total noise reflects the comprehensive situation of various noise interferences suffered by the detector in actual work.

[0113] The calculating the total noise of the detector comprises:

[0114] (1) Calculating the first noise according to solar radiation intensity and surface reflectivity.

[0115] Specifically, the first noise can be calculated by the following formula:

[0116]

[0117] Among them, I solar is the solar background radiation intensity;

[0118] ρ is the surface reflectivity of the spaceborne lidar in zenith observation mode;

[0119] A is the effective detection area of ​​the telescope;

[0120] is the field of view of IPDA lidar.

[0121] (2) Calculating the detection noise based on the dark current of the detector, the single electron charge, and the bandwidth of the detector, wherein the dark current is generated by the photoelectric conversion device of the detector, and the single electron charge is generated in the working circuit of the detector.

[0122] Specifically, dark current noise comes from the LiDAR detector, which is defined as the output current generated by the detector without any photon signal input. The detector is generally composed of a photosensitive area, a cascade diode structure, a forward bias power supply, an electron proliferation area, and a photocurrent output. By applying a forward bias voltage, the detector can achieve internal linear amplification and output an amplified current signal. Due to the existence of the avalanche multiplication effect, the detector has higher gain and lower noise characteristics, which is very suitable for the detection and amplification of low echo light intensity signals of LiDAR.

[0123] Usually the dark current size of the detector is marked in the factory report of the detector, but considering the differences in actual working environment and its significant impact on measurement accuracy, the dark current noise needs to be calibrated and verified after it is actually integrated into the lidar system.

[0124] Specifically, the detection noise can be calculated by the following formula:

[0125]

[0126] Where, B is the detector bandwidth;

[0127] i dark is the dark current value;

[0128] e is the charge of a single electron;

[0129] M is the detector gain factor;

[0130] F is the detector excess noise coefficient;

[0131] R is the sensitivity of the detector;

[0132] I 0is the intensity of the first laser pulse;

[0133] I 1 The first noise.

[0134] (3) Calculating the signal processing noise based on the operating temperature and resistance value of the detector, where the resistance value comes from the resistance in the working circuit of the detector.

[0135] Specifically, the signal processing noise can be calculated by the following formula:

[0136]

[0137] Among them, k B is the Boltzmann constant;

[0138] R F is the resistance value;

[0139] T is the detector operating temperature;

[0140] B is the detector bandwidth.

[0141] Furthermore, after determining the total noise of the detector, it also includes:

[0142] (1) Calculating the influence of the detection noise on the second laser pulse according to the signal-to-noise ratio.

[0143] Specifically, the influence of the detection noise on the second laser pulse can be calculated by the following formula:

[0144]

[0145] Where, B is the detector bandwidth;

[0146] i dark is the dark current value;

[0147] e is the charge of a single electron;

[0148] M is the detector gain factor;

[0149] F is the detector excess noise coefficient;

[0150] R is the sensitivity of the detector;

[0151] I 1 is the first noise;

[0152] k B is the Boltzmann constant;

[0153] R F is the resistance value;

[0154] T is the detector operating temperature.

[0155] (2) Determine the effect of the smoothing times on the second laser pulse.

[0156] Specifically, the influence of the number of smoothing times on the second laser pulse can be calculated by the following formula:

[0157]

[0158] in, is the signal-to-noise ratio of the detector at different detection wavelengths;

[0159] is the signal-to-noise ratio at different operating wavelengths of the laser;

[0160] N shots is the number of smoothing times.

[0161] S105 , establishing a response curve of the detector, receiving the second laser pulse based on the response curve, and obtaining a predicted response.

[0162] Specifically, the response curve of the detector describes the relationship between the detector output signal and the input light intensity. The response curve is usually not completely linear and may be affected by factors such as the nonlinear characteristics and saturation effect of the detector. The specific form and parameters of the response curve can be determined by calibration experiments on the detector or based on the physical model of the detector.

[0163] Furthermore, based on the established response curve, the second laser pulse after the transmission process is received, and the predicted response of the detector is calculated according to the relationship of the response curve. This predicted response is the signal that the detector should output under ideal conditions without considering the detector error and noise.

[0164] S106. Correct the predicted response according to the detection error and the total noise to obtain an output result of the detector.

[0165] Specifically, the influence of detection error and total noise is removed from the predicted response, and finally the output result of the detector is obtained.

[0166] Specifically, the specific implementation steps of correcting the predicted response according to the detection error and the total noise include:

[0167] (1) The laser light source emitted by the laser is input into the IPDA laser radar detection model to calculate the actual light intensity after molecular absorption and scattering.

[0168] Specifically, combined with the above description, we first need to determine the relevant parameters of atmospheric molecules, such as the molecular number density and absorption cross section at different altitudes. These parameters can be obtained through atmospheric models or actual measurements. Then, according to the initial light intensity of the laser, the transmission path length, and the physical laws of molecular absorption and scattering, the relevant formulas are used for calculation. For the specific implementation process of calculating light intensity, please refer to the description in the relevant technology, which will not be repeated here.

[0169] (2) Calculating the ideal response of the detector under the actual light source based on the response characteristic curve of the detector.

[0170] Specifically, the calculated actual light intensity is used as input and substituted into the response characteristic curve equation of the detector for calculation. The response characteristic curve of the detector can usually be obtained through experimental measurement or a mathematical model established based on the physical principle of the detector.

[0171] (3) Calculating a detection response based on the sum of the ideal response and the detection error.

[0172] Specifically, due to the detection error of the detector, the actual detection response will deviate from the ideal response. The detection error includes the system error and calibration error of the detector, which will cause the output signal of the detector to deviate from the signal under ideal conditions. In order to obtain a detection response that is closer to the actual situation, it is necessary to add the ideal response and the detection error.

[0173] (4) Calculate the actual response based on the difference between the detection response and the detection noise to complete the simulation of the IPDA lidar detection.

[0174] Specifically, the actual response is equal to the difference between the detection response and the detection noise. This actual response more truly reflects the detection of laser signals by the detector in an actual environment, which is of great significance for evaluating the performance and accuracy of the lidar system and subsequent data processing and analysis.

[0175] Furthermore, after atmospheric attenuation, it is received by the detector and responds to generate a current signal. Without considering the broadening effect of the surface roughness on the laser pulse, the laser pulse width before reaching the detector is 15ns, and the time interval between the detector receiving the monitoring signal and the echo signal is set to 4.7ms. The preamplifier is a reverse amplifier with a bandwidth of 8.9MHz. Therefore, after passing through the optical receiver, the signal is reversely broadened to about 40ns. When the laser pulse is focused on the photosensitive surface, photoelectric conversion occurs to generate a current signal that can be directly detected.

[0176] The method provided in this embodiment obtains a first laser pulse by simulating the emission of laser pulses based on the state matrix, and then obtains a second laser pulse by simulating the radiation energy loss during its transmission process by considering molecular absorption, particle scattering and surface reflection, which can highly restore the propagation of lasers in the actual atmospheric environment. By establishing a detector instrument model, calculating the degree of performance degradation and detection error, and taking the sum of the two as the system error, the performance changes of the detector under long-term use and different working scenarios can be comprehensively considered. This helps to promptly discover possible problems with the detector, take measures in advance for maintenance or calibration, and ensure the reliability and stability of the detector. Secondly, the first noise (affected by the radiation source), detection noise and signal processing noise are calculated respectively, and the total noise is summed to obtain the source and composition of the detector noise. This helps to take targeted measures to reduce the impact of noise, such as by optimizing the detector design, shielding the radiation source or improving the signal processing algorithm, improving the signal-to-noise ratio of the detector, receiving the second laser pulse based on the response curve of the detector to obtain a predicted response, and then correcting the predicted response according to the detection error and total noise, which can effectively reduce the impact of errors and noise on the detector output results. This makes the final output result closer to the true value and improves the measurement accuracy of the entire lidar system. In practical applications, such as high-precision monitoring of greenhouse gas concentrations in the atmosphere, accurate detector output results can provide reliable data for climate change research, environmental assessment, etc. In addition, accurate measurement results can also be used to verify and improve atmospheric models, further promoting scientific research and technological development in related fields.

[0177] Corresponding to the aforementioned embodiment of an IPDA laser radar detection model construction method, the present application also provides an embodiment of an IPDA laser radar detection model construction device.

[0178] Figure 2 This is a schematic diagram of the structure of the first embodiment of the IPDA laser radar detection model construction device provided in this application. Figure 2 , the device provided in this embodiment includes a transmitting module 210, a transmission module 220 and a receiving module 230;

[0179] The transmitting module 210 is used to simulate the emission of laser pulses based on the state matrix to obtain a first laser pulse;

[0180] The transmission module 220 is used to simulate the radiation energy loss of the first laser pulse during the transmission process based on molecular absorption, particle scattering and surface reflection to obtain a second laser pulse;

[0181] The transmission module 220 is further used to establish a detector instrument model, calculate the performance degradation degree of the detector based on the detector instrument model parameters, calculate the detection error of the detector under various working scene state parameters in the state of performance degradation, and use the sum of the performance degradation degree and the detection error as the system error of the detector;

[0182] The transmission module 220 is further used to calculate a first noise detected by the detector, where the first noise is affected by a radiation source; calculate a detection noise and a signal processing noise of the detector, and calculate a total noise of the detector based on the sum of the first noise, the detection noise and the signal processing noise;

[0183] The receiving module 230 is used to establish a response curve of the detector, receive the second laser pulse based on the response curve, and obtain a predicted response;

[0184] The receiving module 230 is further used to correct the predicted response according to the detection error and the total noise to obtain the output result of the detector.

[0185] The device of this embodiment can be used to perform Figure 1 The steps, specific implementation principles and implementation processes of the method embodiment shown are similar and will not be repeated here.

[0186] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0187] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0188] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for constructing an IPDA laser radar detection model, characterized in that: The method comprises: Simulate laser pulse emission based on the state matrix to obtain a first laser pulse; Simulating the radiation energy loss of the first laser pulse during transmission based on molecular absorption, particle scattering and surface reflection to obtain a second laser pulse; Establishing a detector instrument model, calculating the performance degradation degree of the detector based on the detector instrument model parameters, calculating the detection error of the detector under various working scene state parameters in a state of performance degradation, and taking the sum of the performance degradation degree and the detection error as the system error of the detector; Calculating a first noise detected by the detector, wherein the first noise is affected by a radiation source; calculating a detection noise and a signal processing noise of the detector, and calculating a total noise of the detector based on a sum of the first noise, the detection noise and the signal processing noise; Establishing a response curve of the detector, receiving the second laser pulse based on the response curve, and obtaining a predicted response; The predicted response is corrected according to the detection error and the total noise to obtain the output result of the detector.

2. The method according to claim 1, characterized in that The method of simulating laser pulse emission based on the state matrix comprises: Calculating a state matrix of the detector based on a detector system model; Calculate the detector transmission response curve and receiving response curve based on the state matrix; Calculate the time domain characteristics and frequency domain characteristics of the emitted laser according to the emission response curve; Calculate the frequency half-maximum full width value of the emitted laser based on the time domain characteristics and the frequency characteristics; The laser pulse emission process is simulated based on the frequency half-width value to obtain a first laser pulse as a transmission module of the laser radar detection model.

3. The method according to claim 1, characterized in that The method of simulating the radiation energy loss of the first laser pulse during transmission based on molecular absorption comprises: Calculate the average velocity of atmospheric molecule motion based on the Gaussian line shape, and obtain Doppler broadening based on the average velocity of atmospheric molecule motion; Calculate the pressure broadening based on the phase mutation produced by the collision; Calculate the equivalent absorption thickness of atmospheric molecules based on the Voigt convolution of Doppler broadening and spectral line broadening; The process of atmospheric molecules absorbing light source is simulated based on the atmospheric molecule absorption equivalent thickness.

4. The method according to claim 1, characterized in that: The method of simulating the radiation energy loss of the first laser pulse during transmission based on particle scattering includes: Calculate Mie scattering characteristics based on cloud scattering characteristics; Calculate the aerosol equivalent thickness at each location point based on the Mie scattering and molecular density; The cloud scattering light source process is simulated based on the aerosol equivalent thickness.

5. The method according to claim 1, characterized in that The method of simulating the radiation energy loss of the first laser pulse during transmission based on surface reflection comprises: Determine the surface reflectivity based on the surface type and state information; Calculating reflected light energy based on the surface reflectivity; The process of ground surface reflecting light source is simulated based on the reflected light energy.

6. The method according to claim 1, characterized in that The method comprises: determining a disturbance factor based on the performance degradation degree and the detection error; Optimizing the system error according to the disturbance factor to obtain an optimized system error; The optimized system error is compared with the error threshold. If the optimized system error is less than the error threshold, the optimized system error is used as the system error. If the optimized system error is greater than or equal to the error threshold, the disturbance factor is re-determined and the system error is optimized until the optimized system error is less than the error threshold.

7. The method according to claim 1, characterized in that The method comprises: Calculating the influence of the detection noise on the second laser pulse according to the signal-to-noise ratio; The effect of the smoothing number on the second laser pulse is determined.

8. The method according to claim 1, characterized in that The calculating the total noise of the detector comprises: Calculating the first noise according to solar radiation intensity and surface reflectivity; Calculating the detection noise according to the dark current, the single electron charge of the detector and the bandwidth of the detector, wherein the dark current is generated by the photoelectric conversion device of the detector and the single electron charge is generated in the working circuit of the detector; The signal processing noise is calculated based on the operating temperature of the detector and the resistance value, where the resistance value comes from the resistance in the working circuit of the detector.

9. The method according to claim 1, characterized in that: The method comprises: Inputting the laser light source emitted by the laser into the IPDA laser radar detection model to calculate the actual light intensity after molecular absorption and scattering; Calculating the ideal response of the detector under the actual light source based on the response characteristic curve of the detector; calculating a detection response based on a sum of the ideal response and the detection error; The actual response is calculated based on the difference between the detection response and the detection noise to complete the simulation of the IPDA laser radar detection.

10. An IPDA laser radar detection model construction device, characterized in that: The device is used to implement any one of the methods described in claims 1-9, and the device includes a transmitting module, a transmission module and a receiving module; The transmitting module is used to simulate the emission of laser pulses based on the state matrix to obtain a first laser pulse; The transmission module is used to simulate the radiation energy loss of the first laser pulse during the transmission process based on molecular absorption, particle scattering and surface reflection to obtain a second laser pulse; The transmission module is further used to establish a detector instrument model, calculate the performance degradation degree of the detector based on the detector instrument model parameters, calculate the detection error of the detector under various working scene state parameters when the detector is in a state of performance degradation, and use the sum of the performance degradation degree and the detection error as the system error of the detector; The transmission module is further used to calculate a first noise detected by the detector, the first noise being affected by a radiation source; calculate a detection noise and a signal processing noise of the detector, and calculate a total noise of the detector based on the sum of the first noise, the detection noise and the signal processing noise; The receiving module is used to establish a response curve of the detector, receive the second laser pulse based on the response curve, and obtain a predicted response; The receiving module is further used to correct the predicted response according to the detection error and the total noise to obtain the output result of the detector.

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