A method, device, equipment and storage medium for estimating geometric parameter calibration accuracy of spaceborne laser radar

Abstract

The present invention discloses a method, device, equipment and storage medium for estimating the geometric parameter calibration accuracy of a satellite-borne laser radar, and belongs to the field of satellite-borne laser radar technology. The method includes: determining the statistical characteristics of the positioning random error corresponding to the satellite-borne laser radar; determining the statistical characteristics of the terrain gradient in the reference elevation grid data; inputting the statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient into a positioning parameter calibration accuracy estimation model to obtain a calibration accuracy estimation result of the positioning parameter; inputting the calibration accuracy estimation result of the positioning parameter into a ranging and pointing parameter calibration accuracy estimation model to obtain a calibration accuracy estimation result of the ranging and pointing parameters. The technical solution provided by the present invention can improve the effectiveness and efficiency of the calibration accuracy estimation of the geometric parameters of the satellite-borne laser radar, thereby ensuring the reliability of the satellite-borne laser radar measurement data.

Description

Technical Field

[0001] The present invention relates to the field of satellite-borne laser radar technology, and in particular to a method, device, equipment and storage medium for estimating the geometric parameter calibration accuracy of a satellite-borne laser radar. Background Art

[0002] Spaceborne lidar technology provides a highly accurate remote sensing method for observing the Earth's surface. It has been widely used in global topographic mapping, marine environment and ecosystem surveys, climate change, and natural disaster research. Spaceborne lidar measures the distance between the satellite and surface targets by emitting laser pulses and receiving echoes. Combining the satellite's position, attitude, and laser pointing information, it can determine the three-dimensional geographic location of the surface target.

[0003] The positioning accuracy of spaceborne lidar (LiDAR) depends on the system's geometric parameters, namely, ranging values, satellite attitude, and laser pointing angle. However, due to the satellite platform's motion, payload placement errors, and the complexity of the onboard environment, these parameters can contain systematic and random errors, affecting measurement accuracy. Currently, there are three main methods for geometric parameter calibration: satellite attitude maneuvers, active detectors (including ground-based detectors and airborne infrared camera imaging), and natural surface matching (including waveform matching and terrain matching).

[0004] Comparing the calibration principles of the aforementioned methods reveals that satellite attitude maneuvers place extremely high demands on the control capabilities of the satellite platform, leading to decreased stability of multi-payload platforms. Active detectors are expensive in terms of both manpower and material resources, and the amount of calibration data is limited. Waveform matching is not suitable for currently developed single-photon lidars. Terrain matching, however, has become a more commonly used geometric parameter calibration scheme due to its relatively low site requirements, ease of implementation, and ability to achieve high-frequency calibration. The basic principle of the terrain matching method is to calibrate geometric parameters by matching spaceborne lidar measurement data with known, high-precision terrain reference data. However, random errors in the spaceborne lidar measurement data and the accuracy of the terrain reference data can affect the accuracy of the terrain matching results (i.e., the geometric parameter calibration results), potentially impacting the reliability and usability of the spaceborne lidar measurement data. Therefore, a more reliable method is needed to estimate the accuracy of the geometric parameter calibration results. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method, device, equipment and storage medium for estimating the geometric parameter calibration accuracy of satellite-borne laser radar, which can improve the accuracy and efficiency of the geometric parameter calibration accuracy estimation of satellite-borne laser radar, thereby ensuring the reliability of satellite-borne laser radar measurement data.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In one aspect, the present invention provides a method for estimating the geometric parameter calibration accuracy of a spaceborne laser radar, wherein the geometric parameters include positioning parameters and ranging and pointing parameters. The method comprises:

[0008] Determine the statistical characteristics of the random positioning errors corresponding to the spaceborne lidar;

[0009] Determine the statistical characteristics of terrain gradients in reference elevation raster data;

[0010] Inputting the statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient into a positioning parameter calibration accuracy estimation model to obtain a calibration accuracy estimation result of the positioning parameter;

[0011] The calibration accuracy estimation result of the positioning parameter is input into the ranging and pointing parameter calibration accuracy estimation model to obtain the calibration accuracy estimation result of the ranging and pointing parameter.

[0012] In some possible implementations, determining the statistical characteristics of the random positioning error corresponding to the spaceborne laser radar includes:

[0013] respectively determining the satellite position measurement random error, the satellite attitude measurement random error, the laser pointing measurement random error, and the laser distance measurement random error;

[0014] Based on the positioning model of the satellite-borne laser radar, as well as the satellite position measurement random error, the satellite attitude measurement random error, the laser pointing measurement random error, and the laser distance measurement random error, the covariance matrix of the positioning random error to be converted is determined; the covariance matrix of the positioning random error to be converted is the covariance matrix of the positioning random error in the geocentric inertial coordinate system, as shown in formula (1):

[0015] (1)

[0016] in, 、 、 They represent the random positioning errors of the satellite-borne laser radar in the X, Y and Z directions in the Earth-centered inertial coordinate system respectively; 、 、 They represent the variance of the random positioning error of the spaceborne lidar in the X, Y, and Z directions in the Earth-centered inertial coordinate system respectively; represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Y direction of the spaceborne lidar in the Earth-centered inertial coordinate system; represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Z direction of the spaceborne lidar in the Earth-centered inertial coordinate system; represents the cross-covariance between the random positioning error in the Y direction and the random positioning error in the Z direction of the spaceborne lidar in the Earth-centered inertial coordinate system; Represents the random error of satellite position measurement; Represents the random error of satellite attitude measurement; Represents the random error of laser pointing measurement; Indicates the random error of laser distance measurement; A Jacobian matrix representing position information of a target object measured by a satellite-borne laser radar with respect to multiple random errors; the multiple random errors include the satellite position measurement random error, the satellite attitude measurement random error, the laser pointing measurement random error, and the laser distance measurement random error;

[0017] Based on the covariance matrix of the positioning random error to be converted, the covariance matrix of the positioning random error is determined. The covariance matrix of the positioning random error is the covariance matrix of the positioning random error in the coordinate system of the reference elevation grid data used for geometric parameter calibration converted from the covariance matrix of the positioning random error to be converted, as shown in formula (2):

[0018] (2)

[0019] in, 、 、 They represent the random positioning errors of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data. 、 、 They represent the variance of the random positioning error of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data; It represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Y direction of the spaceborne lidar in the coordinate system of the reference elevation grid data. It represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Z direction of the spaceborne lidar in the coordinate system of the reference elevation grid data. It represents the cross-covariance between the random positioning error in the Y direction and the random positioning error in the Z direction of the spaceborne lidar in the coordinate system of the reference elevation grid data; Represents the rotation matrix from the geocentric inertial coordinate system to the coordinate system of the reference elevation grid data;

[0020] The variance of the random positioning errors of the satellite-borne laser radar in the X direction, Y direction, and Z direction in the coordinate system where the reference elevation grid data is located; the cross-covariance between the random positioning errors of the satellite-borne laser radar in the X direction and the random positioning errors in the Y direction in the coordinate system where the reference elevation grid data is located, the cross-covariance between the random positioning errors of the satellite-borne laser radar in the X direction and the random positioning errors in the Z direction in the coordinate system where the reference elevation grid data is located, and the cross-covariance between the random positioning errors of the satellite-borne laser radar in the Y direction and the random positioning errors in the Z direction in the coordinate system where the reference elevation grid data is located are used as statistical features of the random positioning errors.

[0021] In some possible implementations, determining statistical characteristics of terrain gradients in reference elevation grid data includes:

[0022] Acquire multiple laser radar measurement data that have been calibrated with geometric parameters;

[0023] Performing coordinate system conversion on geographic coordinate information corresponding to the plurality of laser radar measurement data to obtain a plurality of plane coordinate information in the coordinate system of the reference elevation raster data;

[0024] Determining a first gradient data set and a second gradient data set based on a plurality of plane coordinate information in a coordinate system where the reference elevation raster data is located;

[0025] Based on the first gradient data set and the second gradient data set, the first-order moment, second-order moment, third-order moment, and fourth-order moment corresponding to the first terrain gradient expected data, and the first-order moment, second-order moment, third-order moment, and fourth-order moment corresponding to the second terrain gradient expected data are determined, as shown in equations (3) and (4):

[0026] (3)

[0027] (4)

[0028] in, and They respectively represent the first expected terrain gradient data and the second expected terrain gradient data corresponding to the i-th laser radar measurement data, where the first expected terrain gradient data is the expected terrain gradient data in the X direction corresponding to the i-th laser radar measurement data, and the second expected terrain gradient data is the expected terrain gradient data in the Y direction corresponding to the i-th laser radar measurement data; and The n-th order moments representing the first terrain gradient expected data and the second terrain gradient expected data respectively; and Respectively represent the first gradient data set and the second gradient dataset The number of elements in the set, N represents the number of laser radar measurement data that have been calibrated with geometric parameters;

[0029] The first component standard deviation and the second component standard deviation of the terrain gradient are determined as shown in equations (5) and (6):

[0030] (5)

[0031] (6)

[0032] in, represents the first component standard deviation, represents the standard deviation of the second component; and Respectively represent the grid intervals of the reference elevation grid data in the X and Y directions; Indicates the elevation standard deviation of the reference elevation raster data;

[0033] Based on the first-order moment, second-order moment, third-order moment, and fourth-order moment corresponding to the first terrain gradient expected data, the first-order moment, second-order moment, third-order moment, and fourth-order moment corresponding to the second terrain gradient expected data, the first component standard deviation, and the second component standard deviation, the statistical characteristics of the terrain gradient are determined. The statistical characteristics of the terrain gradient include the first-order moment, second-order moment, third-order moment, and fourth-order moment of the first terrain gradient, and the first-order moment, second-order moment, third-order moment, and fourth-order moment of the second terrain gradient, as shown in equations (7) and (8):

[0034] (7)

[0035] (8)

[0036] in, represents the first terrain gradient, represents the second terrain gradient, where the first terrain gradient is the gradient component of the terrain gradient corresponding to the i-th lidar measurement data in the X direction, and the second terrain gradient is the gradient component of the terrain gradient corresponding to the i-th lidar measurement data in the Y direction; represents the first moment of the first terrain gradient, represents the second moment of the first terrain gradient, represents the third moment of the first terrain gradient, represents the fourth moment of the first topographic gradient; represents the first moment of the second terrain gradient, represents the second moment of the second terrain gradient, represents the third moment of the second terrain gradient, The fourth moment representing the second topographic gradient .

[0037] In some possible implementations, the positioning parameter calibration accuracy estimation model is constructed in the following manner:

[0038] The positioning parameter calibration accuracy estimation model is shown in formula (9):

[0039] (9)

[0040] in, 、 、 Respectively represent the uncertainty of the positioning parameter calibration results in the X direction, Y direction and Z direction, and are used to characterize the positioning parameter calibration accuracy in the X direction, Y direction and Z direction respectively; It represents the cross-covariance between the calibration accuracy of the positioning parameters in the X direction and the Y direction of the spaceborne lidar in the geocentric inertial coordinate system. It represents the cross-covariance between the calibration accuracy of the positioning parameters in the X direction and the Z direction of the spaceborne lidar in the geocentric inertial coordinate system. represents the cross-covariance between the Y-direction positioning parameter calibration accuracy and the Z-direction positioning parameter calibration accuracy of the spaceborne lidar in the geocentric inertial coordinate system; the matrix The Jacobian matrix representing the positioning calibration results of the elevation data corresponding to the lidar measurement data in the X, Y, and Z directions. δ → = [ δ 1 , δ 2 ,..., δ N ] Represents the random error of elevation of N lidar measurement data.

[0041] In some possible implementations, inputting the statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient into a positioning parameter calibration accuracy estimation model to obtain the positioning parameter calibration accuracy estimation result includes:

[0042] In the positioning parameter calibration accuracy estimation model E [ ( D T D ) − 1 ] and E [ D T δ → δ → T D ] , as shown in formula (10) and formula (11):

[0043] (10)

[0044] (11)

[0045] in, represents the random error of the elevation of the lidar measurement data, represents the first terrain gradient, represents the second terrain gradient;

[0046] Based on the relationship between the random error of elevation shown in equation (12) and the random error of positioning of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data, the elements in equations (10) and (11) are determined as shown in equations (13) to (18):

[0047] (12)

[0048] (13)

[0049] (14)

[0050] (15)

[0051] (16)

[0052] (17)

[0053] (18)

[0054] in, 、 、 They represent the random positioning errors of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data. represents the first moment of the first terrain gradient, represents the second moment of the first terrain gradient, represents the third moment of the first terrain gradient, represents the fourth moment of the first topographic gradient; represents the first moment of the second terrain gradient, represents the second moment of the second terrain gradient, represents the third moment of the second terrain gradient, represents the fourth moment of the second topographic gradient;

[0055] The statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient are input into a positioning parameter calibration accuracy estimation model for solution processing to obtain a calibration accuracy estimation result of the positioning parameter.

[0056] In some possible implementations, the laser pointing and ranging parameter calibration accuracy includes the pointing parameter calibration accuracy on the roll pointing angle, the pointing parameter calibration accuracy on the pitch pointing angle, and the ranging parameter calibration accuracy on the ranging value. The ranging and pointing parameter calibration accuracy estimation model is constructed in the following manner:

[0057] The ranging and pointing parameter calibration accuracy estimation model is shown in formula (19):

[0058] (19)

[0059] in, 、 、 They represent the uncertainty of the pointing parameter calibration result on the roll pointing angle, the uncertainty of the pointing parameter calibration result on the pitch pointing angle, and the uncertainty of the ranging parameter calibration result on the ranging value, respectively. They are used to characterize the pointing parameter calibration accuracy on the roll pointing angle, the pointing parameter calibration accuracy on the pitch pointing angle, and the ranging parameter calibration accuracy on the ranging value; represents the cross-covariance between the calibration accuracy of the pointing parameters on the roll pointing angle and the calibration accuracy of the pointing parameters on the pitch pointing angle, represents the cross-covariance between the calibration accuracy of the pointing parameters on the roll pointing angle and the calibration accuracy of the ranging parameters on the ranging value, It represents the cross-covariance between the pointing parameter calibration accuracy on the pitch pointing angle and the ranging parameter calibration accuracy on the ranging value; 、 、 Respectively represent the uncertainty of the positioning parameter calibration results in the X direction, Y direction and Z direction, and are used to characterize the positioning parameter calibration accuracy in the X direction, Y direction and Z direction respectively; It represents the cross-covariance between the calibration accuracy of the positioning parameters in the X direction and the Y direction of the spaceborne lidar in the geocentric inertial coordinate system. It represents the cross-covariance between the calibration accuracy of the positioning parameters in the X direction and the Z direction of the spaceborne lidar in the geocentric inertial coordinate system. It represents the cross-covariance between the calibration accuracy of the positioning parameters in the Y direction and the Z direction of the spaceborne lidar in the geocentric inertial coordinate system; Represents the rotation matrix from the coordinate system of the reference elevation grid data to the geocentric inertial coordinate system; is the Jacobian matrix of the roll pointing angle r, pitch pointing angle p, and ranging value l regarding the position measurement information of the target object measured by the satellite-borne lidar.

[0060] In some possible implementations, determining the first gradient dataset and the second gradient dataset based on multiple plane coordinate information in the coordinate system of the reference elevation raster data includes:

[0061] Based on the multiple plane coordinate information in the coordinate system of the reference elevation grid data, the index data set of multiple grid points in the preset range of each lidar measurement data in the reference elevation grid data is searched, as shown in formula (20):

[0062] (20)

[0063] in, A set of index data representing a plurality of grid points within a preset range of the reference elevation grid data for the i-th laser radar measurement data; the plane coordinate information includes first coordinate information and second coordinate information, and Respectively represent the first coordinate information and the second coordinate information corresponding to the i-th laser radar measurement data in the reference elevation raster data; and represents the first coordinate information and the second coordinate information corresponding to the grid point with index data (m, n) in the reference elevation raster data, where (m, n) represents the mth row and the nth column; r is the search radius corresponding to the index data set of multiple grid points in the preset range;

[0064] Based on the index data set, the terrain gradient data set corresponding to each lidar measurement data is determined by combining the gradient component in the X direction and the gradient component in the Y direction of the terrain gradient corresponding to each index data, as shown in equations (21) and (22):

[0065] (twenty one)

[0066] (twenty two)

[0067] in, represents the first gradient data set, represents the second gradient data set, the first gradient data set is the gradient component set of the terrain gradient corresponding to the i-th lidar measurement data in the X direction, and the second gradient data set is the gradient component set of the terrain gradient corresponding to the i-th lidar measurement data in the Y direction; and Respectively represent the gradient components of the terrain gradient in the X direction and the Y direction at the grid point with index data (m, n);

[0068] The gradient component in the X direction and the gradient component in the Y direction of the terrain gradient corresponding to each index data are determined based on equations (23) and (24):

[0069] (twenty three)

[0070] (twenty four)

[0071] in, Indicates that the plane coordinate information corresponding to the grid point with index data (m,n) in the reference elevation grid data is The elevation of the place; and Respectively represent the grid intervals in the X direction and Y direction in the reference elevation grid data.

[0072] On the other hand, a device for calibrating random errors of pointing and ranging of a space-borne laser radar is provided, wherein the geometric parameters include positioning parameters and ranging and pointing parameters, and the device comprises:

[0073] A module for determining the statistical characteristics of the random positioning errors is used to determine the statistical characteristics of the random positioning errors corresponding to the spaceborne laser radar;

[0074] A module for determining the statistical characteristics of terrain gradients, used to determine the statistical characteristics of terrain gradients in reference elevation grid data;

[0075] A positioning parameter calibration accuracy estimation module, configured to input the statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient into a positioning parameter calibration accuracy estimation model to obtain a positioning parameter calibration accuracy estimation result;

[0076] The ranging and pointing parameter calibration accuracy estimation module is used to input the positioning parameter calibration accuracy estimation result into the ranging and pointing parameter calibration accuracy estimation model to obtain the ranging and pointing parameter calibration accuracy estimation result.

[0077] On the other hand, an electronic device is provided, which includes a processor and a memory, wherein the memory stores at least one instruction and at least one program, and the at least one instruction and the at least one program are loaded and executed by the processor to implement the above-mentioned method for estimating the geometric parameter calibration accuracy of the satellite-borne laser radar.

[0078] On the other hand, a computer-readable storage medium is provided, in which at least one instruction and at least one program are stored. The at least one instruction and the at least one program are loaded and executed by a processor to implement the above-mentioned method for estimating the geometric parameter calibration accuracy of the satellite-borne laser radar.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] In the present invention, by determining the statistical characteristics of the random positioning error corresponding to the space-borne laser radar, the statistical characteristics of the terrain gradient in the reference elevation grid data used in the process of geometric parameter calibration of the space-borne laser radar using the terrain matching method are determined; then, the statistical characteristics of the random positioning error and the statistical characteristics of the terrain gradient are input into the positioning parameter calibration accuracy estimation model to obtain the calibration accuracy estimation result of the positioning parameter; then, the calibration accuracy estimation result of the positioning parameter is input into the ranging and pointing parameter calibration accuracy estimation model to obtain the calibration accuracy estimation result of the ranging and pointing parameter. The calibration accuracy estimation results of positioning parameters and ranging and pointing parameters can be used as evaluation indicators of geometric parameter calibration results, thereby realizing the estimation of geometric parameter calibration accuracy of spaceborne lidar. The calibration accuracy estimation results of positioning parameters are determined by using the positioning parameter calibration accuracy estimation model, and the calibration accuracy estimation results of ranging and pointing parameters are determined by using the ranging and pointing parameter calibration accuracy estimation model, which can improve the effectiveness, accuracy and efficiency of geometric parameter calibration accuracy estimation, thereby ensuring the reliability of spaceborne lidar measurement data. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0082] Figure 1 This is a flow chart of a method for estimating the geometric parameter calibration accuracy of a space-borne laser radar provided by an embodiment of the present invention;

[0083] Figure 2 Schematic diagram of a positioning model for measuring a target object by a space-borne laser radar provided in an embodiment of the present invention;

[0084] Figure 3 is the calibration accuracy estimation result of the space-borne laser radar positioning parameter calibration provided by an embodiment of the present invention;

[0085] Figure 4 It is a structural schematic diagram of a device for estimating the geometric parameters calibration accuracy of a satellite-borne laser radar provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0086] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0087] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0088] In the embodiments of the present invention, the term "module" or "unit" refers to a computer program or portion of a computer program that has a predetermined function and works together with other related components to achieve a predetermined goal. The term "module" or "unit" refers to a computer program or portion of a computer program that has a predetermined function and works together with other related components to achieve a predetermined goal. The term "module" or "unit" may be implemented in whole or in part using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a single processor (or multiple processors or memories) may be used to implement one or more modules or units. Furthermore, each module or unit may be part of an overall module or unit that incorporates the functionality of that module or unit.

[0089] Various exemplary embodiments, features, and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0090] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0091] The term "and / or" herein simply describes an association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can represent the existence of three situations: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0092] In addition, numerous specific details are provided in the following detailed description to better illustrate the present invention. Those skilled in the art will appreciate that the present invention may be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of the present invention.

[0093] Figure 1 It is a flow chart of a method for estimating the accuracy of the geometric parameters calibration of a satellite-borne laser radar provided by an embodiment of the present invention. This specification provides method operation steps such as the embodiment or flow chart, but may include more or fewer operation steps based on conventional or non-creative work. The order of steps listed in the embodiment is only one way of executing the steps among many, and does not represent the only execution order. When the actual system or server product is executed, it can be executed in sequence or in parallel (for example, in a parallel processor or multi-threaded processing environment) according to the method shown in the embodiment or the accompanying drawings. Specifically, Figure 1 As shown, the above method may include:

[0094] S101: Determine the statistical characteristics of the random positioning error corresponding to the spaceborne laser radar;

[0095] In a specific embodiment, the geometric parameters may include positioning parameters and ranging and pointing parameters; optionally, the geometric parameters of the satellite-borne laser radar are parameters calibrated by a terrain matching method.

[0096] In an optional embodiment, the above-mentioned determination of the statistical characteristics of the random positioning error corresponding to the space-borne laser radar includes:

[0097] Determine the satellite position measurement random error, satellite attitude measurement random error, laser pointing measurement random error and laser distance measurement random error respectively;

[0098] Based on the positioning model of the satellite-borne lidar, as well as the satellite position measurement random error, satellite attitude measurement random error, laser pointing measurement random error, and laser distance measurement random error, the covariance matrix of the positioning random error to be converted is determined; the covariance matrix of the positioning random error to be converted is the covariance matrix of the positioning random error in the geocentric inertial coordinate system, as shown in formula (1):

[0099] (1)

[0100] in, 、 、 They represent the random positioning errors of the satellite-borne laser radar in the X, Y and Z directions in the Earth-centered inertial coordinate system respectively; 、 、 They represent the variance of the random positioning error of the spaceborne lidar in the X, Y, and Z directions in the Earth-centered inertial coordinate system respectively; represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Y direction of the spaceborne lidar in the Earth-centered inertial coordinate system; represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Z direction of the spaceborne lidar in the Earth-centered inertial coordinate system; represents the cross-covariance between the random positioning error in the Y direction and the random positioning error in the Z direction of the spaceborne lidar in the Earth-centered inertial coordinate system; Represents the random error of satellite position measurement; Represents the random error of satellite attitude measurement; Represents the random error of laser pointing measurement; Indicates the random error of laser distance measurement; A Jacobian matrix representing position information of a target object measured by a spaceborne laser radar with respect to multiple random errors; the multiple random errors include satellite position measurement random error, satellite attitude measurement random error, laser pointing measurement random error, and laser distance measurement random error;

[0101] Based on the covariance matrix of the positioning random error to be converted, the covariance matrix of the positioning random error is determined. The covariance matrix of the positioning random error is the covariance matrix of the positioning random error in the coordinate system of the reference height grid data used for geometric parameter calibration converted from the covariance matrix of the positioning random error to be converted, as shown in formula (2):

[0102] (2)

[0103] in, 、 、 They represent the random positioning errors of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data. 、 、 They represent the variance of the random positioning error of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data; It represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Y direction of the spaceborne lidar in the coordinate system of the reference elevation grid data. It represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Z direction of the spaceborne lidar in the coordinate system of the reference elevation grid data. It represents the cross-covariance between the random positioning error in the Y direction and the random positioning error in the Z direction of the spaceborne lidar in the coordinate system of the reference elevation grid data; Represents the rotation matrix from the geocentric inertial coordinate system to the coordinate system of the reference elevation grid data;

[0104] The variance of the random positioning error of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data; the cross-covariance between the random positioning error of the spaceborne lidar in the X direction and the random positioning error of the Y direction in the coordinate system of the reference elevation grid data; the cross-covariance between the random positioning error of the spaceborne lidar in the X direction and the random positioning error of the Z direction in the coordinate system of the reference elevation grid data; and the cross-covariance between the random positioning error of the spaceborne lidar in the Y direction and the random positioning error of the Z direction in the coordinate system of the reference elevation grid data are used as statistical characteristics of the random positioning error.

[0105] In a specific embodiment, the satellite position measurement random error represents the position vector of the satellite in the Earth-Centered Inertial coordinate system (ECI) The error is shown as follows:

[0106] ;

[0107] in, Represents the random error of satellite position measurement; 、 、 They represent the standard deviation or root mean square error of the satellite's position information in the X, Y, and Z directions in the Earth-centered inertial coordinate system, respectively, and can be calculated from the satellite position measurement subsystem parameters carried by the satellite.

[0108] Satellite attitude measurement random error represents satellite attitude rotation matrix That is, the error of the rotation matrix converted from the satellite body coordinate system (Spacecraft Body Fixed System, SBF) to the Earth-centered inertial coordinate system is shown in the following formula:

[0109] ;

[0110] in, Represents the random error of satellite attitude measurement; 、 、 They represent the standard deviation or root mean square error of the rotation angles (i.e., roll angle, pitch angle, and yaw angle) around the X-axis, Y-axis, and Z-axis in the satellite's body coordinate system, respectively, and can be calculated from the satellite attitude measurement subsystem parameters carried by the satellite.

[0111] The random error of laser pointing measurement represents the laser beam pointing vector in the satellite body coordinate system. The error is shown as follows:

[0112] ;

[0113] in, Represents the random error of laser pointing measurement; Nadir angle The standard deviation or root mean square error of the angular quantity, Indicates azimuth The standard deviation or root mean square error of the angular quantity can be calculated from the parameters of the laser pointing measurement subsystem carried by the satellite.

[0114] The random error of laser distance measurement refers to the error in the distance measurement value between the satellite-borne laser radar and the target object, that is, the timing error of the pulse flight time during laser pulse ranging, including the ranging uncertainty caused by the laser pulse width, the atmospheric delay correction error, the time jitter of the timing circuit, and the ranging disturbance caused by the laser pointing disturbance. The ranging random errors caused by the above factors are independent of each other. The random error of laser distance measurement determined by this is expressed as follows:

[0115] ;

[0116] in, Indicates the random error of laser distance measurement; It represents the random error of ranging caused by the ranging uncertainty caused by the laser pulse width, represents the random error in ranging caused by the atmospheric delay correction error, Indicates the random error in ranging caused by the time jitter of the timing circuit, It represents the random error of ranging caused by the ranging disturbance brought by the laser pointing disturbance.

[0117] In a specific embodiment, the positioning model of the spaceborne laser radar can be expressed as follows:

[0118] ;

[0119] in, Represents the position vector of the target object in the geocentric inertial coordinate system; represents the position vector of the satellite in the Earth-centered inertial coordinate system; l represents the distance value from the satellite to the target object; Represents the rotation matrix from the satellite body coordinate system to the Earth-centered inertial coordinate system, which can be determined by the satellite attitude; The unit vector that represents the direction of the laser beam in the satellite body coordinate system.

[0120] Alternatively, a spaceborne lidar is a lidar system that uses a satellite as its carrier platform. The satellite serves as the carrier platform for the spaceborne lidar, so the distance value from the satellite to the target object can be the distance value from the spaceborne lidar to the target object. The target object can be the object observed by the spaceborne lidar, specifically, the Earth's surface, a ground target, the atmosphere, etc.

[0121] In a specific embodiment, based on the positioning model of the satellite-borne laser radar, as well as the satellite position measurement random error, satellite attitude measurement random error, laser pointing measurement random error and laser distance measurement random error, combined with the covariance propagation law, the covariance matrix of the positioning random error in the geocentric inertial coordinate system can be determined. Optionally, Figure 2 : is a schematic diagram of a positioning model for measuring a target object by a space-borne laser radar according to an embodiment of the present invention; Figure 2 As shown, it can be determined that the random error of the positioning to be converted can be determined by the random errors from four sources: satellite position measurement, satellite attitude measurement, laser pointing measurement, and laser distance measurement. That is, the random error of the positioning to be converted can be determined by the random error of satellite position measurement, the random error of satellite attitude measurement, the random error of laser pointing measurement, and the random error of laser distance measurement; the covariance propagation law can be used to quantify the influence of the random error of satellite position measurement, the random error of satellite attitude measurement, the random error of laser pointing measurement, and the random error of laser distance measurement on the random error of the positioning to be converted, and the covariance matrix of the random error of the positioning to be converted is determined, so as to determine the statistical characteristics of the random error of the positioning to be converted, and then the positioning accuracy can be evaluated.

[0122] In a specific embodiment, the Jacobian matrix of the position information of the target object measured by the satellite-borne laser radar with respect to the random error of satellite position measurement, the random error of satellite attitude measurement, the random error of laser pointing measurement, and the random error of laser distance measurement is specifically shown as follows:

[0123] ;

[0124] in, Represents the position vector of the target object in the geocentric inertial coordinate system; Represents the position vector of the satellite in the Earth-centered inertial coordinate system; Indicates the roll angle of the satellite in the satellite body coordinate system; Indicates the pitch angle of the satellite in the satellite body coordinate system; Indicates the yaw angle of the satellite in the satellite body coordinate system; represents the nadir angle; Indicates azimuth; Indicates the distance value between the satellite-borne laser radar and the target object.

[0125] In a specific embodiment, the reference elevation raster data can be a data format that divides geographic space into regular grid cells, and each grid cell can include an attribute value corresponding to the cell to represent a geographic entity. Specifically, each grid cell can represent a specific area on the target observation object, and the attribute value corresponding to each grid cell can be elevation. Optionally, the reference elevation raster data can be a digital elevation model or a digital surface model. The digital elevation model uses raster data to represent terrain elevation information, and the digital surface model uses raster data to represent surface elevation information. Specifically, the reference elevation raster data can be determined in conjunction with the actual implementation site.

[0126] The covariance matrix of the random positioning error can be the covariance matrix of the random positioning error in the geocentric inertial coordinate system converted to the covariance matrix of the random positioning error in the coordinate system of the reference elevation grid data used for geometric parameter calibration. Optionally, the reference elevation grid data can be benchmark data used in the terrain matching method for geometric parameter calibration of spaceborne lidar. The reference elevation grid data can be stored in a regular grid of rows and columns, with each grid cell corresponding to an elevation. Optionally, the reference elevation grid data can be a digital elevation model or a digital surface model, which can be used to describe the overall characteristics of the terrain. Optionally, the terrain reference data can be the reference elevation grid data, which can be used to provide surface elevation information and describe the relief characteristics of the terrain. Because the reference elevation grid data has a regular grid structure and easily accessible elevations, the terrain matching method for geometric parameter calibration of spaceborne lidar is used to match and compare the spaceborne lidar measurement data with the reference elevation grid data.

[0127] S102: Determine statistical characteristics of terrain gradients in reference elevation raster data;

[0128] In a specific embodiment, the LiDAR measurement data may be data that has undergone geometric parameter calibration using a terrain matching method. Terrain gradient is a parameter that can be used to describe the steepness of terrain undulations. Specifically, terrain gradient can refer to the rate of change of terrain elevation. Terrain gradient affects positioning accuracy, so the positioning parameter calibration process includes correction for errors caused by terrain gradient.

[0129] In an optional embodiment, determining statistical characteristics of terrain gradients in reference elevation grid data includes:

[0130] Acquire multiple laser radar measurement data that have been calibrated with geometric parameters;

[0131] Perform coordinate system conversion on the geographic coordinate information corresponding to the multiple lidar measurement data to obtain multiple plane coordinate information in the coordinate system of the reference elevation grid data;

[0132] Determining a first gradient dataset and a second gradient dataset based on a plurality of plane coordinate information in a coordinate system where the reference elevation raster data is located;

[0133] Based on the first gradient data set and the second gradient data set, the first-order moment, second-order moment, third-order moment, and fourth-order moment corresponding to the first terrain gradient expected data, and the first-order moment, second-order moment, third-order moment, and fourth-order moment corresponding to the second terrain gradient expected data are determined, as shown in Equations (3) and (4):

[0134] (3)

[0135] (4)

[0136] in, and They represent the first expected terrain gradient data and the second expected terrain gradient data corresponding to the i-th laser radar measurement data, respectively. The first expected terrain gradient data is the expected terrain gradient data of the terrain gradient in the X direction corresponding to the i-th laser radar measurement data, and the second expected terrain gradient data is the expected terrain gradient data of the terrain gradient in the Y direction corresponding to the i-th laser radar measurement data; and The n-th order moments representing the first terrain gradient expected data and the second terrain gradient expected data respectively; and Represent the first gradient data set respectively and the second gradient dataset The number of elements in the set, N represents the number of laser radar measurement data that have been calibrated with geometric parameters;

[0137] Determine the standard deviation of the first component and the standard deviation of the second component of the terrain gradient, as shown in Equations (5) and (6):

[0138] ; (5)

[0139] ; (6)

[0140] in, represents the first component standard deviation, represents the standard deviation of the second component; and Respectively represent the grid intervals of the reference elevation grid data in the X and Y directions; Indicates the elevation standard deviation of the reference elevation raster data;

[0141] Based on the first-order moment, second-order moment, third-order moment and fourth-order moment corresponding to the expected data of the first terrain gradient, the first-order moment, second-order moment, third-order moment and fourth-order moment corresponding to the expected data of the second terrain gradient, the standard deviation of the first component and the standard deviation of the second component, the statistical characteristics of the terrain gradient are determined. The statistical characteristics of the terrain gradient include the first-order moment, second-order moment, third-order moment and fourth-order moment of the first terrain gradient, and the first-order moment, second-order moment, third-order moment and fourth-order moment of the second terrain gradient, as shown in Equations (7) and (8):

[0142] (7)

[0143] (8)

[0144] in, represents the first terrain gradient, represents the second terrain gradient, the first terrain gradient is the gradient component of the terrain gradient corresponding to the i-th lidar measurement data in the X direction, and the second terrain gradient is the gradient component of the terrain gradient corresponding to the i-th lidar measurement data in the Y direction; represents the first moment of the first terrain gradient, represents the second moment of the first terrain gradient, represents the third moment of the first terrain gradient, represents the fourth moment of the first topographic gradient; represents the first moment of the second terrain gradient, represents the second moment of the second terrain gradient, represents the third moment of the second terrain gradient, Represents the fourth moment of the second topographic gradient.

[0145] In an optional embodiment, the determining of the first gradient dataset and the second gradient dataset based on the plurality of plane coordinate information in the coordinate system of the reference elevation grid data includes:

[0146] Based on the multiple plane coordinate information in the coordinate system of the reference elevation grid data, the index data set of multiple grid points within the preset range of each lidar measurement data in the reference elevation grid data is searched, as shown in formula (20):

[0147] (20)

[0148] in, Represents a set of index data of multiple grid points within a preset range of the i-th laser radar measurement data in the reference elevation grid data; the plane coordinate information includes the first coordinate information and the second coordinate information, and Respectively represent the first coordinate information and the second coordinate information corresponding to the i-th laser radar measurement data in the reference elevation raster data; and Respectively represent the first coordinate information and the second coordinate information corresponding to the grid point with index data (m, n) in the reference elevation raster data, where (m, n) represents the mth row and the nth column; r is the preset search radius corresponding to the index data set of multiple grid points in the preset search range;

[0149] Based on the index data set, the first gradient data set and the second gradient data set corresponding to each lidar measurement data are determined by combining the gradient component of the terrain gradient in the X direction and the gradient component in the Y direction corresponding to each index data, as shown in Equations (21) and (22):

[0150] (twenty one)

[0151] (twenty two)

[0152] in, represents the first gradient data set, represents the second gradient dataset. The first gradient dataset is the gradient component set of the terrain gradient corresponding to the i-th lidar measurement data in the X direction. The second gradient dataset is the gradient component set of the terrain gradient corresponding to the i-th lidar measurement data in the Y direction. and Respectively represent the gradient components of the terrain gradient in the X direction and the Y direction at the grid point with index data (m, n);

[0153] The gradient component of the terrain gradient in the X direction and the gradient component in the Y direction at the grid point corresponding to each index data are determined based on equations (23) and (24):

[0154] (twenty three)

[0155] (twenty four)

[0156] in, Indicates that the plane coordinate information corresponding to the grid point with index data (m,n) in the reference elevation grid data is The reference elevation at and Respectively represent the grid intervals in the X and Y directions of the reference elevation grid data.

[0157] In a specific embodiment, the first gradient data set corresponding to each lidar measurement data may be a set of gradient components of the terrain gradient corresponding to each lidar measurement data in the X direction. Specifically, the first gradient data set corresponding to each lidar measurement data may include the gradient components of the terrain gradient in the X direction corresponding to multiple grid points within a preset range of the lidar measurement data in the reference elevation raster data; the second gradient data set corresponding to each lidar measurement data may be a set of gradient components of the terrain gradient corresponding to each lidar measurement data in the Y direction. Specifically, the second gradient data set corresponding to each lidar measurement data may include the gradient components of the terrain gradient in the Y direction corresponding to multiple grid points within a preset range of the lidar measurement data in the reference elevation raster data.

[0158] In a specific embodiment, the geographic coordinate information corresponding to the multiple laser radar measurement data may include the longitude and latitude corresponding to the multiple laser radar measurement data. The longitude and latitude of each laser radar measurement data may be subjected to a coordinate system conversion to obtain plane coordinate information corresponding to the coordinate system of the reference elevation raster data. The plane coordinate information includes first coordinate information and second coordinate information. Optionally, the first coordinate information may be the horizontal coordinate information of the laser radar measurement data in the coordinate system of the reference elevation raster data, and the second coordinate information may be the vertical coordinate information of the laser radar measurement data in the coordinate system of the reference elevation raster data.

[0159] In a specific embodiment, the preset range can be a range determined by taking the position of the lidar measurement data in the reference elevation grid data as the origin and the preset search radius as the radius; the index data set of multiple grid points in the preset range can include the index data of multiple grid points in the preset range. Optionally, the index data of the grid point can be data for locating the grid point in the reference elevation grid data, specifically, the index data of the grid point can be the row number and column number of the grid point. Optionally, the preset search radius can be a radius determined when searching the index data set of multiple grid points in the preset range; specifically, the preset search radius can be set in combination with actual application requirements. Optionally, The plane coordinate information may be the plane coordinate information of the laser radar measurement data at the (m, n) grid point within a preset range near the plane coordinate information in the reference elevation grid data.

[0160] In a specific embodiment, the first terrain gradient can be the gradient component of the terrain gradient in the X direction corresponding to the plane coordinate information of each lidar measurement data in the reference elevation grid data; the second terrain gradient can be the gradient component of the terrain gradient in the Y direction corresponding to the plane coordinate information of each lidar measurement data in the reference elevation grid data; the plane coordinate information of each lidar measurement data in the reference elevation grid data can include first coordinate information and second coordinate information. Optionally, the first gradient dataset and the second gradient dataset are data determined based on actual statistics of the lidar measurement data; the first terrain gradient and the second terrain gradient are real values ​​set in the process of determining the statistical characteristics of the terrain gradient, and can be determined based on the first gradient dataset and the second gradient dataset.

[0161] Optionally, the first terrain gradient can be expressed as , the second terrain gradient can be expressed as , and It can be a random variable that obeys the normal distribution, that is, and ,in, It can represent the first terrain gradient expected data, can represent the second terrain gradient expected data, It can represent the first component standard deviation, which can be the standard deviation of the terrain gradient in the X direction corresponding to the i-th lidar measurement data. It can represent the second component standard deviation, which can be the terrain gradient standard deviation in the Y direction of the terrain gradient corresponding to the i-th lidar measurement data. Optionally, the first component standard deviation and the second component standard deviation are related to the grid resolution (i.e., grid interval) and elevation accuracy of the reference elevation grid data.

[0162] In the above embodiment, by utilizing the statistical characteristics of positioning random errors and the statistical characteristics of terrain gradients, the calibration accuracy of the satellite-borne lidar positioning parameters can be estimated, thereby improving the effectiveness and accuracy of the estimation of the calibration accuracy of the satellite-borne lidar positioning parameters, and further improving the effectiveness and accuracy of the estimation of the calibration accuracy of the satellite-borne lidar geometric parameters.

[0163] S103: Inputting the statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient into the positioning parameter calibration accuracy estimation model to obtain the positioning parameter calibration accuracy estimation result;

[0164] In a specific embodiment, the positioning parameter calibration accuracy estimation model may be a model for performing positioning parameter calibration accuracy estimation.

[0165] In an optional embodiment, the positioning parameter calibration accuracy estimation model can be constructed in the following manner:

[0166] The positioning parameter calibration accuracy estimation model is shown in formula (9):

[0167] (9)

[0168] in, 、 、 Respectively represent the uncertainty of the positioning parameter calibration results in the X direction, Y direction and Z direction, and are used to characterize the positioning parameter calibration accuracy in the X direction, Y direction and Z direction respectively; It represents the cross-covariance between the calibration accuracy of the positioning parameters in the X direction and the Y direction of the spaceborne lidar in the geocentric inertial coordinate system. It represents the cross-covariance between the calibration accuracy of the positioning parameters in the X direction and the Z direction of the spaceborne lidar in the geocentric inertial coordinate system. represents the cross-covariance between the Y-direction positioning parameter calibration accuracy and the Z-direction positioning parameter calibration accuracy of the spaceborne lidar in the geocentric inertial coordinate system; the matrix The Jacobian matrix representing the positioning calibration results of the elevation data corresponding to the lidar measurement data in the X, Y, and Z directions. Represents the random error of elevation of N lidar measurement data.

[0169] In a specific embodiment, ;

[0170] in, Indicates the gradient component of the terrain gradient in the X direction corresponding to the plane coordinate information of N lidar measurement data in the reference elevation grid data, Represents the gradient component in the Y direction of the terrain gradient corresponding to the plane coordinate information of N laser radar measurement data in the reference elevation grid data.

[0171] In a specific embodiment, the calibration accuracy estimation result of the positioning parameters may be a covariance matrix of the positioning parameter calibration accuracy, which may specifically include the positioning parameter calibration accuracy variance of the satellite-borne laser radar in the X direction, the positioning parameter calibration accuracy variance of the satellite-borne laser radar in the Y direction, the cross-covariance between the positioning parameter calibration accuracy of the satellite-borne laser radar in the X direction and the positioning parameter calibration accuracy of the satellite-borne laser radar in the Y direction in the geocentric inertial coordinate system, the cross-covariance between the positioning parameter calibration accuracy of the satellite-borne laser radar in the X direction and the positioning parameter calibration accuracy of the satellite-borne laser radar in the Z direction in the geocentric inertial coordinate system, and the cross-covariance between the positioning parameter calibration accuracy of the satellite-borne laser radar in the Y direction and the positioning parameter calibration accuracy of the satellite-borne laser radar in the geocentric inertial coordinate system.

[0172] In a specific embodiment, the uncertainty of the satellite-borne laser radar positioning parameter calibration result can be obtained by using the positioning parameter calibration accuracy estimation result determined by the positioning parameter calibration accuracy estimation model. The uncertainty of the positioning parameter calibration result represents the positioning parameter calibration accuracy and can be used to measure the reliability of the positioning parameter calibration result. Optionally, when the uncertainty of the positioning parameter calibration result is large, the positioning parameter calibration accuracy is low, and the positioning parameter calibration result is less reliable. When the uncertainty of the positioning parameter calibration result is small, the positioning parameter calibration accuracy is high, and the positioning parameter calibration result is more reliable.

[0173] In an optional embodiment, the statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient are input into the positioning parameter calibration accuracy estimation model to obtain the positioning parameter calibration accuracy estimation result, including:

[0174] Positioning parameter calibration accuracy estimation model and , as shown in formula (10) and formula (11):

[0175] (10)

[0176] (11)

[0177] in, represents the random error of the elevation of the lidar measurement data, represents the first terrain gradient, represents the second terrain gradient;

[0178] Based on the relationship between the random error of elevation shown in equation (12) and the random error of positioning of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data, the elements in equations (10) and (11) are determined as shown in equations (13) to (18):

[0179] ; (12)

[0180] (13)

[0181] (14)

[0182] (15)

[0183] (16)

[0184] (17)

[0185] (18)

[0186] in, 、 、 They represent the random positioning errors of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data.

[0187] The statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient are input into the positioning parameter calibration accuracy estimation model for solution processing to obtain the positioning parameter calibration accuracy estimation result.

[0188] In a specific embodiment, Figure 3 is the calibration accuracy estimation result of the satellite-borne laser radar positioning parameter calibration provided by the embodiment of the present invention; Figure 3 As shown in the figure, the points represent the calibration results of the spaceborne lidar positioning parameters, namely the lidar measurement data from the spaceborne lidar that has been calibrated using terrain matching-based geometric parameters. The grayscale background image in the figure represents the root mean square error (RMS) of the elevation difference between the lidar measurement data and the reference elevation grid data under different positioning parameter calibration results, and the grayscale bar graph indicates the magnitude of the RMS error of the elevation difference. The ellipse in the figure represents the X and Y directions of the positioning parameter calibration accuracy, which is determined based on the variance and covariance in the X and Y directions of the positioning parameter calibration accuracy estimate, i.e., the covariance matrix of the positioning parameter calibration accuracy. The circular error probability can be analyzed based on the ellipse in the figure to determine the positioning parameter calibration accuracy.

[0189] S104: Inputting the calibration accuracy estimation result of the positioning parameters into the ranging and pointing parameter calibration accuracy estimation model to obtain the calibration accuracy estimation result of the ranging and pointing parameters.

[0190] In a specific embodiment, the ranging and pointing parameter calibration accuracy estimation model may be a model for performing ranging and pointing parameter calibration accuracy estimation.

[0191] In an optional embodiment, the laser pointing and ranging parameter calibration accuracy includes the pointing parameter calibration accuracy of the roll pointing angle, the pointing parameter calibration accuracy of the pitch pointing angle, and the ranging parameter calibration accuracy of the ranging value. The ranging and pointing parameter calibration accuracy estimation model can be constructed in the following manner:

[0192] The ranging and pointing parameter calibration accuracy estimation model is shown in formula (19):

[0193] (19)

[0194] in, 、 、 They represent the uncertainty of the pointing parameter calibration result on the roll pointing angle, the uncertainty of the pointing parameter calibration result on the pitch pointing angle, and the uncertainty of the ranging parameter calibration result on the ranging value, respectively. They are used to characterize the pointing parameter calibration accuracy on the roll pointing angle, the pointing parameter calibration accuracy on the pitch pointing angle, and the ranging parameter calibration accuracy on the ranging value; represents the cross-covariance between the calibration accuracy of the pointing parameters on the roll pointing angle and the calibration accuracy of the pointing parameters on the pitch pointing angle, represents the cross-covariance between the calibration accuracy of the pointing parameters on the roll pointing angle and the calibration accuracy of the ranging parameters on the ranging value, It represents the cross-covariance between the pointing parameter calibration accuracy on the pitch pointing angle and the ranging parameter calibration accuracy on the ranging value; 、 、 Respectively represent the uncertainty of the positioning parameter calibration results in the X direction, Y direction and Z direction, and are used to characterize the positioning parameter calibration accuracy in the X direction, Y direction and Z direction respectively; Represents the rotation matrix from the coordinate system of the reference elevation grid data to the geocentric inertial coordinate system; is the Jacobian matrix of the roll pointing angle r, pitch pointing angle p, and ranging value l regarding the position measurement information of the target object measured by the satellite-borne lidar.

[0195] In a specific embodiment, ;

[0196] Among them, r represents the roll pointing angle, p represents the pitch pointing angle, and l represents the ranging value. The position vector of the target object in the Earth-centered inertial coordinate system.

[0197] In a specific embodiment, the calibration accuracy estimation result of the ranging and pointing parameters can be the covariance matrix of the ranging and pointing parameter calibration accuracy, which can specifically include the pointing parameter calibration accuracy variance on the roll pointing angle of the satellite-borne laser radar, the pointing parameter calibration accuracy variance on the pitch pointing angle of the satellite-borne laser radar, the ranging parameter calibration accuracy variance on the ranging value of the satellite-borne laser radar, the cross-covariance between the pointing parameter calibration accuracy on the roll pointing angle of the satellite-borne laser radar and the pointing parameter calibration accuracy on the pitch pointing angle, the cross-covariance between the pointing parameter calibration accuracy on the roll pointing angle of the satellite-borne laser radar and the ranging parameter calibration accuracy on the ranging value, and the cross-covariance between the pointing parameter calibration accuracy on the pitch pointing angle of the satellite-borne laser radar and the ranging parameter calibration accuracy on the ranging value.

[0198] In a specific embodiment, the uncertainty of the ranging and pointing parameter calibration result of the satellite-borne lidar can be obtained by using the calibration accuracy estimation result of the ranging and pointing parameters determined by the ranging and pointing parameter calibration accuracy estimation model. The uncertainty of the ranging and pointing parameter calibration result represents the ranging and pointing parameter calibration accuracy and can be used to measure the reliability of the ranging and pointing parameter calibration result. Optionally, when the uncertainty of the ranging and pointing parameter calibration result is large, the ranging and pointing parameter calibration accuracy is low, and the ranging and pointing parameter calibration result is less reliable. When the uncertainty of the ranging and pointing parameter calibration result is small, the ranging and pointing parameter calibration accuracy is high, and the ranging and pointing parameter calibration result is more reliable.

[0199] In the above embodiment, the uncertainty of the positioning parameter calibration result and the uncertainty of the ranging and pointing parameter calibration result can be obtained through the calibration accuracy estimation results of the positioning parameters and the calibration accuracy estimation results of the ranging and pointing parameters, and then the positioning parameter calibration accuracy and the ranging and pointing parameter calibration accuracy can be determined, and the reliability of the positioning parameter calibration result and the reliability of the ranging and pointing parameter calibration result can be measured, thereby realizing the estimation of the geometric parameter calibration accuracy of the satellite-borne laser radar, and improving the accuracy, effectiveness and efficiency of the geometric parameter calibration accuracy estimation of the satellite-borne laser radar, and ensuring that the laser radar measurement data of the applied satellite-borne laser radar has high reliability. Moreover, the uncertainty of the positioning parameter calibration result and the uncertainty of the ranging and pointing parameter calibration result provide quantitative performance evaluation indicators, which can help designers and researchers accurately understand the geometric parameter calibration accuracy of the satellite-borne laser radar, further analyze the source of uncertainty, that is, determine the part of the satellite-borne laser radar system with larger errors, provide a basis for the optimization design of the system, and then improve this part in a targeted manner to improve the overall performance of the system.

[0200] The embodiment of the present invention also provides a device for estimating the geometric parameter calibration accuracy of a space-borne laser radar. Figure 4 : is a schematic diagram of the structure of a device for calibrating and estimating geometric parameters of a space-borne laser radar according to an embodiment of the present invention; the geometric parameters include positioning parameters and ranging and pointing parameters, such as Figure 4 As shown, the above device includes:

[0201] The module 410 for determining the statistical characteristics of the random positioning errors is used to determine the statistical characteristics of the random positioning errors corresponding to the spaceborne laser radar.

[0202] A terrain gradient statistical characteristic determination module 420 is used to determine the statistical characteristics of the terrain gradient in the reference elevation grid data;

[0203] A positioning parameter calibration accuracy estimation module 430 is configured to input the statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient into a positioning parameter calibration accuracy estimation model to obtain a positioning parameter calibration accuracy estimation result;

[0204] The ranging and pointing parameter calibration accuracy estimation module 440 is configured to input the positioning parameter calibration accuracy estimation result into the ranging and pointing parameter calibration accuracy estimation model to obtain the ranging and pointing parameter calibration accuracy estimation result.

[0205] An embodiment of the present invention also provides an electronic device, comprising: a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method for estimating the geometric parameter calibration accuracy of a satellite-borne laser radar as described in any one of the method embodiments.

[0206] An embodiment of the present invention also provides a computer storage medium, which can be set in a server to store at least one instruction, at least one program, code set or instruction set for implementing the method embodiment. The at least one instruction, the at least one program, the code set or instruction set is loaded and executed by the processor to implement the satellite-borne laser radar geometric parameter calibration accuracy estimation method as described in any one of the method embodiments.

[0207] Optionally, in an embodiment of the present invention, the storage medium may be located in at least one of a plurality of network servers in a computer network. Optionally, in an embodiment of the present invention, the storage medium may include, but is not limited to, a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard drive, a magnetic disk, or an optical disk, among other media capable of storing program code.

[0208] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0209] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple flow charts and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0210] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple flow charts and / or boxes Figure 1 The function specified in one or more boxes.

[0211] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple flow charts and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0212] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the function or action of the specification, or can be implemented by a combination of dedicated hardware and computer instructions.

[0213] Finally, it should be noted that the embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A method for estimating the geometric parameter calibration accuracy of a spaceborne laser radar, characterized in that: The geometric parameters include positioning parameters and ranging and pointing parameters, and the method includes: Determine the statistical characteristics of the random positioning errors corresponding to the spaceborne lidar; Determine the statistical characteristics of terrain gradients in reference elevation raster data; Inputting the statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient into a positioning parameter calibration accuracy estimation model to obtain a calibration accuracy estimation result of the positioning parameter; The calibration accuracy estimation result of the positioning parameter is input into the ranging and pointing parameter calibration accuracy estimation model to obtain the calibration accuracy estimation result of the ranging and pointing parameter.

2. The method for estimating the geometric parameter calibration accuracy of a space-borne laser radar according to claim 1, wherein: Determining the statistical characteristics of the random positioning error corresponding to the spaceborne laser radar includes: Determine the satellite position measurement random error, satellite attitude measurement random error, laser pointing measurement random error and laser distance measurement random error respectively; Based on the positioning model of the satellite-borne laser radar, as well as the satellite position measurement random error, the satellite attitude measurement random error, the laser pointing measurement random error, and the laser distance measurement random error, the covariance matrix of the positioning random error to be converted is determined; the covariance matrix of the positioning random error to be converted is the covariance matrix of the positioning random error in the geocentric inertial coordinate system, as shown in formula (1): （1） in, 、 、 They represent the random positioning errors of the satellite-borne laser radar in the X, Y and Z directions in the Earth-centered inertial coordinate system respectively; 、 、 They represent the variance of the random positioning error of the spaceborne lidar in the X, Y, and Z directions in the Earth-centered inertial coordinate system respectively; represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Y direction of the spaceborne lidar in the Earth-centered inertial coordinate system; represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Z direction of the spaceborne lidar in the Earth-centered inertial coordinate system; represents the cross-covariance between the random positioning error in the Y direction and the random positioning error in the Z direction of the spaceborne lidar in the Earth-centered inertial coordinate system; Represents the random error of satellite position measurement; Represents the random error of satellite attitude measurement; Represents the random error of laser pointing measurement; Indicates the random error of laser distance measurement; A Jacobian matrix representing position information of a target object measured by a satellite-borne laser radar with respect to multiple random errors; the multiple random errors include the satellite position measurement random error, the satellite attitude measurement random error, the laser pointing measurement random error, and the laser distance measurement random error; Based on the covariance matrix of the positioning random error to be converted, the covariance matrix of the positioning random error is determined. The covariance matrix of the positioning random error is the covariance matrix of the positioning random error in the coordinate system of the reference elevation grid data used for geometric parameter calibration converted from the covariance matrix of the positioning random error to be converted, as shown in formula (2): （2） in, 、 、 They represent the random positioning errors of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data. 、 、 They represent the variance of the random positioning error of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data; It represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Y direction of the spaceborne lidar in the coordinate system of the reference elevation grid data. It represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Z direction of the spaceborne lidar in the coordinate system of the reference elevation grid data. It represents the cross-covariance between the random positioning error in the Y direction and the random positioning error in the Z direction of the spaceborne lidar in the coordinate system of the reference elevation grid data; Represents the rotation matrix from the geocentric inertial coordinate system to the coordinate system of the reference elevation grid data; The variance of the random positioning errors of the satellite-borne laser radar in the X direction, Y direction, and Z direction in the coordinate system where the reference elevation grid data is located; the cross-covariance between the random positioning errors of the satellite-borne laser radar in the X direction and the random positioning errors in the Y direction in the coordinate system where the reference elevation grid data is located, the cross-covariance between the random positioning errors of the satellite-borne laser radar in the X direction and the random positioning errors in the Z direction in the coordinate system where the reference elevation grid data is located, and the cross-covariance between the random positioning errors of the satellite-borne laser radar in the Y direction and the random positioning errors in the Z direction in the coordinate system where the reference elevation grid data is located are used as statistical features of the random positioning errors.

3. The method for estimating the geometric parameter calibration accuracy of a space-borne laser radar according to claim 1, wherein: Determining the statistical characteristics of terrain gradients in the reference elevation grid data includes: Acquire multiple laser radar measurement data that have been calibrated with geometric parameters; Performing coordinate system conversion on geographic coordinate information corresponding to the plurality of laser radar measurement data to obtain a plurality of plane coordinate information in the coordinate system of the reference elevation raster data; Determining a first gradient data set and a second gradient data set based on a plurality of plane coordinate information in a coordinate system where the reference elevation raster data is located; Based on the first gradient data set and the second gradient data set, the first-order moment, second-order moment, third-order moment, and fourth-order moment corresponding to the first terrain gradient expected data, and the first-order moment, second-order moment, third-order moment, and fourth-order moment corresponding to the second terrain gradient expected data are determined, as shown in equations (3) and (4): （3） （4） in, and They respectively represent the first expected terrain gradient data and the second expected terrain gradient data corresponding to the i-th laser radar measurement data, where the first expected terrain gradient data is the expected terrain gradient data in the X direction corresponding to the i-th laser radar measurement data, and the second expected terrain gradient data is the expected terrain gradient data in the Y direction corresponding to the i-th laser radar measurement data; and The n-th order moments representing the first terrain gradient expected data and the second terrain gradient expected data respectively; and Respectively represent the first gradient data set and the second gradient dataset The number of elements in the set, N represents the number of laser radar measurement data that have been calibrated with geometric parameters; The first component standard deviation and the second component standard deviation of the terrain gradient are determined as shown in equations (5) and (6): （5） （6） in, represents the first component standard deviation, represents the standard deviation of the second component; ∆x and ∆y represent the grid intervals of the reference elevation grid data in the X and Y directions, respectively; Indicates the elevation standard deviation of the reference elevation raster data; Based on the first-order moment, second-order moment, third-order moment, and fourth-order moment corresponding to the first terrain gradient expected data, the first-order moment, second-order moment, third-order moment, and fourth-order moment corresponding to the second terrain gradient expected data, the first component standard deviation, and the second component standard deviation, the statistical characteristics of the terrain gradient are determined. The statistical characteristics of the terrain gradient include the first-order moment, second-order moment, third-order moment, and fourth-order moment of the first terrain gradient, and the first-order moment, second-order moment, third-order moment, and fourth-order moment of the second terrain gradient, as shown in equations (7) and (8): （7） （8） in, represents the first terrain gradient, represents the second terrain gradient, where the first terrain gradient is the gradient component of the terrain gradient corresponding to the i-th lidar measurement data in the X direction, and the second terrain gradient is the gradient component of the terrain gradient corresponding to the i-th lidar measurement data in the Y direction; represents the first moment of the first terrain gradient, represents the second moment of the first terrain gradient, represents the third moment of the first terrain gradient, represents the fourth moment of the first topographic gradient; represents the first moment of the second terrain gradient, represents the second moment of the second terrain gradient, represents the third moment of the second terrain gradient, The fourth moment representing the second topographic gradient .

4. The method for estimating the geometric parameter calibration accuracy of a space-borne laser radar according to claim 1, wherein: The positioning parameter calibration accuracy estimation model is constructed in the following way: The positioning parameter calibration accuracy estimation model is shown in formula (9): （9） in, 、 、 Respectively represent the uncertainty of the positioning parameter calibration results in the X direction, Y direction and Z direction, and are used to characterize the positioning parameter calibration accuracy in the X direction, Y direction and Z direction respectively; It represents the cross-covariance between the calibration accuracy of the positioning parameters in the X direction and the Y direction of the spaceborne lidar in the geocentric inertial coordinate system. It represents the cross-covariance between the calibration accuracy of the positioning parameters in the X direction and the Z direction of the spaceborne lidar in the geocentric inertial coordinate system. represents the cross-covariance between the Y-direction positioning parameter calibration accuracy and the Z-direction positioning parameter calibration accuracy of the spaceborne lidar in the geocentric inertial coordinate system; the matrix The Jacobian matrix representing the positioning calibration results of the elevation data corresponding to the lidar measurement data in the X, Y, and Z directions. Represents the random error of elevation of N lidar measurement data.

5. The method for estimating the geometric parameter calibration accuracy of a space-borne laser radar according to claim 4, characterized in that: The step of inputting the statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient into a positioning parameter calibration accuracy estimation model to obtain the positioning parameter calibration accuracy estimation result includes: In the positioning parameter calibration accuracy estimation model and , as shown in formula (10) and formula (11): （10） （11） in, represents the random error of the elevation of the lidar measurement data, represents the first terrain gradient, represents the second terrain gradient; Based on the relationship between the random error of elevation shown in equation (12) and the random error of positioning of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data, the elements in equations (10) and (11) are determined as shown in equations (13) to (18): （12） （13） （14） （15） （16） （17） （18） in, 、 、 They represent the random positioning errors of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data. represents the first moment of the first terrain gradient, represents the second moment of the first terrain gradient, represents the third moment of the first terrain gradient, represents the fourth moment of the first topographic gradient; represents the first moment of the second terrain gradient, represents the second moment of the second terrain gradient, represents the third moment of the second terrain gradient, represents the fourth moment of the second topographic gradient; 、 、 They represent the variance of the random positioning error of the spaceborne lidar in the X, Y, and Z directions in the coordinate system of the reference elevation grid data; It represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Y direction of the spaceborne lidar in the coordinate system of the reference elevation grid data. It represents the cross-covariance between the random positioning error in the X direction and the random positioning error in the Z direction of the spaceborne lidar in the coordinate system of the reference elevation grid data. It represents the cross-covariance between the random positioning error in the Y direction and the random positioning error in the Z direction of the spaceborne lidar in the coordinate system of the reference elevation grid data; The statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient are input into a positioning parameter calibration accuracy estimation model for solution processing to obtain a calibration accuracy estimation result of the positioning parameter.

6. The method for estimating the geometric parameter calibration accuracy of a space-borne laser radar according to claim 1, wherein: The laser pointing and ranging parameter calibration accuracy includes the pointing parameter calibration accuracy on the roll pointing angle, the pointing parameter calibration accuracy on the pitch pointing angle, and the ranging parameter calibration accuracy on the ranging value. The ranging and pointing parameter calibration accuracy estimation model is constructed in the following manner: The ranging and pointing parameter calibration accuracy estimation model is shown in formula (19): （19） in, 、 、 They represent the uncertainty of the pointing parameter calibration result on the roll pointing angle, the uncertainty of the pointing parameter calibration result on the pitch pointing angle, and the uncertainty of the ranging parameter calibration result on the ranging value, respectively. They are used to characterize the pointing parameter calibration accuracy on the roll pointing angle, the pointing parameter calibration accuracy on the pitch pointing angle, and the ranging parameter calibration accuracy on the ranging value; represents the cross-covariance between the calibration accuracy of the pointing parameters on the roll pointing angle and the calibration accuracy of the pointing parameters on the pitch pointing angle, represents the cross-covariance between the calibration accuracy of the pointing parameters on the roll pointing angle and the calibration accuracy of the ranging parameters on the ranging value, It represents the cross-covariance between the pointing parameter calibration accuracy on the pitch pointing angle and the ranging parameter calibration accuracy on the ranging value; 、 、 Respectively represent the uncertainty of the positioning parameter calibration results in the X direction, Y direction and Z direction, and are used to characterize the positioning parameter calibration accuracy in the X direction, Y direction and Z direction respectively; It represents the cross-covariance between the calibration accuracy of the positioning parameters in the X direction and the Y direction of the spaceborne lidar in the geocentric inertial coordinate system. It represents the cross-covariance between the calibration accuracy of the positioning parameters in the X direction and the Z direction of the spaceborne lidar in the geocentric inertial coordinate system. It represents the cross-covariance between the calibration accuracy of the positioning parameters in the Y direction and the Z direction of the spaceborne lidar in the geocentric inertial coordinate system; Represents the rotation matrix from the coordinate system of the reference elevation grid data to the geocentric inertial coordinate system; is the Jacobian matrix of the roll pointing angle r, pitch pointing angle p, and ranging value l regarding the position measurement information of the target object measured by the satellite-borne lidar.

7. The method for estimating geometric parameter calibration accuracy of a space-borne laser radar according to claim 1, wherein: Determining a first gradient dataset and a second gradient dataset based on a plurality of plane coordinate information in a coordinate system where the reference elevation raster data resides includes: Based on the multiple plane coordinate information in the coordinate system of the reference elevation grid data, the index data set of multiple grid points in the preset range of each lidar measurement data in the reference elevation grid data is searched, as shown in formula (20): （20） in, A set of index data representing a plurality of grid points within a preset range of the reference elevation grid data for the i-th laser radar measurement data; the plane coordinate information includes first coordinate information and second coordinate information, and Respectively represent the first coordinate information and the second coordinate information corresponding to the i-th laser radar measurement data in the reference elevation raster data; and represents the first coordinate information and the second coordinate information corresponding to the grid point with index data (m, n) in the reference elevation raster data, where (m, n) represents the mth row and the nth column; r is the search radius corresponding to the index data set of multiple grid points in the preset range; Based on the index data set, the terrain gradient data set corresponding to each lidar measurement data is determined by combining the gradient component in the X direction and the gradient component in the Y direction of the terrain gradient corresponding to each index data, as shown in equations (21) and (22): （21） （22） in, represents the first gradient data set, represents the second gradient data set, the first gradient data set is the gradient component set of the terrain gradient corresponding to the i-th lidar measurement data in the X direction, and the second gradient data set is the gradient component set of the terrain gradient corresponding to the i-th lidar measurement data in the Y direction; and Respectively represent the gradient components of the terrain gradient in the X direction and the Y direction at the grid point with index data (m, n); The gradient component in the X direction and the gradient component in the Y direction of the terrain gradient corresponding to each index data are determined based on equations (23) and (24): （23） （24） in, Indicates that the plane coordinate information corresponding to the grid point with index data (m,n) in the reference elevation grid data is ∆x and ∆y represent the grid intervals in the X and Y directions of the reference elevation grid data, respectively.

8. A device for estimating the geometric parameter calibration accuracy of a space-borne laser radar, characterized in that: The geometric parameters include positioning parameters and ranging and pointing parameters, and the device includes: A module for determining the statistical characteristics of the random positioning errors is used to determine the statistical characteristics of the random positioning errors corresponding to the spaceborne laser radar; A module for determining the statistical characteristics of terrain gradients, used to determine the statistical characteristics of terrain gradients in reference elevation grid data; A positioning parameter calibration accuracy estimation module, configured to input the statistical characteristics of the positioning random error and the statistical characteristics of the terrain gradient into a positioning parameter calibration accuracy estimation model to obtain a positioning parameter calibration accuracy estimation result; The ranging and pointing parameter calibration accuracy estimation module is used to input the positioning parameter calibration accuracy estimation result into the ranging and pointing parameter calibration accuracy estimation model to obtain the ranging and pointing parameter calibration accuracy estimation result.

9. An electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction and at least one program, and the at least one instruction and the at least one program are loaded and executed by the processor to implement the method for estimating the geometric parameter calibration accuracy of a satellite-borne laser radar as described in any one of claims 1 to 7.

10. A computer storage medium, wherein at least one instruction and at least one program are stored in the computer storage medium, and the at least one instruction and the at least one program are loaded and executed by a processor to implement the method for estimating the geometric parameter calibration accuracy of a satellite-borne laser radar as described in any one of claims 1 to 7.

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