Performance simulation method and device, computer equipment and storage medium

Abstract

The invention provides a performance simulation method and device for an SPAD laser radar, computer equipment and a storage medium, and the method comprises the steps: obtaining the number of photons received by a single photon avalanche diode (SPAD) array, and determining the number of photons received by a single SPAD at a current moment; determining a target avalanche probability of the single SPAD according to the photon number received by the single SPAD and a pre-constructed target mapping relation between the photon number and the avalanche probability; according to the target avalanche probability and the total number of SPAD, calculating the avalanche number probability of avalanche of each SPAD with different numbers, and generating initial avalanche probability distribution of the SPAD array; performing iterative operation by using the initial avalanche probability distribution to obtain target avalanche probability distribution of the SPAD array; according to the target avalanche probability distribution, the range finding performance of the SPAD laser radar is predicted, and the range finding performance of the SPAD laser radar at least comprises the limit detection distance of the SPAD laser radar.

Description

Technical Field

[0001] This disclosure belongs to the field of simulation testing and lidar technology, specifically relating to a lidar performance simulation method, apparatus, computer equipment, and storage medium. Background Technology

[0002] Single-photon avalanche diode (SPAD) lidar actively emits laser light toward the target and uses a SPAD array to receive the reflected light from the target. It determines the presence of the target by the intensity of the reflected light and measures the target range by measuring the laser transmission delay.

[0003] Typically, to reduce development risks, it is necessary to verify the design results of SPAD lidar, for example, by using system link simulation to calculate the system's limiting ranging performance. However, traditional SPAD lidar link simulation methods calculate the system's limiting ranging performance through Monte Carlo simulation, which requires a large number of simulations of the system's limiting ranging performance, resulting in a large computational load and low efficiency. Summary of the Invention

[0004] This disclosure aims to at least address one of the technical problems existing in the prior art, and to provide a performance simulation method, apparatus, computer device, and storage medium.

[0005] Firstly, the technical solution adopted to solve the technical problem disclosed herein is a performance simulation method for SPAD lidar, including:

[0006] Obtain the number of photons received by the SPAD array and determine the number of photons received by a single SPAD at the current moment;

[0007] The target avalanche probability of a single SPAD is determined based on the target mapping relationship between the number of photons received by a single SPAD and the pre-constructed number of photons and the avalanche probability.

[0008] Based on the target avalanche probability and the total number of SPADs, the probability of an avalanche occurring for each different number of SPADs is calculated, and the initial avalanche probability distribution of the SPAD array is generated.

[0009] The initial avalanche probability distribution is used for iterative calculations to obtain the target avalanche probability distribution of the SPAD array;

[0010] Based on the target avalanche probability distribution, the ranging performance of the SPAD lidar is predicted, wherein the ranging performance of the SPAD lidar includes at least the limit detection range of the SPAD lidar.

[0011] In some embodiments, the number of photons received by a single SPAD includes the number of photons corresponding to different types of light, denoted as the first photon count belonging to ambient light and the second photon count belonging to reflected light, respectively; the target avalanche probability includes the sub-target avalanche probability corresponding to different types of light, denoted as the first avalanche probability corresponding to ambient light and the second avalanche probability corresponding to reflected light, respectively.

[0012] The steps for determining the sub-target avalanche probability of a single SPAD include:

[0013] Construct a first state matrix for the current time; the first state matrix includes elements corresponding to the current time, and the elements include a pre-set first prediction probability and a first attenuation coefficient for the number of photons;

[0014] The system iterates through all elements in the first state matrix. Based on the first predicted probability, the first photon count attenuation coefficient, the number of photons of the same type of light at the current moment, and the target mapping relationship, it determines the first conditional probability that a single SPAD will avalanche at the next moment, and the second conditional probability that a single SPAD will not avalanche at the next moment. Based on the comparison result of whether the first conditional probability is equal to 0, it generates the second photon count attenuation coefficient corresponding to the first conditional probability at the next moment. Based on the comparison result of whether the second conditional probability is equal to 0, it generates the second photon count attenuation coefficient corresponding to the second conditional probability at the next moment.

[0015] The second state matrix is ​​obtained by traversing all elements, including the first conditional probability, the second conditional probability, the second photon number attenuation coefficient corresponding to the first conditional probability, and the second photon number attenuation coefficient corresponding to the second conditional probability.

[0016] Based on the second state matrix, determine the second predicted probability of a single SPAD avalanche at the next moment; and take the second state matrix at the next moment as the first state matrix at the current moment, return to the step of traversing all elements in the first state matrix in turn until the second predicted probability of a single SPAD avalanche at the next moment is determined and execute it in a loop until the second predicted probability converges.

[0017] The maximum value of the second predicted probability is taken as the sub-target avalanche probability of a single SPAD.

[0018] In some embodiments, determining the first conditional probability of a single SPAD avalanche occurring at the next moment based on the first predicted probability, the first attenuation coefficient of the photon number, the photon number corresponding to the same type of light at the current moment, and the target mapping relationship includes:

[0019] For any element in the first state matrix, the attenuated photon number at the next moment is determined based on the first attenuation coefficient of the photon number and the number of photons of the same type of light at the current moment.

[0020] Based on the attenuation photon number and the target mapping relationship, determine the target attenuation probability of a single SPAD avalanche at the next moment;

[0021] Based on the target decay probability and the first predicted probability, the first conditional probability of a single SPAD avalanche at the next moment is determined.

[0022] In some embodiments, the formula for calculating the first conditional probability is: P act =P prev ·f N2P (C prev ·N); where P act Let P represent the first conditional probability. prev f represents the first predicted probability. N2P (C prev ·N) represents the target attenuation probability; C prev The photon count represents the first attenuation coefficient; N represents the number of photons of the same type of light at the current moment; C prev ·N represents the number of decayed photons after decay at the next time step.

[0023] In some embodiments, for the first state matrix constructed at the current moment, which includes data pairs with elements [1,1], the first prediction probability is 1, and the first attenuation coefficient of the photon number is 1.

[0024] In some embodiments, generating a second attenuation coefficient for the number of photons corresponding to the first conditional probability at the next moment, based on the comparison result of whether the first conditional probability is equal to 0, includes:

[0025] When the first conditional probability is not equal to 0, the second attenuation coefficient of the photon number corresponding to the first conditional probability at the next moment is 0; when the first conditional probability is equal to 0, the second attenuation coefficient of the photon number corresponding to the first conditional probability at the next moment is (t+1) / T; where t represents the current moment and T represents the sum of the rise time and dead time of SPAD.

[0026] The step of generating the second attenuation coefficient of the photon number corresponding to the second conditional probability at the next moment based on the comparison result of whether the second conditional probability is equal to 0 includes:

[0027] When the second conditional probability is not equal to 0, the second attenuation coefficient of the photon number corresponding to the second conditional probability at the next moment is (t+1) / T; when the first conditional probability is equal to 0, the second attenuation coefficient of the photon number corresponding to the first conditional probability at the next moment is 0.

[0028] In some embodiments, obtaining the second state matrix based on the first conditional probability, the second conditional probability, the second photon number attenuation coefficient corresponding to the first conditional probability, and the second photon number attenuation coefficient corresponding to the second conditional probability obtained after traversing all elements includes:

[0029] The second state matrix is ​​obtained by taking the first conditional probability and the second photon number attenuation coefficient corresponding to the first conditional probability as new elements, and taking the second conditional probability and the second photon number attenuation coefficient corresponding to the second conditional probability as new elements.

[0030] In some embodiments, determining the second predicted probability of a single SPAD avalanche occurring at the next moment based on the second state matrix includes:

[0031] The second attenuation coefficient of the photon number that is not equal to 1 is selected from the elements containing the second conditional probability in the second state matrix;

[0032] The second conditional probability corresponding to the second attenuation coefficient of the photon number that is not equal to 1, and the sum of all the first conditional probabilities in the second state matrix, are used as the second predicted probability of a single SPAD avalanche occurring at the next moment.

[0033] In some embodiments, the step of determining the first photon number includes:

[0034] The number of photons of ambient light received by the SPAD array is determined based on the intensity of the ambient light, the bandwidth of the filter of the SPAD lidar, the vertical and horizontal field of view of the SPAD lidar, the reflectivity of the target observed by the SPAD lidar, the receiving aperture of the SPAD lidar, the receiving efficiency of the SPAD lidar, the wavelength of the laser emitted by the SPAD lidar, Planck's constant, and the speed of light.

[0035] The first photon count is determined based on the number of ambient light photons received by the SPAD array and the total number of SPADs.

[0036] In some embodiments, the step of determining the second photon number includes:

[0037] The number of photons of the reflected light received by the SPAD array is determined based on the SPAD lidar's emission power, vertical and horizontal divergence angles, vertical and horizontal field of view, reflectivity of the target observed by the SPAD lidar, receiving aperture, receiving and emission efficiency, wavelength of the emitted laser light, Planck's constant, speed of light, and the preset test distance.

[0038] The maximum number of photons is determined based on the number of photons of the reflected light received by the SPAD array and the total number of SPADs.

[0039] The second photon number is determined based on the maximum photon number and the fitting coefficient corresponding to the maximum photon number at the current moment, wherein the fitting coefficient is related to the waveform of the laser emitted by the SPAD lidar.

[0040] In some embodiments, the initial avalanche probability distribution includes sub-initial avalanche probability distributions corresponding to different types of light, denoted as the first avalanche probability distribution corresponding to ambient light and the second avalanche probability distribution corresponding to reflected light, respectively.

[0041] The formula for calculating the initial avalanche probability distribution is as follows: Among them, P HIST (m,M) represents the sub-initial avalanche probability distribution, including the probability of avalanche occurrence for each different number of SPADs; m represents the number of SPADs that will experience an avalanche; M represents the total number of SPADs. This represents the formula for combination calculation; This represents the sub-target avalanche probability of a single SPAD.

[0042] In some embodiments, the target avalanche probability distribution includes sub-target avalanche probability distributions corresponding to different types of light, respectively denoted as the third avalanche probability distribution corresponding to ambient light and the fourth avalanche probability distribution corresponding to reflected light;

[0043] The formula for calculating the avalanche probability distribution of the sub-target is:

[0044]

[0045] in, The sub-target avalanche probability distribution is represented by v; v represents the total number of SPADs that caused avalanches after S measurements; P HIST (vi,M) represents the probability of the number of avalanches occurring in (vi) SPADs under the S-th initial avalanche probability distribution; P represents the peak probability of an avalanche occurring in the i-th SPAD under the sub-target avalanche probability distribution obtained from the previous (S-1) iterations; HIST (v,M) represents the initial avalanche probability distribution determined in the first iteration.

[0046] In some embodiments, the step of iteratively calculating the initial avalanche probability distribution to obtain the target avalanche probability distribution of the SPAD array includes:

[0047] The initial avalanche probability distribution is iterated S times to obtain the target avalanche probability distribution of the SPAD array; S is a positive integer greater than or equal to 2.

[0048] In some embodiments, after calculating the avalanche probability of each different number of SPADs occurring based on the target avalanche probability and the total number of SPADs, and generating the initial avalanche probability distribution of the SPAD array, the method further includes:

[0049] Return to the step of obtaining the number of photons received by the SPAD array until the initial avalanche probability distribution of the SPAD array is generated, and repeat the process S times; S is a positive integer greater than or equal to 2.

[0050] The step of iteratively calculating the target avalanche probability distribution of the SPAD array using the initial avalanche probability distribution includes:

[0051] The target avalanche probability distribution of the SPAD array is obtained by iterative calculation based on the initial avalanche probability distribution described in S times.

[0052] In some embodiments, predicting the ranging performance of the SPAD lidar based on the target avalanche probability distribution includes:

[0053] Based on the third avalanche probability distribution and the target detection threshold of the lidar, the false alarm probability to be verified of the lidar is obtained;

[0054] Based on the fourth avalanche probability distribution and the target detection threshold of the lidar, the detection probability to be verified of the lidar is obtained;

[0055] Obtain a first comparison result between the detection probability to be verified and a first preset detection probability threshold, and a second comparison result between the false alarm probability to be verified and a second preset false alarm probability threshold. If at least one of the first comparison result and the second comparison result satisfies a critical condition, the preset test distance is taken as the limit detection distance of the lidar.

[0056] In some embodiments, the formula for calculating the detection probability to be verified is: Among them, PDET Indicates the probability of detection to be verified. V represents the probability distribution of the fourth avalanche. DET This represents the target detection threshold; In this case, v is not less than V. DET And not greater than the value of S·M;

[0057] The formula for calculating the false alarm probability to be verified is: Among them, P FA This represents the false alarm probability to be verified. This represents the probability distribution of the third avalanche. In this case, v is not less than V. DET And not greater than the value of S·M.

[0058] In some embodiments, the step of determining the target mapping relationship between the pre-constructed photon number and the avalanche probability includes:

[0059] Based on the different number of photons received by a single SPAD, the avalanche probability corresponding to each different number of photons is calculated according to the Poisson distribution function, and the target mapping relationship is constructed.

[0060] Secondly, this disclosure also provides a performance simulation device for SPAD lidar, including a photon number receiving module, a first processing module, a second processing module, a third processing module, and a fourth processing module.

[0061] The photon count determination module is configured to acquire the number of photons received by the SPAD array and determine the number of photons received by a single SPAD at the current moment.

[0062] The first processing module is configured to determine the target avalanche probability of a single SPAD based on the number of photons received by a single SPAD and the target mapping relationship between the number of photons and the avalanche probability pre-constructed.

[0063] The second processing module is configured to calculate the avalanche probability of avalanche occurring for each different number of SPADs based on the target avalanche probability and the total number of SPADs, and generate the initial avalanche probability distribution of the SPAD array.

[0064] The third processing module is configured to perform iterative calculations using the initial avalanche probability distribution to obtain the target avalanche probability distribution of the SPAD array;

[0065] The fourth processing module is configured to predict the ranging performance of the SPAD lidar based on the target avalanche probability distribution, wherein the ranging performance of the SPAD lidar includes at least the limit detection range of the SPAD lidar.

[0066] Thirdly, embodiments of this disclosure also provide a computer device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the performance simulation method as described in any one of the first aspects are performed.

[0067] Fourthly, embodiments of this disclosure also provide a computer non-transient readable storage medium storing a computer program that, when executed by a processor, performs the steps of the performance simulation method as described in any one of the first aspects. Attached Figure Description

[0068] Figure 1 A flowchart of a performance simulation method provided in this embodiment of the disclosure;

[0069] Figure 2 A flowchart for determining the sub-target avalanche probability of a single SPAD, provided for embodiments of this disclosure;

[0070] Figure 3 A schematic diagram of the process for generating the second state matrix provided in an embodiment of this disclosure;

[0071] Figure 4 A flowchart illustrating the process of predicting the limiting ranging performance of a SPAD lidar according to an embodiment of this disclosure;

[0072] Figure 5 A schematic diagram of the performance simulation apparatus provided in the embodiments of this disclosure;

[0073] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0075] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “including,” “comprising,” or “containing,” and similar terms mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. The terms “connected,” “linked,” or similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms “upper,” “lower,” “left,” and “right,” etc., are used only to indicate relative positional relationships, and these relative positional relationships may change accordingly when the absolute position of the described objects changes.

[0076] In this disclosure, "multiple or several" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0077] In related technologies, SPAD lidar receivers utilize SPAD arrays, and whether an avalanche (or activation) occurs after a SPAD receives photons is a probabilistic event. For example, when the number of photons received by the SPAD array is constant, the avalanche behavior of the SPAD will differ in different measurements. Therefore, SPAD lidar employs a multiple measurement method, that is, emitting lasers at the target multiple times in a short period of time and accumulating the results of multiple measurements to improve detection and ranging accuracy.

[0078] Ranging performance, especially the limiting ranging performance, is a crucial indicator for lidar systems. Traditional SPAD lidar link simulation calculates the system's limiting ranging performance using Monte Carlo simulation. The specific process includes: setting the SPAD lidar parameters → calculating the number of photons received by the SPAD array from ambient light and the number of photons received from reflected light → generating the received waveforms for a single measurement and a single SPAD → generating the measurement results for a single measurement and a single SPAD → generating the measurement results for a single measurement and the SPAD array → generating the measurement results for multiple measurements and the SPAD array → calculating the limiting ranging performance of the SPAD lidar. This process constitutes one Monte Carlo simulation. However, the entire SPAD lidar link simulation process requires numerous simulations of the system's limiting ranging performance, and the average of the simulation results is calculated to approximate the true value of the system's limiting ranging performance.

[0079] Traditional SPAD lidar link simulation methods are related to system parameter type, number of SPADs, received waveform length, number of measurements, and number of Monte Carlo simulations. To improve the accuracy of Monte Carlo simulation, a higher number of simulations is necessary. Therefore, traditional methods involve a large computational load and low efficiency.

[0080] In view of this, the present disclosure provides a performance simulation method for SPAD lidar. This performance simulation method is mainly applied to the ranging performance simulation of lidar based on single-photon avalanche diode (SPAD) to verify the ranging performance of SPAD lidar.

[0081] The performance simulation method provided in this disclosure can be executed by a computer device with certain computing power, hereinafter referred to as a ranging performance simulation device, such as a server or terminal device. The performance simulation method provided in this disclosure will be described below using a ranging performance simulation device as the executing entity.

[0082] Figure 1 A flowchart of a performance simulation method provided in this disclosure embodiment is shown below. Figure 1 As shown, the specific steps include S11 to S15, wherein:

[0083] S11. Obtain the number of photons received by the SPAD array and determine the number of photons received by a single SPAD at the current moment.

[0084] The ranging performance simulation device defines a virtual lidar by setting parameters, simulating the emission of laser light towards a virtual target at a preset test distance, in order to obtain the number of photons received by the SPAD array in the virtual lidar. The term "SPAD array" as used below refers to the SPAD array in the virtual lidar.

[0085] The photons received by the SPAD array include ambient light photons and reflected light photons. Therefore, the number of photons received by the SPAD array also includes the first photon count of ambient light and the second photon count of reflected light. In practical applications, ambient light can also be called background light, representing the light in the environment where the lidar is located, including but not limited to sunlight. Reflected light represents the light reflected back from the target after the lidar emits laser light. In the simulation, by combining the actual equipment parameters of the SPAD lidar and setting the equipment parameters of the ranging performance simulation device, the ambient light and reflected light are simulated to determine the first photon count of ambient light and the second photon count of reflected light.

[0086] It should be noted that, in practice, based on the energy distribution of the light spot received by the SPAD array, it can be assumed that the energy distribution of the light spot within the SPAD array is uniform. Therefore, it is only necessary to count the total number of photons received by the SPAD array and, based on the number of SPADs in the SPAD array, calculate the average number of photons received by each SPAD. In other words, the number of photons received by each SPAD is the same, thus obtaining the number of photons received by each SPAD.

[0087] The number of photons received by the SPAD includes the first number of photons from the ambient light and the second number of photons from the reflected light.

[0088] S12. Determine the target avalanche probability of a single SPAD based on the target mapping relationship between the number of photons received by a single SPAD and the pre-constructed number of photons and avalanche probability.

[0089] In this step, the target mapping relationship is pre-constructed, and the methods for constructing the target mapping relationship include, but are not limited to, using the Poisson distribution function or Monte Carlo simulation.

[0090] The number of photons received by a single SPAD includes the number of photons corresponding to different types of light, denoted as the first photon count belonging to ambient light and the second photon count belonging to reflected light. The target avalanche probability includes the sub-target avalanche probability corresponding to different types of light, denoted as the first avalanche probability for ambient light and the second avalanche probability for reflected light. The first avalanche probability represents the probability that a single SPAD may avalanche after receiving the first number of photons due to ambient light influence; the second avalanche probability represents the probability that a single SPAD may avalanche after receiving the second number of photons due to reflected light influence.

[0091] The first avalanche probability is determined based on the number of first photons received at the current moment; the second avalanche probability is determined based on the number of second photons received at the current moment. That is, the first avalanche probability of a single SPAD can be determined based on the mapping relationship between the number of first photons received by the individual SPAD and the target. The second avalanche probability of the individual SPAD is determined based on the mapping relationship between the number of second photons received by the individual SPAD and the target. The steps for determining the first and second avalanche probabilities are the same; please refer to the subsequent steps for determining the "sub-target avalanche probability," which will not be detailed here.

[0092] S13. Based on the target avalanche probability and the total number of SPADs, calculate the avalanche probability of avalanche occurring for each different number of SPADs, and generate the initial avalanche probability distribution of the SPAD array.

[0093] In this step, the initial avalanche probability distribution includes sub-initial avalanche probability distributions corresponding to different types of light, denoted as the first avalanche probability distribution corresponding to ambient light and the second avalanche probability distribution corresponding to reflected light. The first avalanche probability distribution represents the probability of avalanches occurring in the SPAD array due to ambient light, with different numbers of SPADs (within the range M, where M represents the total number of SPADs in the SPAD array) experiencing avalanches. The second avalanche probability distribution represents the probability of avalanches occurring in the SPAD array due to reflected light, with different numbers of SPADs (within the range M).

[0094] For example, the curve characterizing the initial avalanche probability distribution includes a first horizontal axis and a first vertical axis. The first horizontal axis represents the different numbers m of SPADs that will cause avalanches, where m ≤ M, and m is an integer, for example, m takes the values ​​0, 1, 2, ..., M; the first vertical axis represents the probability P of avalanches occurring with different numbers of SPADs. HIST The initial avalanche probability distribution is denoted as P. HIST (m,M), the first avalanche probability distribution is denoted as P HIST_BG (m,M), the second avalanche probability distribution is denoted as P HIST_SIG (m,M).

[0095] The first avalanche probability distribution is determined based on the first avalanche probability in S12; the second avalanche probability distribution is determined based on the second avalanche probability in S12. That is, the probability of avalanche occurrence for different numbers of SPADs can be calculated based on the first avalanche probability and the total number of SPADs, thus generating the first avalanche probability distribution for the SPAD array. Similarly, the probability of avalanche occurrence for different numbers of SPADs can be calculated based on the second avalanche probability and the total number of SPADs, thus generating the second avalanche probability distribution for the SPAD array. The steps for determining the first and second avalanche probability distributions are the same; please refer to the subsequent steps for determining the "sub-initial avalanche probability distribution," which will not be detailed here.

[0096] S14. Iterative calculations are performed using the initial avalanche probability distribution to obtain the target avalanche probability distribution of the SPAD array.

[0097] In this step, the target avalanche probability distribution includes sub-target avalanche probability distributions corresponding to different types of light, denoted as the third avalanche probability distribution for ambient light and the fourth avalanche probability distribution for reflected light, respectively. The third avalanche probability distribution represents the peak probability of avalanches occurring in different numbers (within the S×M range) of SPADs caused by ambient light after the iterative calculation; the fourth avalanche probability distribution represents the peak probability of avalanches occurring in different numbers (within the S×M range) of SPADs caused by reflected light after the iterative calculation. Here, S represents the iteration number, and S is a positive integer greater than or equal to 2.

[0098] For example, the curve characterizing the avalanche probability distribution of the sub-target includes a second horizontal axis and a second vertical axis. The second horizontal axis represents the different numbers v of SPADs that cause avalanches after (S-1) iterations, where v ≤ S×M and v is rounded down, for example, v takes values ​​of 0, 1, 2, ..., S×M. The second vertical axis represents the peak probability of different numbers of SPADs causing avalanches after (S-1) iterations. The probability distribution of a sub-target avalanche is denoted as The third avalanche probability distribution is denoted as... The fourth avalanche probability distribution is denoted as

[0099] The above steps S11 to S13 describe the process of measuring the initial avalanche probability distribution of the SPAD array. S iterations require S initial avalanche probability distributions.

[0100] In one possible implementation, the initial avalanche probability distribution of the SPAD array is the same in each of the S measurements. Therefore, only one test is needed to obtain the initial avalanche probability distribution of the SPAD array under the S measurements. Based on this, the initial avalanche probability distribution is iterated S times to obtain the target avalanche probability distribution of the SPAD array.

[0101] In another possible implementation, S11 to S13 are executed S times to obtain S initial avalanche probability distributions for the SPAD array from a single measurement. Then, iterative calculations are performed based on these S initial avalanche probability distributions to obtain the target avalanche probability distribution for the SPAD array. Specifically, each execution of S11 to S13 generates a first avalanche probability distribution and a second avalanche probability distribution; this process is repeated S times to generate S first avalanche probability distributions and S second avalanche probability distributions. Following the loop order, iterative calculations are performed on the 1 to S first avalanche probability distributions to obtain the third avalanche probability distribution after S-1 iterations. Similarly, iterative calculations are performed on the 1 to S second avalanche probability distributions to obtain the fourth avalanche probability distribution after S-1 iterations. In the i-th iteration, the sub-target avalanche probability distribution is obtained using the results of the previous i-1 iterations and the i-th sub-initial avalanche probability distribution.

[0102] S15. Based on the target avalanche probability distribution, predict the ranging performance of the lidar.

[0103] Among them, the ranging performance of SPAD lidar includes at least the limit detection distance of SPAD lidar.

[0104] Based on the fourth avalanche probability distribution corresponding to the reflected light, the detection probability to be verified of the SPAD lidar is calculated; based on the third avalanche probability distribution corresponding to the ambient light, the false alarm probability to be verified of the SPAD lidar is calculated. If the detection probability to be verified is greater than or equal to the first preset detection probability threshold and the false alarm probability to be verified is less than or equal to the second preset detection probability threshold, and if at least one of the detection probability to be verified or the false alarm probability to be verified satisfies the critical condition, then the current preset test distance is taken as the limit detection distance of the lidar.

[0105] Among them, the detection probability to be verified can characterize the probability of identifying a target as a target, that is, the confidence level of identifying a target under the target recognition accuracy; the false alarm probability to be verified characterizes the probability of identifying noise as a target.

[0106] The performance simulation method for SPAD lidar provided in this disclosure simplifies the traditional simulation process and improves computational efficiency by constructing a first preset algorithm for calculating the sub-target avalanche probability of a single SPAD, a second preset algorithm for calculating the sub-initial avalanche probability distribution of the SPAD array, and a third preset algorithm for calculating the sub-target avalanche probability distribution of the SPAD array. Furthermore, through the first, second, and third preset algorithms, a single test process (S11-S15) can directly verify whether the preset test distance is the limit test distance. Compared to traditional technologies that require multiple Monte Carlo simulation calculations for the preset test distance, this disclosure effectively improves the link simulation efficiency of the SPAD lidar system while ensuring simulation accuracy.

[0107] In some embodiments, the step of determining the number of first photons received by the SPAD in step S11 includes S11-1-1 to S11-1-2, wherein:

[0108] S11-1-1. Based on the intensity of ambient light, the bandwidth of the SPAD lidar filter, the vertical and horizontal field of view of the SPAD lidar, the reflectivity of the target observed by the SPAD lidar, the receiving aperture of the SPAD lidar, the receiving efficiency of the SPAD lidar, the wavelength of the laser emitted by the SPAD lidar, Planck's constant, and the speed of light, determine the first number of photons of ambient light received by the SPAD array.

[0109] In this step, the filter is an optical film in a SPAD lidar, and may include one or more. When multiple filters are used, the filter bandwidth characterizes the equivalent bandwidth calculated from the multiple filters. The filter bandwidth refers to the wavelength range within which the filter's transmittance exceeds a certain value, and is typically used to describe the filter's transmittance performance. The reflectivity of the target observed by the SPAD lidar refers to the reflectivity of the target object after the lidar emits laser light and it is reflected back. Since the lidar emits laser light, which is reflected back by the target object, the reflectivity of the target object affects the number of photons received. The lidar's receiving aperture refers to the aperture through which the lidar receives light.

[0110] The bandwidth of the filter, the vertical and horizontal field of view of the SPAD lidar, the reflectivity of the target observed by the SPAD lidar, the receiving aperture of the SPAD lidar, the receiving efficiency of the SPAD lidar, and the wavelength of the laser emitted by the SPAD lidar are all system parameters of the SPAD lidar, which are preset and remain unchanged during the simulation. Planck's constant and the speed of light are constants and also remain unchanged.

[0111] The intensity of ambient light is limited by the environment in which the SPAD lidar is located and is variable; different ambient light intensities correspond to different numbers of the first photons received.

[0112] The number of ambient light photons received by the SPAD array is determined according to Formula 1 below.

[0113]

[0114] Where Amb represents the intensity of ambient light; B OF θ represents the bandwidth of the filter in a SPAD lidar system. V and θ H ρ and S represent the vertical and horizontal field of view of the SPAD lidar, respectively; ρ represents the reflectivity of the target observed by the SPAD lidar; S represents the vertical and horizontal field of view of the target observed by the SPAD lidar. REC Indicates the receiving aperture of the SPAD lidar; η R λ represents the receiving efficiency of the SPAD lidar; h represents Planck's constant; and c represents the speed of light.

[0115] S11-1-2. Determine the first photon count based on the number of ambient light photons received by the SPAD array and the total number of SPADs.

[0116] Specifically, it can be assumed that the energy distribution of the light spot within the SPAD array is uniform; therefore, the first photon number can be equal to... M represents the total number of SPADs in the SPAD matrix.

[0117] In some embodiments, the step of determining the number of second photons received by the SPAD in step S11 includes S11-2-1 to S11-2-3, wherein:

[0118] S11-2-1. The SPAD lidar's transmit power, vertical and horizontal divergence angles, vertical and horizontal field of view, reflectivity of the target observed by the SPAD lidar, receiving aperture, receiving and transmitting efficiency, wavelength of the laser emitted by the SPAD lidar, Planck's constant, speed of light, and preset test distance are used to determine the number of photons of the reflected light received by the SPAD array.

[0119] In this step, emission efficiency refers to the emission power of the laser emitted by the SPAD lidar; emission efficiency refers to the emission efficiency of the laser emitted by the SPAD lidar. The preset test distance refers to the distance between the SPAD lidar and the target to be measured. The preset test distance remains unchanged for a single measurement; however, in different measurement processes, the preset test distance is adjusted based on the ranging performance of the predicted lidar during process S15.

[0120] The SPAD lidar's transmit power, vertical and horizontal divergence angles, vertical and horizontal field of view, reflectivity of the observed target, receiver aperture, receiver and transmitter efficiency, and wavelength of the emitted laser are all pre-set system parameters that remain constant during the simulation. Planck's constant and the speed of light are also constants and remain unchanged.

[0121] The number of photons of the reflected light received by the SPAD array is determined according to Formula 2 below.

[0122]

[0123] Among them, P T Indicates the transmit power of the SPAD lidar; α V and α H η represents the vertical divergence angle and the horizontal divergence angle of the SPAD lidar, respectively. T R represents the emission efficiency of the SPAD lidar; R represents the preset test distance.

[0124] S11-2-2. Determine the maximum number of photons based on the number of photons of the reflected light received by the SPAD array and the total number of SPADs.

[0125] Specifically, it can be assumed that the energy distribution of the light spot within the SPAD array is uniform; therefore, the maximum number of photons can be equal to...

[0126] S11-2-3. Determine the second photon number based on the fitting coefficients corresponding to the maximum photon number and the maximum photon number at the current moment.

[0127] Wherein, the fitting coefficient f ps It is related to the waveform of the laser emitted by the lidar, which changes over time.

[0128] The number of second photons is related to the waveform shape of the laser emitted by the SPAD lidar. For example, if the waveform of the laser emitted by the lidar is a rectangular waveform, the number of second photons is constant, that is, the fitting coefficient is set to 1. It should be noted that the duty cycle duration corresponding to one rising edge of the rectangular waveform is the duration of executing one S11 to S13 process.

[0129] For example, if the waveform of the laser emitted by the lidar is a Gaussian waveform, then the change in the second photon number follows a Gaussian distribution, which is the maximum photon number multiplied by the fitting coefficient at different times. Specifically, in the process of a single measurement and a single SPAD's second avalanche probability, the second photon number at the target center time = maximum photon number × 1; the distance from the target center time t... c The further back in time, the lower the fitting coefficient and the fewer the number of second photons, where the target center time t c The expression is

[0130] In some embodiments, the step of pre-constructing the target mapping relationship for S12 includes: calculating the avalanche probability corresponding to each different number of photons received by a single SPAD according to the Poisson distribution function, and constructing the target mapping relationship.

[0131] In a single measurement, the mapping between the number of photons received by a single SPAD and the avalanche probability of that single SPAD follows a Poisson distribution. Therefore, the probability of an avalanche occurring when a single SPAD receives different numbers of photons can be directly calculated based on the Poisson distribution function, thus obtaining the avalanche probabilities corresponding to each different number of photons. Then, based on the calculated avalanche probabilities corresponding to each different number of photons, a target mapping relationship is constructed, as shown in Formula 3 below.

[0132] P act =f N2P (N)………………………………Formula 3

[0133] Where N represents the number of photons received by a single SPAD in a single measurement, and P actThis represents the probability that an avalanche will occur when SPAD receives N photons. N is an integer greater than or equal to 0.

[0134] In some embodiments, for S12, the step of pre-constructing the target mapping relationship can also be: multiple Monte Carlo simulations can be used to obtain the probability of an avalanche occurring in a single SPAD when receiving different numbers of photons; similarly, based on the calculated avalanche probabilities corresponding to each different number of photons, a target mapping relationship is constructed, as shown in Formula 1 above. It should be noted that in this case, the Monte Carlo simulation is different from... Figure 1 The Monte Carlo simulation process shown here does not require constructing the probability of an avalanche occurring over the entire duration of the laser waveform emitted by the SPAD lidar; it only needs to simulate the probability of an avalanche occurring in a single SPAD. Therefore, the simulation time is short. Furthermore, this mapping relationship is independent of the SPAD lidar parameters, so once constructed, it can be used for all subsequent simulations, improving simulation efficiency.

[0135] In some embodiments, after an avalanche occurs in a single SPAD, there is a certain rise time and dead time. During this period, the avalanche probability of the SPAD will be lower than normal. This phenomenon can be equivalent to a decrease in the number of photons, and can be expressed as: in t represents the photon number attenuation coefficient. rise and t dead t' represents the ascent time and the time of death, respectively, and t' represents the time since the last avalanche.

[0136] Figure 2 A flowchart for determining the sub-target avalanche probability of a single SPAD, as provided in embodiments of this disclosure, is shown below. Figure 2 As shown, specifically including S121 to S125, wherein:

[0137] S121. Construct the first state matrix at the current time.

[0138] The first state matrix includes elements corresponding to the current time step. Each element contains a pre-set first prediction probability and a first photon count attenuation coefficient. The first prediction probability represents the probability of the current state occurring; the first photon count attenuation coefficient represents the photon count attenuation coefficient corresponding to that state. It should be noted that when the first state matrix is ​​constructed for the first time, the current time step is the initial time step. Because there is only one state at the initial time step, it is assumed that the state will definitely occur, therefore the first prediction probability at the initial time step is 1. Furthermore, since it is assumed that no avalanche has occurred in this state, there is no photon count attenuation in this state, so the photon count attenuation coefficient is set to 1 at the initial time step. That is, for the first state matrix constructed for the current time step, the data pairs containing elements [1,1] represent a first prediction probability of 1 and a first photon count attenuation coefficient of 1.

[0139] The initial state matrix has only one element. The number of elements in the subsequently generated second state matrix increases continuously.

[0140] Figure 3 This is a schematic diagram of the process for generating the second state matrix provided in an embodiment of this disclosure, as shown below. Figure 3 As shown, it includes steps S122, S1221 to S1223 and S124.

[0141] S122. Iterate through all elements in the first state matrix in sequence, including the following steps S1221 to S1223.

[0142] S1221. Based on the first predicted probability, the first attenuation coefficient of the photon number, the photon number corresponding to the same type of light at the current moment and the target mapping relationship, determine the first conditional probability of a single SPAD avalanche occurring at the next moment, and determine the second conditional probability of a single SPAD not avalanche occurring at the next moment.

[0143] The first conditional probability represents the probability that a single SPAD will avalanche in the next moment; the second conditional probability represents the probability that no avalanche will occur in the next moment.

[0144] In one possible implementation, for any element in the first state matrix, the attenuated photon number after attenuation is determined based on the first attenuation coefficient of the photon number and the photon number of the same type of light at the current time; the target attenuation probability of a single SPAD avalanche at the next time is determined based on the attenuated photon number and the target mapping relationship; and the first conditional probability of a single SPAD avalanche at the next time is determined based on the target attenuation probability and the first prediction probability.

[0145] Determine the first conditional probability P act For details, please refer to Formula 4 below:

[0146] P act =P prev ×f N2P (C prev Formula 4 (N)

[0147] Among them, P prev C represents the first predicted probability. prev The first attenuation coefficient represents the photon count; N represents the number of photons of the same type of light at the current moment, such as the first photon count or the second photon count; f N2P (x) represents the target mapping relationship, x represents the number of photons, and f N2P (x) represents the number of photons received x and the avalanche probability of a single SPAD avalanche; C prev ·N represents the number of decayed photons after decay at the next time step; f N2P (Cprev ·N) represents the target decay probability.

[0148] For any given moment, the sum of the first and second conditional probabilities of a single SPAD avalanche is 1. Determine the second conditional probability P. notact For details, please refer to Formula 5 below:

[0149] P notact =1-p act Formula 5

[0150] S1222. Based on the comparison result of whether the first conditional probability is equal to 0, generate the second attenuation coefficient of the photon number corresponding to the first conditional probability at the next moment.

[0151] First conditional probability p act When the probability is not equal to 0, generate the second attenuation coefficient C corresponding to the first conditional probability at the next time step. prev_1 The value is 0; in the first conditional probability P act When = 0, generate the second attenuation coefficient C corresponding to the first conditional probability at the next time step. prev_1 The value is (t+1) / T; where t represents the current time, and T = t rise +t dead .

[0152] S1223. Based on the comparison result of whether the second conditional probability is equal to 0, generate the second attenuation coefficient of the photon number corresponding to the second conditional probability at the next moment.

[0153] Second conditional probability p notact When the probability is not equal to 0, generate the second attenuation coefficient C of the photon number corresponding to the second conditional probability at the next time step. prev_2 The value is (t+1) / T; in the second conditional probability P notact When = 0, generate the second attenuation coefficient C of the photon number corresponding to the second conditional probability at the next time step. prev_2 It is 0.

[0154] S123. Based on the first conditional probability, the second conditional probability, the second attenuation coefficient of the photon number corresponding to the first conditional probability, and the second attenuation coefficient of the photon number corresponding to the second conditional probability obtained after traversing each element, the second state matrix is ​​obtained.

[0155] Specifically, the first conditional probability and the corresponding second photon number attenuation coefficient can be used as new elements, as can the second conditional probability and the corresponding second photon number attenuation coefficient, to obtain the second state matrix. Furthermore, to improve simulation efficiency, elements with a conditional probability of 0 can be removed from the second state matrix after it is obtained.

[0156] For example, the second state matrix includes elements [P act C prev_1 ;P notact C prev_2 The data pairs.

[0157] S124. Based on the second state matrix, determine the second predicted probability of a single SPAD avalanche occurring at the next moment.

[0158] After this step, the second state matrix of the next time step is used as the first state matrix of the current time step, and the next time step is also used as the current time step. Then, return to step S122 and start looping through steps S122 to S124 until the determined second prediction probability converges.

[0159] The steps for determining the second predicted probability include: selecting the second attenuation coefficient of the photon number that is not equal to 1 from the elements containing the second conditional probability in the second state matrix; and using the second conditional probability corresponding to the second attenuation coefficient of the photon number that is not equal to 1, and the sum of all the first conditional probabilities in the second state matrix, as the second predicted probability of a single SPAD avalanche occurring at the next moment.

[0160] For example, the second state matrix includes elements of [P] act_1 C prev_1_1 ;P notact_1 C prev_2_1 ;P act_2 C prev_1_2 ;P notact_2 C prev_2_2 Given C prev_2_1 ≠1 and C prev_2_2 =1, select elements that are not equal to 1 from the second decay coefficient of photon number, and calculate the second predicted probability P of a single SPAD avalanche at the next moment. ACT =P acr_1 +P act_2 +P notact_1 It should be noted that the sum of all conditional probabilities in the second state matrix is ​​1.

[0161] Continuously execute S122 to S124, and each execution of S122 to S124 yields a second prediction probability P. ACT This cycle continues, eventually reaching P. ACT The process involves a continuous rise, a gradual leveling off, and then a decline. This process from leveling off to declining is called convergence.

[0162] S125. The maximum value of the second predicted probability is taken as the sub-target avalanche probability of a single SPAD.

[0163] Multiple second prediction probabilities P ACTThe maximum value is used as the sub-target avalanche probability of a single SPAD.

[0164] The subtarget avalanche probability can be either the first avalanche probability or the second avalanche probability. The first avalanche probability is the probability that a single SPAD may avalanche after receiving the first number of photons, influenced by ambient light, and is denoted as P. ACT-BG The second avalanche probability, also known as the probability of an avalanche occurring after a single SPAD receives a second number of photons, is affected by reflected light and is denoted as P. ACT-S .

[0165] For example, the first state matrix constructed at the current moment includes data pairs with elements [1,1], representing a first prediction probability of 1 and a first photon number attenuation coefficient of 1. Based on this first state matrix [1,1], the second state matrix for the next moment is determined, specifically: based on the first prediction probability P... prev =1, the first attenuation coefficient of photon number C prev =1. The number of photons N corresponding to the same type of light at the current moment and the target mapping relationship f N2P (N), according to Formula 4, the first conditional probability P is calculated. act =f N2P (N), second conditional probability P notact =1-P act That is, P notact =1-f N2P (N). At this point, the first conditional probability P act It is entirely influenced by the number N of photons of the same type of light at the current moment. If P is determined... act =f N2P If (N)≠0, then the first conditional probability P is generated at the next moment. act The corresponding photon number second attenuation coefficient is 0; if P is determined notact =1-P act If the probability is not equal to 0, then the second attenuation coefficient C corresponding to the second conditional probability at the next time step is generated. prev_2 The value is (t+1) / T. At this point, the second state matrix includes two elements, that is, two data pairs [f]. N2P (N), 0;1-f N2P (N),(t+1) / T].

[0166] In some embodiments, the initial avalanche probability distribution includes sub-initial avalanche probability distributions corresponding to different types of light, denoted as the first avalanche probability distribution corresponding to ambient light and the second avalanche probability distribution corresponding to reflected light, respectively.

[0167] Specifically, based on the sub-target avalanche probability The initial avalanche probability distribution is obtained by using the second preset algorithm with the total number M of SPADs and the different numbers m of SPADs that will cause an avalanche.

[0168] For an example, the calculation process of the initial avalanche probability distribution is shown in Formula 6 below.

[0169]

[0170] Among them, P HIST (m,M) represents the initial avalanche probability distribution, including the probability of avalanche occurrence for each different number of SPADs; m represents the number of SPADs that will experience an avalanche; M represents the total number of SPADs. This represents the formula for combination calculation; This represents the sub-target avalanche probability of a single SPAD.

[0171] The steps for determining the first and second avalanche probability distributions are the same, and can be found in Formula 6 above; repeated parts will not be elaborated further. The first avalanche probability distribution is denoted as P. HIST_BG (m,M), the second avalanche probability distribution is denoted as P HIST_SIG (m,M).

[0172] In some embodiments, the target avalanche probability distribution includes sub-target avalanche probability distributions corresponding to different types of light, respectively denoted as the third avalanche probability distribution corresponding to ambient light and the fourth avalanche probability distribution corresponding to reflected light.

[0173] Specifically, the sub-target avalanche probability distribution of the SPAD array under S measurements can be obtained based on the S-1th sub-target avalanche probability distribution calculated iteratively and the Sth initial sub-avalanche probability distribution.

[0174] For example, the sub-target avalanche probability distribution obtained from the first measurement is the sub-initial avalanche probability distribution; when S=2, based on the first and second sub-initial avalanche probability distributions, the sub-target avalanche probability distribution of the SPAD array under two measurements is obtained; when S=3, based on the sub-target avalanche probability distribution obtained when S=2 and the third sub-initial avalanche probability distribution, the sub-target avalanche probability distribution of the SPAD array under three measurements is obtained.

[0175] For an example, the calculation process of the sub-target avalanche probability distribution is shown in Formula 7 below.

[0176]

[0177] in, P represents the probability distribution of avalanches for the sub-target; v represents the total number of SPADs that cause avalanches after S measurements, for example, v can be 0, 1, 2, ..., S×M; HIST (vi,M) represents the probability of the number of avalanches occurring in (vi) SPADs under the S-th initial avalanche probability distribution; P represents the peak probability of an avalanche occurring in the i-th SPAD under the sub-target avalanche probability distribution obtained from the previous (S-1) iterations; HIST (v,M) represents the initial avalanche probability distribution determined in the first iteration.

[0178] The steps for determining the third avalanche probability distribution corresponding to ambient light and the fourth avalanche probability distribution corresponding to reflected light are the same, and can be found in Formula 7 above. Repeated parts will not be elaborated further. The third avalanche probability distribution is denoted as... The fourth avalanche probability distribution is denoted as

[0179] In some embodiments, Figure 4 A flowchart illustrating the predictive ranging performance of a SPAD lidar according to an embodiment of this disclosure is shown below. Figure 4 As shown, steps S151 to S153 are included, wherein:

[0180] S151. Based on the third avalanche probability distribution and the target detection threshold of the lidar, the false alarm probability of the lidar to be verified is obtained.

[0181] In this step, the target detection threshold is calculated based on the system parameters of the LiDAR. The false alarm probability P to be verified is then determined. FA For the process, please refer to Formula 8 below.

[0182]

[0183] in, V represents the probability distribution of the third avalanche. DET Indicates the target detection threshold; In this case, v is not less than V. DET And not greater than the value of S·M.

[0184] S152. Based on the fourth avalanche probability distribution and the target detection threshold of the lidar, the detection probability to be verified by the lidar is obtained.

[0185] Determine the detection probability P to be verified DET For the process, please refer to Formula Nine below.

[0186]

[0187] in, This represents the probability distribution of the fourth avalanche. In this case, v is not less than V. DET And not greater than the value of S·M.

[0188] S153. Obtain the first comparison result between the detection probability to be verified and the first preset detection probability threshold and the second comparison result between the false alarm probability to be verified and the second preset false alarm probability threshold. If at least one of the first comparison result and the second comparison result satisfies the critical condition, the preset test distance is taken as the limit detection distance of the lidar.

[0189] In this step, the first preset detection probability threshold α1 is an industry-defined critical value used to verify the detection probability under extreme ranging conditions. The second preset false alarm probability threshold α2 is an industry-defined critical value used to verify the false alarm probability under extreme ranging conditions.

[0190] When the probability of detection to be verified is less than the first preset detection probability threshold α1, it means that the lidar ranging accuracy is unqualified. When the probability of detection to be verified is greater than or equal to the first preset detection probability threshold, it means that the lidar ranging accuracy is qualified. For example, the first preset detection probability threshold is 90%.

[0191] When the false alarm probability to be verified is greater than the second preset detection probability threshold α2, it means that the lidar ranging accuracy is unqualified. When the detection probability to be verified is less than or equal to the second preset detection probability threshold, it means that the lidar ranging accuracy is qualified. For example, the second preset detection probability threshold is 10%.

[0192] In P FA =α1,P DET When ≥α2, the preset test distance is taken as the limit detection distance of the lidar. Alternatively, in P... FA ≤α1, P DET If α = 2, the preset test distance is taken as the limit detection distance of the lidar. Otherwise, repeat the process from S11 to S15 until the condition is met.

[0193] This disclosure addresses the issues of high computational complexity and low efficiency in current SPAD lidar link simulations by constructing a target mapping relationship and deriving analytical formulas for the sub-target avalanche probability of a single SPAD, the sub-initial avalanche probability distribution of the SPAD array, and the sub-target avalanche probability distribution of the SPAD array, namely formulas four and five, formula six, and formula seven. The ultimate ranging performance of the SPAD lidar is directly calculated using these formulas, avoiding extensive numerical simulations and improving the efficiency of SPAD lidar link simulation.

[0194] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0195] In addition, this disclosure also provides a performance simulation device for SPAD lidar corresponding to the performance simulation method for SPAD lidar. Since the principle of the device in this disclosure for solving the problem is similar to the performance simulation method for SPAD lidar described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0196] Figure 5 A schematic diagram of the performance simulation apparatus provided in the embodiments of this disclosure, such as... Figure 5 As shown, the performance simulation apparatus is configured to perform a performance simulation method for SPAD lidar as described in any of the above embodiments.

[0197] The performance simulation device includes a photon number receiving module 51, a first processing module 52, a second processing module 53, a third processing module 54, and a fourth processing module 55.

[0198] The photon count determination module 51 is configured to acquire the number of photons received by the SPAD array and determine the number of photons received by a single SPAD at the current moment.

[0199] The first processing module 52 is configured to determine the target avalanche probability of a single SPAD based on the number of photons received by the single SPAD and the target mapping relationship between the number of photons and the avalanche probability pre-constructed.

[0200] The second processing module 53 is configured to calculate the probability of avalanche occurrence for each different number of SPADs based on the target avalanche probability and the total number of SPADs, and generate the initial avalanche probability distribution of the SPAD array.

[0201] The third processing module 54 is configured to perform iterative calculations using the initial avalanche probability distribution to obtain the target avalanche probability distribution of the SPAD array.

[0202] The fourth processing module 55 is configured to predict the ranging performance of the SPAD lidar based on the target avalanche probability distribution, wherein the ranging performance of the SPAD lidar includes at least the limit detection range of the SPAD lidar.

[0203] In some embodiments, the number of photons received by a single SPAD includes the number of photons corresponding to different types of light, denoted as the first photon number belonging to ambient light and the second photon number belonging to reflected light, respectively; the target avalanche probability includes the sub-target avalanche probability corresponding to different types of light, denoted as the first avalanche probability corresponding to ambient light and the second avalanche probability corresponding to reflected light, respectively.

[0204] The first processing module 52 includes a matrix construction unit, an element traversal unit, a matrix generation unit, a probability determination unit, and a target probability determination unit.

[0205] The first processing module 52 is specifically configured to determine the sub-target avalanche probability of a single SPAD, specifically including: a matrix construction unit, configured to construct a first state matrix at the current moment; the first state matrix includes elements corresponding to the current moment, and the elements include a pre-set first predicted probability and a first photon number attenuation coefficient; an element traversal unit, configured to sequentially traverse all elements in the first state matrix, and determine the first conditional probability of a single SPAD avalanche at the next moment, and the second conditional probability of a single SPAD not avalanche at the next moment, based on the first predicted probability, the first photon number attenuation coefficient, the photon number corresponding to the same type of light at the current moment, and the target mapping relationship; based on the comparison result of whether the first conditional probability is equal to 0, generate the second photon number attenuation coefficient corresponding to the first conditional probability at the next moment; and root Based on the comparison result of whether the second conditional probability is equal to 0, a second photon number attenuation coefficient corresponding to the second conditional probability at the next moment is generated; the matrix generation unit is configured to obtain a second state matrix based on the first conditional probability, the second conditional probability, the second photon number attenuation coefficient corresponding to the first conditional probability, and the second photon number attenuation coefficient corresponding to the second conditional probability obtained after traversing each element; the prediction probability determination unit is configured to determine the second prediction probability of a single SPAD avalanche at the next moment based on the second state matrix; and to use the second state matrix at the next moment as the first state matrix at the current moment, return to the element traversal unit to continue execution until the second prediction probability converges; the target probability determination unit is configured to use the maximum value of the second prediction probability as the sub-target avalanche probability of a single SPAD.

[0206] In some embodiments, the element traversal unit, in determining the first conditional probability, specifically includes: for any element in the first state matrix, determining the attenuated photon number after attenuation at the next moment based on the first attenuation coefficient of the photon number and the photon number corresponding to the same type of light at the current moment; determining the target attenuation probability of a single SPAD avalanche at the next moment based on the attenuated photon number and the target mapping relationship; and determining the first conditional probability of a single SPAD avalanche at the next moment based on the target attenuation probability and the first predicted probability.

[0207] In some embodiments, the formula for calculating the first conditional probability is: P act =P prev ·f N2P (C prev ·N); where P act Let P represent the first conditional probability. prev f represents the first predicted probability. N2P (C prev ·N) represents the target decay probability; C prev Represents the first attenuation coefficient of the photon count; N represents the number of photons of the same type of light at the current moment; C prev ·N represents the number of decayed photons after decay at the next time step.

[0208] In some embodiments, the first state matrix constructed for the current moment includes data pairs with elements [1,1], the first prediction probability is 1, and the first attenuation coefficient of the photon number is 1.

[0209] In some embodiments, the element traversal unit generates a second attenuation coefficient of the photon number corresponding to the first conditional probability at the next time step based on the comparison result of whether the first conditional probability is equal to 0. Specifically, this includes: if the first conditional probability is not equal to 0, generating a second attenuation coefficient of the photon number corresponding to the first conditional probability at the next time step of 0; if the first conditional probability is equal to 0, generating a second attenuation coefficient of the photon number corresponding to the first conditional probability at the next time step of (t+1) / T; where t represents the current time step, and T represents the sum of the rise time and dead time of the SPAD.

[0210] The element traversal unit generates the second attenuation coefficient of the photon number corresponding to the second conditional probability at the next moment based on the comparison result of whether the second conditional probability is equal to 0. Specifically, it generates the second attenuation coefficient of the photon number corresponding to the second conditional probability at the next moment as (t+1) / T when the second conditional probability is not equal to 0; and generates the second attenuation coefficient of the photon number corresponding to the first conditional probability at the next moment as 0 when the first conditional probability is equal to 0.

[0211] In some embodiments, the matrix generation unit is specifically configured to take the first conditional probability and the second photon number attenuation coefficient corresponding to the first conditional probability as new elements, and take the second conditional probability and the second photon number attenuation coefficient corresponding to the second conditional probability as new elements to obtain a second state matrix.

[0212] In some embodiments, the prediction probability determination unit is specifically configured to select a second attenuation coefficient of photon number that is not equal to 1 from the elements containing the second conditional probability in the second state matrix; and to use the second conditional probability corresponding to the second attenuation coefficient of photon number that is not equal to 1, and the sum of all the first conditional probabilities in the second state matrix, as the second prediction probability of a single SPAD avalanche occurring at the next moment.

[0213] In some embodiments, the process of the photon count receiving module 51 determining the first photon count specifically includes: determining the number of photons of ambient light received by the SPAD array based on the intensity of the ambient light, the bandwidth of the filter of the SPAD lidar, the vertical and horizontal field of view of the SPAD lidar, the reflectivity of the target observed by the SPAD lidar, the receiving aperture of the SPAD lidar, the receiving efficiency of the SPAD lidar, the wavelength of the laser emitted by the SPAD lidar, Planck's constant, and the speed of light; and determining the first photon count based on the number of photons of ambient light received by the SPAD array and the total number of SPADs.

[0214] In some embodiments, the number of photons of the reflected light received by the SPAD array is determined based on the SPAD lidar's emission power, vertical and horizontal divergence angles, vertical and horizontal field of view, reflectivity of the target observed by the SPAD lidar, receiving aperture, receiving and emission efficiency, wavelength of the laser emitted by the SPAD lidar, Planck's constant, speed of light, and the preset test distance. The maximum number of photons is determined based on the number of photons received by the SPAD array and the total number of SPADs. The second number of photons is determined based on the maximum number of photons and the fitting coefficient corresponding to the maximum number of photons at the current moment; the fitting coefficient is related to the waveform of the laser emitted by the SPAD lidar.

[0215] In some embodiments, the initial avalanche probability distribution includes sub-initial avalanche probability distributions corresponding to different types of light, denoted as the first avalanche probability distribution corresponding to ambient light and the second avalanche probability distribution corresponding to reflected light, respectively.

[0216] The formula for calculating the initial avalanche probability distribution is: Among them, P HIST (m,M) represents the initial avalanche probability distribution, including the probability of avalanche occurrence for each different number of SPADs; m represents the number of SPADs that will cause an avalanche, with different m values ​​representing different pre-set numbers of SPADs that will cause an avalanche; M represents the total number of SPADs. This represents the formula for combination calculation; This represents the sub-target avalanche probability of a single SPAD.

[0217] In some embodiments, the target avalanche probability distribution includes sub-target avalanche probability distributions corresponding to different types of light, which are respectively denoted as the third avalanche probability distribution corresponding to ambient light and the fourth avalanche probability distribution corresponding to reflected light.

[0218] The formula for calculating the probability distribution of a sub-target avalanche is:

[0219]

[0220] in, The sub-target avalanche probability distribution is represented by v; v represents the total number of SPADs that caused avalanches after S measurements; P HIST (vi,M) represents the probability of the number of avalanches occurring in (vi) SPADs under the S-th initial avalanche probability distribution; P represents the peak probability of an avalanche occurring in the i-th SPAD under the sub-target avalanche probability distribution obtained from the previous (S-1) iterations; HIST (v,M) represents the initial avalanche probability distribution determined in the first iteration.

[0221] In some embodiments, the third processing module 54 is specifically configured to perform S iterations on the initial avalanche probability distribution to obtain the target avalanche probability distribution of the SPAD array; S is a positive integer greater than or equal to 2.

[0222] In some embodiments, the second processing module 53 is specifically configured to generate the initial avalanche probability distribution of the SPAD array for S times; the third processing module 54 is specifically configured to perform iterative calculations based on the initial avalanche probability distribution for S times to obtain the target avalanche probability distribution of the SPAD array.

[0223] In some embodiments, the fourth processing module 55 is specifically configured to: obtain the false alarm probability of the SPAD LiDAR to be verified based on the third avalanche probability distribution and the target detection threshold of the SPAD LiDAR; obtain the detection probability of the SPAD LiDAR to be verified based on the fourth avalanche probability distribution and the target detection threshold of the SPAD LiDAR; obtain a first comparison result between the detection probability to be verified and a first preset detection probability threshold and a second comparison result between the false alarm probability to be verified and a second preset false alarm probability threshold; and, if at least one of the first comparison result and the second comparison result satisfies a critical condition, use the preset test distance as the limit detection distance of the SPAD LiDAR.

[0224] In some embodiments, the formula for calculating the probability of detection to be verified is: Among them, P DETIndicates the probability of detection to be verified. V represents the probability distribution of the fourth avalanche. DET Indicates the target detection threshold; In this case, v is not less than V. DET And not greater than the value of S·M;

[0225] The formula for calculating the probability of a false alarm to be verified is: Among them, P FA This represents the probability of a false alarm to be verified. This represents the probability distribution of the third avalanche. In this case, v is not less than V. DET And not greater than the value of S·M.

[0226] In some embodiments, the performance simulation apparatus further includes a mapping construction module, which is configured to calculate the avalanche probability corresponding to each different number of photons received by a single SPAD according to the Poisson distribution function, and construct a target mapping relationship.

[0227] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure. Figure 6 As shown, this disclosure provides a computer device including: one or more processors 601, a memory 602, and one or more I / O interfaces 603. The memory 602 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the performance simulation methods described in the above embodiments; the one or more I / O interfaces 603 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0228] The processor 601 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 602 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 603 is connected between the processor 601 and the memory 602, enabling information exchange between the processor 601 and the memory 602, including but not limited to a data bus (Bus).

[0229] In some embodiments, the processor 601, memory 602, and I / O interface 603 are interconnected via bus 604, and thus connected to other components of the computing device.

[0230] According to embodiments of this disclosure, a computer non-transient readable storage medium is also provided. This computer non-transient readable storage medium stores a computer program, wherein, when executed by a processor, the program implements the steps of any of the performance simulation methods described in the above embodiments.

[0231] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a machine-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined above in the system of this disclosure.

[0232] It should be noted that the computer-readable non-transient readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any non-transient readable computer storage medium other than a computer-readable storage medium, which can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the non-transient readable computer storage medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0233] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two adjacent blocks may actually represent substantially parallel execution, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0234] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this disclosure, and this disclosure is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this disclosure, and these modifications and improvements are also considered to be within the scope of protection of this disclosure.

Claims

1. A performance simulation method for SPAD lidar, characterized in that, include: Obtain the number of photons received by the single-photon avalanche diode SPAD array and determine the number of photons received by a single SPAD at the current moment; The target avalanche probability of a single SPAD is determined based on the target mapping relationship between the number of photons received by a single SPAD and the pre-constructed number of photons and the avalanche probability. Based on the target avalanche probability and the total number of SPADs, calculate the avalanche probability of avalanche occurring for each different number of SPADs, and generate the initial avalanche probability distribution of the SPAD array. The initial avalanche probability distribution is used for iterative calculations to obtain the target avalanche probability distribution of the SPAD array; Based on the target avalanche probability distribution, the ranging performance of the SPAD lidar is predicted, wherein the ranging performance of the SPAD lidar includes at least the limit detection range of the SPAD lidar.

2. The performance simulation method according to claim 1, characterized in that, The number of photons received by a single SPAD includes the number of photons corresponding to different types of light, denoted as the first photon count belonging to ambient light and the second photon count belonging to reflected light, respectively; the target avalanche probability includes the sub-target avalanche probability corresponding to different types of light, denoted as the first avalanche probability corresponding to ambient light and the second avalanche probability corresponding to reflected light, respectively. The steps for determining the sub-target avalanche probability of a single SPAD include: Construct a first state matrix for the current time; the first state matrix includes elements corresponding to the current time, and the elements include a pre-set first prediction probability and a first attenuation coefficient for the number of photons; The system iterates through all elements in the first state matrix. Based on the first predicted probability, the first photon count attenuation coefficient, the number of photons of the same type of light at the current moment, and the target mapping relationship, it determines the first conditional probability that a single SPAD will avalanche at the next moment, and the second conditional probability that a single SPAD will not avalanche at the next moment. Based on the comparison result of whether the first conditional probability is equal to 0, it generates the second photon count attenuation coefficient corresponding to the first conditional probability at the next moment. Based on the comparison result of whether the second conditional probability is equal to 0, it generates the second photon count attenuation coefficient corresponding to the second conditional probability at the next moment. The second state matrix is ​​obtained by traversing all elements, including the first conditional probability, the second conditional probability, the second photon number attenuation coefficient corresponding to the first conditional probability, and the second photon number attenuation coefficient corresponding to the second conditional probability. Based on the second state matrix, determine the second predicted probability of a single SPAD avalanche at the next moment; and take the second state matrix at the next moment as the first state matrix at the current moment, return to the step of traversing all elements in the first state matrix in turn until the second predicted probability of a single SPAD avalanche at the next moment is determined and execute it in a loop until the second predicted probability converges. The maximum value of the second predicted probability is taken as the sub-target avalanche probability of a single SPAD.

3. The performance simulation method according to claim 2, characterized in that, The step of determining the first conditional probability of a single SPAD avalanche occurring at the next moment based on the first predicted probability, the first attenuation coefficient of the photon number, the number of photons of the same type of light at the current moment, and the target mapping relationship includes: For any element in the first state matrix, the attenuated photon number at the next moment is determined based on the first attenuation coefficient of the photon number and the number of photons of the same type of light at the current moment. Based on the attenuation photon number and the target mapping relationship, determine the target attenuation probability of a single SPAD avalanche at the next moment; Based on the target decay probability and the first predicted probability, the first conditional probability of a single SPAD avalanche at the next moment is determined.

4. The performance simulation method according to claim 2 or 3, characterized in that, The formula for calculating the first conditional probability is: P act =P prev ·f N2P (C prev ·N); Among them, P act Let P represent the first conditional probability. prev f represents the first predicted probability. N2P (C prev ·N) represents the target attenuation probability; C prev The photon count represents the first attenuation coefficient; N represents the number of photons of the same type of light at the current moment; C prev ·N represents the number of decayed photons after decay at the next time step.

5. The performance simulation method according to claim 4, characterized in that, For the first state matrix constructed at the current moment, which includes data pairs with elements [1,1], the first prediction probability is 1, and the first attenuation coefficient of the photon number is 1.

6. The performance simulation method according to claim 2, characterized in that, The step of generating a second attenuation coefficient for the number of photons corresponding to the first conditional probability at the next moment, based on the comparison result of whether the first conditional probability is equal to 0, includes: When the first conditional probability is not equal to 0, the second attenuation coefficient of the photon number corresponding to the first conditional probability at the next moment is 0; when the first conditional probability is equal to 0, the second attenuation coefficient of the photon number corresponding to the first conditional probability at the next moment is (t+1) / T; where t represents the current moment and T represents the sum of the rise time and dead time of SPAD. The step of generating the second attenuation coefficient of the photon number corresponding to the second conditional probability at the next moment based on the comparison result of whether the second conditional probability is equal to 0 includes: When the second conditional probability is not equal to 0, the second attenuation coefficient of the photon number corresponding to the second conditional probability at the next moment is (t+1) / T; when the first conditional probability is equal to 0, the second attenuation coefficient of the photon number corresponding to the first conditional probability at the next moment is 0.

7. The performance simulation method according to claim 2 or 6, characterized in that, The second state matrix is ​​obtained by traversing all elements, including the first conditional probability, the second conditional probability, the second photon number attenuation coefficient corresponding to the first conditional probability, and the second photon number attenuation coefficient corresponding to the second conditional probability. The second state matrix is ​​obtained by taking the first conditional probability and the second photon number attenuation coefficient corresponding to the first conditional probability as new elements, and taking the second conditional probability and the second photon number attenuation coefficient corresponding to the second conditional probability as new elements.

8. The performance simulation method according to claim 6, characterized in that, The step of determining the second predicted probability of a single SPAD avalanche occurring at the next moment based on the second state matrix includes: The second attenuation coefficient of the photon number that is not equal to 1 is selected from the elements containing the second conditional probability in the second state matrix; The second conditional probability corresponding to the second attenuation coefficient of the photon number that is not equal to 1, and the sum of all the first conditional probabilities in the second state matrix, are used as the second predicted probability of a single SPAD avalanche occurring at the next moment.

9. The performance simulation method according to claim 2, characterized in that, The steps for determining the first photon number include: The number of photons of ambient light received by the SPAD array is determined based on the intensity of the ambient light, the bandwidth of the filter of the SPAD lidar, the vertical and horizontal field of view of the SPAD lidar, the reflectivity of the target observed by the SPAD lidar, the receiving aperture of the SPAD lidar, the receiving efficiency of the SPAD lidar, the wavelength of the laser emitted by the SPAD lidar, Planck's constant, and the speed of light. The first photon count is determined based on the number of ambient light photons received by the SPAD array and the total number of SPADs.

10. The performance simulation method according to claim 2, characterized in that, The steps for determining the second photon number include: The number of photons of the reflected light received by the SPAD array is determined based on the SPAD lidar's emission power, vertical and horizontal divergence angles, vertical and horizontal field of view, reflectivity of the target observed by the SPAD lidar, receiving aperture, receiving and emission efficiency, wavelength of the emitted laser light, Planck's constant, speed of light, and preset test distance. The maximum number of photons is determined based on the number of photons of the reflected light received by the SPAD array and the total number of SPADs. The second photon number is determined based on the maximum photon number and the fitting coefficient corresponding to the maximum photon number at the current moment, wherein the fitting coefficient is related to the waveform of the laser emitted by the SPAD lidar.

11. The performance simulation method according to claim 2, characterized in that, The initial avalanche probability distribution includes sub-initial avalanche probability distributions corresponding to different types of light, denoted as the first avalanche probability distribution corresponding to ambient light and the second avalanche probability distribution corresponding to reflected light, respectively. The formula for calculating the initial avalanche probability distribution is as follows: Among them, P HIST (m,M) represents the sub-initial avalanche probability distribution, including the probability of avalanche occurrence for each different number of SPADs; m represents the number of SPADs that will experience an avalanche; M represents the total number of SPADs. This represents the formula for combination calculation; This represents the avalanche probability of a sub-target of a single SPAD.

12. The performance simulation method according to claim 11, characterized in that, The target avalanche probability distribution includes sub-target avalanche probability distributions corresponding to different types of light, which are respectively denoted as the third avalanche probability distribution corresponding to ambient light and the fourth avalanche probability distribution corresponding to reflected light. The formula for calculating the avalanche probability distribution of the sub-target is: in, The sub-target avalanche probability distribution is represented by v; v represents the total number of SPADs that caused avalanches after S measurements; P HIST (vi,M) represents the probability of the number of avalanches occurring in (vi) SPADs under the S-th initial avalanche probability distribution; P represents the peak probability of an avalanche occurring in the i-th SPAD under the sub-target avalanche probability distribution obtained from the previous (S-1) iterations; HIST (v,M) represents the initial avalanche probability distribution determined in the first iteration.

13. The performance simulation method according to claim 1, characterized in that, The step of iteratively calculating the target avalanche probability distribution of the SPAD array using the initial avalanche probability distribution includes: The initial avalanche probability distribution is iterated S times to obtain the target avalanche probability distribution of the SPAD array; S is a positive integer greater than or equal to 2.

14. The performance simulation method according to claim 1, characterized in that, After calculating the avalanche probability of different numbers of SPADs occurring based on the target avalanche probability and the total number of SPADs, and generating the initial avalanche probability distribution of the SPAD array, the method further includes: Return to the step of obtaining the number of photons received by the SPAD array until the initial avalanche probability distribution of the SPAD array is generated, and repeat the process S times; S is a positive integer greater than or equal to 2. The step of iteratively calculating the target avalanche probability distribution of the SPAD array using the initial avalanche probability distribution includes: The target avalanche probability distribution of the SPAD array is obtained by iterative calculation based on the initial avalanche probability distribution described in S times.

15. The performance simulation method according to claim 12, characterized in that, The step of predicting the ranging performance of the SPAD lidar based on the target avalanche probability distribution includes: Based on the third avalanche probability distribution and the target detection threshold of the SPAD lidar, the false alarm probability to be verified of the SPAD lidar is obtained. Based on the fourth avalanche probability distribution and the target detection threshold of the SPAD lidar, the detection probability to be verified of the SPAD lidar is obtained. Obtain a first comparison result between the detection probability to be verified and a first preset detection probability threshold, and a second comparison result between the false alarm probability to be verified and a second preset false alarm probability threshold. If at least one of the first comparison result and the second comparison result satisfies a critical condition, the preset test distance is taken as the limit detection distance of the SPAD lidar.

16. The performance simulation method according to claim 15, characterized in that, The formula for calculating the detection probability to be verified is: Among them, P DET Indicates the probability of detection to be verified. V represents the probability distribution of the fourth avalanche. DET This represents the target detection threshold; In this case, v is not less than V. DET And not greater than the value of S·M; The formula for calculating the false alarm probability to be verified is: Among them, P FA This represents the false alarm probability to be verified. This represents the probability distribution of the third avalanche. In this case, v is not less than V. DET And not greater than the value of S·M.

17. The performance simulation method according to claim 1, characterized in that, The steps for determining the target mapping relationship between the pre-constructed photon number and the avalanche probability include: Based on the different number of photons received by a single SPAD, the avalanche probability corresponding to each different number of photons is calculated according to the Poisson distribution function, and the target mapping relationship is constructed.

18. A performance simulation device for SPAD lidar, characterized in that, It includes a photon count receiving module, a first processing module, a second processing module, a third processing module, and a fourth processing module; The photon count determination module is configured to acquire the number of photons received by the SPAD array and determine the number of photons received by a single SPAD at the current moment. The first processing module is configured to determine the target avalanche probability of a single SPAD based on the number of photons received by a single SPAD and the target mapping relationship between the number of photons and the avalanche probability pre-constructed. The second processing module is configured to calculate the avalanche probability of avalanche occurring for each different number of SPADs based on the target avalanche probability and the total number of SPADs, and generate the initial avalanche probability distribution of the SPAD array. The third processing module is configured to perform iterative calculations using the initial avalanche probability distribution to obtain the target avalanche probability distribution of the SPAD array; The fourth processing module is configured to predict the ranging performance of the SPAD lidar based on the target avalanche probability distribution, wherein the ranging performance of the SPAD lidar includes at least the limit detection range of the SPAD lidar.

19. A computer device, characterized in that, include: The computer device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the performance simulation method as described in any one of claims 1 to 17 are performed.

20. A computer non-transient readable storage medium, characterized in that, The computer has a computer program stored on a non-transient readable storage medium, which is executed by a processor to perform the steps of the performance simulation method as described in any one of claims 1 to 17.

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