Satellite-borne laser radar pointing and ranging random error estimation method, device and equipment and storage medium

Abstract

The invention discloses a satellite-borne laser radar pointing and ranging random error estimation method, device and equipment and a storage medium, and belongs to the technical field of satellite-borne laser radars. The method comprises the following steps: determining a first coordinate information sequence, a second coordinate information sequence and a reference height program sequence of reference raster data according to the longitude, latitude and elevation of a plurality of obtained laser radar observation data; the reference raster data and the multiple pieces of laser radar observation data unify the vertical reference; according to the elevation, the reference height program sequence, the first coordinate information sequence and the second coordinate information sequence, determining statistical characteristics of plane and elevation random errors by utilizing a random error statistical characteristic resolving model; and inputting the statistical characteristics of the plane and elevation random errors and the spatial measurement and positioning data into a pointing and ranging random error estimation model to obtain the statistical characteristics of the pointing and ranging random errors. By using the technical scheme provided by the invention, the precision of estimating the pointing and ranging random error can be improved.

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 random errors in pointing and ranging of a satellite-borne laser radar. Background Art

[0002] Spaceborne lidar is an active remote sensing device based on pulsed laser ranging technology. It boasts a wide observation range, high measurement accuracy, and around-the-clock observation capabilities, making it crucial for applications such as topography, forestry surveys, and polar and ocean environmental monitoring. The core principle behind lidar's high-precision positioning capabilities in spaceborne, long-range detection scenarios is its ability to emit laser pulses at surface targets and measure the time it takes for the pulses to travel from the target's surface to the detector. This allows the distance between the lidar and the target to be calculated, combining the satellite's position and attitude information with the lidar's onboard position and beam pointing information to effectively determine the target's high-precision three-dimensional position.

[0003] However, spaceborne lidar is inevitably subject to interference from various factors during actual measurement, resulting in positioning errors. Positioning errors in spaceborne lidar can be primarily categorized as pointing error and ranging error. Pointing error refers to the deviation between the actual propagation direction of the lidar laser beam and the intended direction. This deviation can be caused by factors such as the lidar system's mounting structure, temperature fluctuations, vibrations, and refraction, scattering, and irregular disturbances in the propagation medium. Ranging error refers to inaccurate distance measurements caused by various environmental factors, system timing errors, and target characteristics.

[0004] In recent years, increasing research has focused on the geometric calibration and analysis of spaceborne lidars. Some methods have theoretically modeled and corrected pointing and ranging system errors, essentially eliminating these errors. However, random pointing and ranging errors still exist in spaceborne lidar data. These random errors are important indicators of laser payload system performance, and their estimation plays a crucial role in payload design. Therefore, a more reliable method for estimating these random errors is needed. 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 random errors of satellite-borne laser radar pointing and ranging, which can improve the accuracy and efficiency of estimating the random errors of satellite-borne laser radar pointing and ranging, and can effectively evaluate the positioning accuracy of satellite-borne laser radar 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 random errors in pointing and ranging of a spaceborne laser radar, the method comprising:

[0008] Obtaining a geolocation data sequence, reference grid data, and spatial measurement and positioning data corresponding to a spaceborne lidar; the geolocation data sequence includes the longitude, latitude, and elevation of a plurality of lidar observation data; the reference grid data includes a plurality of grid cells, each grid cell including a corresponding cell elevation; and the reference grid data and the plurality of lidar observation data share a unified vertical reference.

[0009] Determine, based on the longitude, latitude, and elevation of the plurality of laser radar observation data, a first coordinate information sequence, a second coordinate information sequence, and a reference elevation sequence corresponding to the reference raster data;

[0010] Determining statistical characteristics of planar and elevation random errors corresponding to the plurality of lidar observation data based on the elevations of the plurality of lidar observation data, a reference elevation sequence corresponding to the reference grid data, a first coordinate information sequence, and a second coordinate information sequence, and utilizing a random error statistical characteristic solution model;

[0011] The statistical characteristics of the plane and elevation random errors and the spatial measurement and positioning data are input into a pointing and ranging random error estimation model to obtain the statistical characteristics of the pointing and ranging random errors of the spaceborne laser radar.

[0012] In some possible implementations, determining statistical characteristics of planar and elevation random errors corresponding to the plurality of lidar observation data based on the elevations of the plurality of lidar observation data, the reference elevation sequence corresponding to the reference grid data, the first coordinate information sequence, and the second coordinate information sequence, and utilizing a random error statistical characteristic solution model, includes:

[0013] Determining the elevation residual corresponding to each lidar observation data according to the elevations of the plurality of lidar observation data and a reference elevation sequence corresponding to the reference grid data;

[0014] Determining first direction slope data and second direction slope data corresponding to each laser radar observation data based on the first coordinate information sequence and the second coordinate information sequence corresponding to the reference grid data;

[0015] The elevation residual, first direction slope data and second direction slope data corresponding to each laser radar observation data are input into the random error statistical feature solution model to obtain the statistical features of the plane and elevation random errors corresponding to the multiple laser radar observation data.

[0016] In some possible implementations, the statistical characteristics of the plane and elevation random errors corresponding to the multiple lidar observation data include statistical variances of the plane and elevation random errors corresponding to the multiple lidar observation data; the statistical variances of the plane and elevation random errors include statistical variances of random errors in the X direction, Y direction, and Z direction of the multiple lidar observation data;

[0017] The random error statistical characteristic solution model is constructed in the following way:

[0018] According to the first direction slope data and the second direction slope data corresponding to each lidar observation data, a plane and elevation random error model is established, as shown in formula (1):

[0019] ; (1)

[0020] in, represents the plane and elevation random errors corresponding to the i-th lidar observation data; the random variable δx represents the random error of multiple lidar observation data in the X direction; δy represents the random error of multiple lidar observation data in the Y direction, and the random variable δz represents the random error of multiple lidar observation data in the Z direction; and Respectively represent the first direction slope data and the second direction slope data corresponding to the i-th laser radar observation data;

[0021] Based on the plane and elevation random error model, combined with the elevation residual corresponding to each lidar observation data, the first direction slope data and the second direction slope data, a random error statistical characteristic solution model is constructed, as shown in formula (2):

[0022] ; (2)

[0023] in, represents the elevation residual corresponding to the i-th lidar observation data; Represents the statistical mean of the elevation residuals corresponding to multiple lidar observation data; Represents the statistical variance of random errors of multiple lidar observation data in the X direction, Represents the statistical variance of random errors of multiple lidar observation data in the Y direction, It represents the statistical variance of the random errors of multiple lidar observation data in the Z direction, and N represents the number of multiple lidar observation data.

[0024] In some possible implementations, the spatial measurement and positioning data includes a unit vector of the laser beam pointing in the satellite body coordinate system; the pointing and ranging random error estimation model is obtained based on the pointing and ranging random error model;

[0025] The pointing and ranging random error model is established in the following way:

[0026] Determine the distance value from the satellite-borne laser radar emission reference point to the target laser radar observation point;

[0027] Based on the ranging value and the unit vector of the laser beam pointing in the satellite body coordinate system, a positioning model for the satellite-borne laser radar to measure the target laser radar observation point is established, as shown in formula (3):

[0028] ; (3)

[0029] in, Represents the position vector of the target lidar observation point in the geocentric inertial coordinate system; represents the position vector of the satellite-borne laser radar emission reference point in the Earth-centered inertial coordinate system; l represents the distance value from the satellite-borne laser radar emission reference point to the target laser radar observation point; A unit vector representing the direction of the laser beam in the satellite body coordinate system; Represents the rotation matrix from the satellite body coordinate system to the geocentric inertial coordinate system;

[0030] Based on the positioning model, a positioning model with random errors in pointing and ranging is established, as shown in formula (4):

[0031] ; (4)

[0032] in, Indicates the position vector of the target lidar observation point with random errors in pointing and ranging in the Earth-centered inertial coordinate system; δ l Represents the random error of ranging; The resultant rotation matrix representing the random error in pointing;

[0033] Combining equations (3) and (4), the pointing and ranging random error model is established, as shown in equation (5):

[0034] ; (5)

[0035] in, represents the random positioning error of the target lidar observation point in the geocentric inertial coordinate system; is the identity matrix; represents the rotation matrix of the random error of the pointing angle including the roll and pitch directions, Indicates the random error of the pointing angle in the roll direction corresponding to multiple lidar observation data, Indicates the random error of the pointing angle in the pitch direction corresponding to multiple lidar observation data.

[0036] In some possible implementations, the spatial measurement and positioning data also includes the rotation matrix converted from the satellite body coordinate system to the geocentric inertial coordinate system, and the rotation matrix converted from the coordinate system where the reference grid data is located to the geocentric inertial coordinate system; the statistical characteristics of the pointing and ranging random errors of the satellite-borne laser radar include the statistical variances of the pointing and ranging random errors corresponding to the multiple laser radar observation data; the statistical variances of the pointing and ranging random errors include the statistical variances of the pointing angle random errors in the roll direction, the statistical variances of the pointing angle random errors in the pitch direction, and the statistical variances of the ranging random errors corresponding to the multiple laser radar observation data;

[0037] The pointing and ranging random error estimation model is established in the following way:

[0038] According to the pointing and ranging random error model shown in formula (5), let: , , Represents the random errors of pointing and ranging corresponding to multiple lidar observation data, and determines the covariance matrix of the random errors of positioning of the target lidar observation point in the geocentric inertial coordinate system, as shown in formula (6):

[0039] ; (6)

[0040] in, The covariance matrix representing the random positioning error of the target lidar observation point in the geocentric inertial coordinate system; represents the target matrix, , , They represent the projection coordinates of the laser beam pointing to the x-axis, y-axis, and z-axis directions of the satellite body coordinate system respectively;

[0041] represents the covariance matrix of the pointing and ranging random errors, Represents the statistical variance of the random error of the pointing angle in the roll direction corresponding to multiple lidar observation data, Represents the statistical variance of the random error of the pointing angle in the pitch direction corresponding to multiple lidar observation data, Represents the statistical variance of the ranging random errors corresponding to multiple lidar observation data; represents the cross-covariance between the random errors of the pointing angles in the roll direction and the random errors of the pointing angles in the pitch direction corresponding to multiple lidar observation data, Represents the cross-covariance between the random error of the pointing angle and the random error of the ranging in the roll direction corresponding to multiple lidar observation data, It represents the cross-covariance between the random errors of the pointing angles in the pitch direction and the random errors of the pointing angles in the roll direction corresponding to multiple lidar observation data. Represents the cross-covariance between the random error of the pointing angle in the pitch direction and the random error of the ranging in the pitch direction corresponding to multiple lidar observation data, It represents the cross-covariance between the random error of ranging and the random error of pointing angle in the pitch direction corresponding to multiple lidar observation data, Represents the cross-covariance between the random errors of ranging and the random errors of pointing angles in the rolling direction corresponding to multiple lidar observation data;

[0042] According to the statistical characteristics of the plane and elevation random errors, the covariance matrix of the positioning random error of the target lidar observation point in the geocentric inertial coordinate system is determined, as shown in formula (7):

[0043] ; (7)

[0044] in, A rotation matrix representing the transformation from the coordinate system of the reference grid data to the geocentric inertial coordinate system; Represents the statistical variance of the random error in the X direction of multiple lidar observation data, Represents the statistical variance of the Y-direction random error of multiple lidar observation data, Represents the statistical variance of the Z-direction random errors of multiple lidar observation data;

[0045] Based on equations (6) and (7), the pointing and ranging random error estimation model is established, as shown in equation (8):

[0046] (8).

[0047] In some possible implementations, determining the first direction slope data and the second direction slope data corresponding to each lidar observation data based on the first coordinate information sequence and the second coordinate information sequence corresponding to the reference grid data includes:

[0048] Based on the first coordinate information sequence, the second coordinate information sequence and formula (9),

[0049] The formula (9) is as follows:

[0050] ; (9)

[0051] in, represents the first direction slope data, represents the second direction slope data; Indicates the reference elevation corresponding to the first coordinate information and the second coordinate information of the reference grid data, where x represents the first coordinate information and y represents the second coordinate information; Indicates the grid interval corresponding to the reference grid data;

[0052] The first directional slope data and the second directional slope data are determined.

[0053] In some possible implementations, determining the first coordinate information sequence, the second coordinate information sequence, and the reference elevation sequence corresponding to the reference raster data based on the longitude, latitude, and elevation of the plurality of lidar observation data includes:

[0054] Performing coordinate system conversion on the longitudes and latitudes of the plurality of laser radar observation data to obtain a first coordinate information sequence and a second coordinate information sequence corresponding to the plane coordinate system of the reference raster data;

[0055] The first coordinate information sequence and the second coordinate information sequence are processed using a bilinear interpolation method to obtain the reference elevation sequence.

[0056] On the other hand, a device for estimating random errors of pointing and ranging of a space-borne laser radar is provided, the device comprising:

[0057] A data acquisition module is configured to acquire a geolocation data sequence, reference grid data, and spatial measurement and positioning data corresponding to a spaceborne lidar; the geolocation data sequence includes the longitude, latitude, and elevation of a plurality of lidar observation data; the reference grid data includes a plurality of grid cells, each grid cell including a corresponding cell elevation; the reference grid data and the plurality of lidar observation data share a unified vertical reference;

[0058] An information determination module, configured to determine a first coordinate information sequence, a second coordinate information sequence, and a reference elevation sequence corresponding to the reference raster data based on the longitude, latitude, and elevation of the plurality of lidar observation data;

[0059] a module for determining the statistical characteristics of the plane and elevation random errors, for determining the statistical characteristics of the plane and elevation random errors corresponding to the plurality of lidar observation data based on the elevations of the plurality of lidar observation data, the reference elevation sequence corresponding to the reference grid data, the first coordinate information sequence, and the second coordinate information sequence, and using a random error statistical characteristic solution model;

[0060] The module for determining the statistical characteristics of the random errors of pointing and ranging is used to input the statistical characteristics of the plane and elevation random errors and the spatial measurement and positioning data into the random error estimation model of pointing and ranging to obtain the statistical characteristics of the random errors of pointing and ranging of the spaceborne laser radar.

[0061] On the other hand, an electronic device is provided, 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 above-mentioned method for estimating random errors of pointing and ranging of a space-borne laser radar.

[0062] 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 random errors of pointing and ranging of a space-borne laser radar.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] In the present invention, the longitude, latitude and elevation of multiple laser radar observation data corresponding to the acquired satellite-borne laser radar are determined to obtain a first coordinate information sequence, a second coordinate information sequence and a reference elevation sequence corresponding to the longitude, latitude and elevation of the laser radar observation data on the reference grid data. On the basis of a unified vertical benchmark between the reference grid data and the multiple laser radar observation data, according to the elevations of the multiple laser radar observation data, the reference elevation sequence corresponding to the reference grid data, the first coordinate information sequence and the second coordinate information sequence, and by using a random error statistical feature solution model, the statistical characteristics of the plane and elevation random errors corresponding to the multiple laser radar observation data are determined; then the statistical characteristics of the plane and elevation random errors and the acquired spatial measurement and positioning data are input into a pointing and ranging random error estimation model to obtain the statistical characteristics of the pointing and ranging random errors corresponding to the satellite-borne laser radar, thereby realizing the pointing and ranging of the satellite-borne laser radar. The estimation of random errors in pointing and ranging can improve the accuracy of estimating random errors in pointing and ranging of spaceborne lidar, as well as the efficiency of estimating random errors in pointing and ranging of spaceborne lidar, thereby effectively evaluating the positioning accuracy of spaceborne lidar data, and providing a reliable basis for subsequent data processing and application. Furthermore, the statistical characteristics of the estimated random errors in pointing and ranging can provide theoretical guidance for the design and performance evaluation of laser payload systems, that is, according to the statistical characteristics of random errors in pointing and ranging, determine whether the random errors in pointing and ranging meet the requirements for the application of lidar observation data. If the requirements for the application of lidar observation data cannot be met, corresponding adjustments are made in the design of the laser payload system; and after the spaceborne lidar is launched, the performance of the spaceborne lidar can be evaluated through real-time estimation of random errors in pointing and ranging, and it is determined whether the lidar observation data can be applied. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] 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.

[0066] Figure 1 1 is a flow chart of a method for estimating random errors of pointing and ranging of a space-borne laser radar provided by an embodiment of the present invention;

[0067] Figure 2 1 is a flow chart of determining statistical characteristics of planar and elevation random errors corresponding to a plurality of laser observation data provided by an embodiment of the present invention;

[0068] Figure 3Schematic diagram of random errors in plane and elevation affected by surface slope, provided by an embodiment of the present invention;

[0069] Figure 4 is a result diagram of elevation residual and total elevation random error provided by an embodiment of the present invention;

[0070] Figure 5 This is a schematic diagram of the three-dimensional positioning of the laser radar observation point of the target measured by the space-borne laser radar provided by an embodiment of the present invention;

[0071] Figure 6 This is a schematic structural diagram of a device for estimating random errors of pointing and ranging of a space-borne laser radar provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] Figure 1 It is a flowchart of a method for estimating random errors in pointing and ranging 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 flowchart, 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:

[0080] S101: Acquire a geolocation data sequence, reference grid data, and spatial measurement and positioning data corresponding to a space-borne lidar; the geolocation data sequence includes the longitude, latitude, and elevation of multiple lidar observation data; the reference grid data includes multiple grid cells, each grid cell includes a corresponding cell elevation; the reference grid data and the multiple lidar observation data are unified in vertical datum;

[0081] In a specific embodiment, the geolocation data sequence may be information obtained by a satellite-borne lidar for describing the spatial positions of various geographic entities on the earth; the geolocation data sequence may include the longitude, latitude, and elevation of multiple lidar observation data.

[0082] The multiple lidar observation data may be data obtained by observing multiple target observation objects through a satellite-borne lidar. Specifically, the lidar observation data may include the position information of the target observation object on the earth's surface, that is, the longitude, latitude and elevation of the target observation object; the target observation object may include the earth's surface, atmosphere, vegetation, polar regions and oceans, etc.; optionally, the longitude, latitude and elevation of the multiple lidar observation data may be the longitude, latitude and elevation of the multiple target observation objects on the earth's surface. Optionally, the elevation may represent the distance from any point on the target observation object to the absolute base plane along the plumb line direction. The absolute base plane usually selects a certain sea surface as the reference plane. Specifically, the longitude of the multiple lidar observation data may be expressed as , is the longitude of the Nth lidar observation data; the latitude of multiple lidar observation data can be expressed as , is the latitude of the Nth lidar observation data; the elevation of multiple lidar observation data can be expressed as , is the elevation of the Nth lidar observation data; the geolocation data sequence can be .

[0083] Optionally, the geolocation data sequence is a geolocation data sequence that has been calibrated for system errors and corrected for atmospheric delays and solid tides, which facilitates the subsequent estimation of random errors in spaceborne lidar pointing and ranging, and can improve the accuracy of the estimation of random errors in spaceborne lidar pointing and ranging.

[0084] In a specific embodiment, the reference raster data may be a data format that divides geographic space into regular grid cells, with each grid cell including an attribute value corresponding to the cell representing a geographic entity. Specifically, each grid cell may represent a specific area on the target observation object, and the attribute value corresponding to each grid cell may be elevation. Optionally, the reference raster data may be a digital elevation model or a digital surface model. A digital elevation model uses raster data to represent terrain elevation information, while a digital surface model uses raster data to represent surface elevation information. Specifically, the reference raster data may be determined based on the actual implementation site.

[0085] In a specific embodiment, the reference grid data and the lidar data have been aligned to a unified vertical reference, ensuring that the lidar data and the reference grid data are consistent in vertical information, thereby determining the random vertical error of the lidar observation data after systematic error calibration. Optionally, by aligning the vertical reference of the reference grid data and the lidar data, the reference grid data can provide an accurate spatial position reference for the lidar data, ensuring that each lidar observation data can be found in the reference grid data.

[0086] S102: Determine a first coordinate information sequence, a second coordinate information sequence, and a reference elevation sequence corresponding to the reference grid data based on the longitude, latitude, and elevation of the plurality of laser radar observation data;

[0087] In a specific embodiment, the first coordinate information sequence corresponding to the reference grid data may include multiple first coordinate information, and the multiple first coordinate information may be the horizontal coordinate information of the longitude of multiple laser radar observation data in the plane coordinate system corresponding to the reference grid data. express, is the horizontal coordinate information of the longitude of the Nth laser radar observation data in the plane coordinate system corresponding to the reference grid data; the second coordinate information sequence corresponding to the reference grid data may include multiple second coordinate information, and the multiple second coordinate information may be the vertical coordinate information of the latitude of multiple laser radar observation data in the plane coordinate system corresponding to the reference grid data, which can be express, It is the vertical coordinate information of the latitude of the Nth laser radar observation data in the plane coordinate system corresponding to the reference raster data; the reference elevation sequence corresponding to the reference raster data can be an elevation information sequence corresponding to the first coordinate information sequence and the second coordinate information sequence on the reference raster data. Specifically, the reference elevation sequence corresponding to the reference raster data can include multiple reference elevations, and the multiple reference elevations can be the horizontal coordinate information and the vertical coordinate information of the longitude and latitude of multiple laser radar observation data in the plane coordinate system corresponding to the reference raster data. The reference elevation sequence can be express, The horizontal and vertical coordinates of the Nth lidar observation data in the plane coordinate system corresponding to the reference raster data correspond to the horizontal and vertical coordinates of the longitude and latitude of the Nth lidar observation data. Optionally, the longitudes of multiple lidar observation data may correspond to the first coordinate information sequence corresponding to the reference raster data, and the latitudes of multiple lidar observation data may correspond to the second coordinate information sequence corresponding to the reference raster data.

[0088] In an optional embodiment, the above-mentioned determination of the first coordinate information sequence, the second coordinate information sequence, and the reference elevation sequence corresponding to the reference raster data based on the longitude, latitude, and elevation of the plurality of lidar observation data includes:

[0089] Performing coordinate system conversion on the longitude and latitude of the plurality of lidar observation data to obtain a first coordinate information sequence and a second coordinate information sequence corresponding to the plane coordinate system of the reference grid data;

[0090] The first coordinate information sequence and the second coordinate information sequence are processed by using a bilinear interpolation method to obtain a reference elevation sequence.

[0091] In a specific embodiment, multiple laser radar observation data are within the grid range of the reference grid data, and the longitude and latitude of the multiple laser radar observation data are converted into a first coordinate information sequence and a second coordinate information sequence in the plane coordinate system of the reference grid data; the first coordinate information sequence and the second coordinate information sequence are processed using a bilinear interpolation method to obtain a reference elevation sequence. Optionally, the reference elevation sequence can be determined based on a bilinear interpolator generated according to the reference grid data, that is, based on Determine the reference elevation sequence. The reference grid data can be .

[0092] In the above embodiment, each lidar observation data has a corresponding position in the reference grid data. The determination of the reference elevation sequence, the first coordinate information sequence and the second coordinate information sequence corresponding to the multiple lidar observation data in the reference grid data provides a data basis for the determination of the statistical characteristics of the subsequent plane and elevation random errors, and facilitates the determination of the statistical characteristics of the subsequent plane and elevation random errors.

[0093] S103: Determine statistical characteristics of planar and elevation random errors corresponding to the plurality of lidar observation data based on the elevations of the plurality of lidar observation data, the reference elevation sequence corresponding to the reference grid data, the first coordinate information sequence, and the second coordinate information sequence, using a random error statistical characteristic solution model;

[0094] In a specific embodiment, the random error statistical feature solution model is used to solve the statistical features of plane and elevation random errors; optionally, the statistical features of plane and elevation random errors can reflect the degree of fluctuation of plane and elevation random errors; optionally, the statistical features can include statistical mean, statistical variance, statistical standard deviation, etc., which can be set in combination with actual application requirements; specifically, the statistical features of plane and elevation random errors in the present invention can be the statistical variance of plane and elevation random errors. The statistical variance of plane and elevation random errors includes the statistical variance of plane random errors and the statistical variance of elevation random errors. The statistical variance of plane random errors is the statistical variance of random errors in the X and Y directions of the laser radar observation data, and the statistical variance of elevation random errors is the statistical variance of random errors in the Z direction of the laser radar observation data.

[0095] In an optional embodiment, Figure 2 1 is a flow chart of determining the statistical characteristics of the plane and elevation random errors corresponding to the plurality of laser observation data provided by the embodiment of the present invention; Figure 2 As shown, the above method determines the statistical characteristics of the plane and elevation random errors corresponding to the multiple lidar observation data based on the elevations of the multiple lidar observation data, the reference elevation sequence corresponding to the reference grid data, the first coordinate information sequence, and the second coordinate information sequence, and utilizes the random error statistical characteristic solution model, including:

[0096] S201: determining an elevation residual corresponding to each lidar observation data according to the elevations of the plurality of lidar observation data and a reference elevation sequence corresponding to the reference grid data;

[0097] S202: Determine first direction slope data and second direction slope data corresponding to each laser radar observation data based on the first coordinate information sequence and the second coordinate information sequence corresponding to the reference grid data;

[0098] S203: Inputting the elevation residual, first direction slope data and second direction slope data corresponding to each lidar observation data into the random error statistical feature solution model, and solving to obtain the statistical features of the plane and elevation random errors corresponding to the multiple lidar observation data.

[0099] In a specific embodiment, the first direction slope data corresponding to each lidar observation data can be the surface slope component in the horizontal axis direction, i.e., the X direction, corresponding to each lidar observation data; the second direction slope data corresponding to each lidar observation data can be the surface slope component in the vertical axis direction, i.e., the Y direction, corresponding to each lidar observation data.

[0100] In a specific embodiment, the elevation residual corresponding to each lidar observation data is determined by subtracting the elevation of multiple lidar observation data from the reference elevation sequence corresponding to the reference grid data; the elevation residual sequence corresponding to the multiple lidar observation data can be expressed as , Represents the elevation residual corresponding to the Nth lidar observation data; since the laser observation data has been calibrated with systematic errors, the elevation residuals of multiple lidar observation data can be random errors, which can be specifically expressed as , Represents the total random error of elevation of multiple lidar observation data, Represents the total random error of the elevation of the Nth lidar observation data.

[0101] In an optional embodiment, the determining of the first direction slope data and the second direction slope data corresponding to each lidar observation data based on the first coordinate information sequence and the second coordinate information sequence corresponding to the reference grid data includes:

[0102] Based on the first coordinate information sequence, the second coordinate information sequence and formula (9),

[0103] Formula (9) is shown as follows:

[0104] ; (9)

[0105] in, Represents the first direction slope data, Indicates the second direction slope data; Indicates the reference elevation corresponding to the first coordinate information and the second coordinate information of the reference grid data, where x represents the first coordinate information and y represents the second coordinate information; Indicates the grid interval corresponding to the reference grid data;

[0106] First direction slope data and second direction slope data are determined.

[0107] In a specific embodiment, the multiple first coordinate information in the first coordinate information sequence and the multiple second coordinate information in the second coordinate information sequence can be substituted into formula (9) to determine the first direction slope data and the second direction slope data corresponding to each laser radar observation data. The first direction slope data sequence corresponding to the multiple laser radar observation data can be expressed as , The first direction slope data corresponding to the Nth laser radar observation data can be expressed as: , Indicates the second direction slope data corresponding to the Nth lidar observation data.

[0108] In an optional embodiment, the statistical characteristics of the plane and elevation random errors corresponding to the plurality of laser radar observation data include the statistical variances of the plane and elevation random errors corresponding to the plurality of laser radar observation data; the statistical variances of the plane and elevation random errors include the statistical variances of the random errors of the plurality of laser radar observation data in the X direction, the Y direction, and the Z direction;

[0109] The statistical characteristic calculation model of random errors is constructed in the following way:

[0110] According to the first direction slope data and the second direction slope data corresponding to each lidar observation data, the plane and elevation random error models are established, as shown in formula (1):

[0111] ; (1)

[0112] in, represents the plane and elevation random errors corresponding to the i-th lidar observation data; the random variable δx represents the random error of multiple lidar observation data in the X direction; δy represents the random error of multiple lidar observation data in the Y direction, and the random variable δz represents the random error of multiple lidar observation data in the Z direction; and Respectively represent the first direction slope data and the second direction slope data corresponding to the i-th laser radar observation data;

[0113] Based on the plane and elevation random error models, the random error statistical characteristic calculation model is constructed by combining the elevation residual, the first direction slope data, and the second direction slope data corresponding to each lidar observation data, as shown in formula (2):

[0114] ; (2)

[0115] in, represents the elevation residual corresponding to the i-th lidar observation data; Represents the statistical mean of the elevation residuals corresponding to multiple lidar observation data; Represents the statistical variance of random errors of multiple lidar observation data in the X direction, Represents the statistical variance of random errors of multiple lidar observation data in the Y direction, It represents the statistical variance of the random errors of multiple lidar observation data in the Z direction, and N represents the number of multiple lidar observation data.

[0116] In a specific embodiment, Figure 3Schematic diagram of random errors in plane and elevation affected by surface slope provided by an embodiment of the present invention; Figure 3 As shown in the figure, the effect of surface slope on random errors in plane and elevation is reflected. When the surface has a slope, the random error in plane direction will be coupled to the random error in elevation direction due to the slope, resulting in a change in the random error in elevation direction compared with the real terrain (such as the unit elevation in the reference grid data). Figure 3 As shown, the elevation random error is δz. Since the LiDAR observation data has planar random errors, the elevation random error obtained by comparing it with the real terrain (such as the unit elevation in the reference grid data) is actually the total elevation random error shown in the figure, which includes the elevation random error and the random error coupled in the elevation direction due to the random error in the planar direction caused by the surface slope. Therefore, the planar and elevation random errors affected by the surface slope (total elevation random error) can include the elevation random error and the random error caused by the slope. The random error caused by the slope is the random error coupled in the elevation direction due to the random error in the planar direction caused by the surface slope. The random error caused by the slope can be determined based on the planar random error and the slope data. Therefore, in combination with the surface slope, the planar and elevation random errors can be determined based on the planar random error, the elevation random error, and the slope data. Specifically, the planar and elevation random errors can be determined based on the planar random error, the first direction slope data, the second direction slope data, and the elevation random error.

[0117] Furthermore, the above-mentioned plane and elevation random error model can be established based on the first direction slope data and the second direction slope data corresponding to the lidar observation data; the plane and elevation random error model can be established by combining the first direction slope data and the second direction slope data (surface slope), which can improve the accuracy and reliability of the plane and elevation random error model.

[0118] In a specific embodiment, the plane and elevation random errors include plane random error and elevation random error. The plane random error is the random error of the laser radar observation data in the X and Y directions, and the elevation random error is the random error of the laser radar observation data in the Z direction. Specifically, the random variable δ x and δ y They represent the random errors of the lidar observation data in the X and Y directions, and the random variable δ z It represents the random error of the lidar observation data in the Z direction. δx, δy, and δz all obey the normal distribution, and their expectation is 0, that is, E(δx)=E(δy)=E(δz)=0, which can improve the stability and reliability of the random error model.

[0119] In a specific embodiment, Figure 4: is a result diagram of elevation residual and total elevation random error provided by an embodiment of the present invention; Figure 4 As shown in the figure, the changing trends of the absolute elevation residuals and the standard deviation of the total elevation random error are shown. The absolute elevation residuals are the absolute values of the elevation residuals, and the standard deviation of the total elevation random error is determined based on the above random error model. The figure shows that the changing trends of the elevation residuals and the standard deviation of the total elevation random error are basically consistent. This further reflects that since the laser observation data has been calibrated for systematic errors, the elevation residuals of multiple lidar observation data can be the total elevation random error. Optionally, the total elevation random residual can be determined based on the elevation residuals, or the total elevation random error (plane and elevation random error) can be determined based on the above plane and elevation random error models.

[0120] In a specific embodiment, optionally, Represents the statistical mean of the elevation residuals of multiple lidar observations, which can represent the possible residual systematic errors after systematic error calibration. and Respectively represent the statistical variance of random errors of multiple lidar observation data in the X direction and Y direction, Represents the statistical variance of random errors in the Z direction of multiple lidar observation data.

[0121] In a specific embodiment, based on the elevation residuals corresponding to multiple lidar observation data, that is, the elevation residuals corresponding to multiple lidar observation data, the statistical variance of the plane and elevation random errors can be estimated, and then the moment estimation method can be used to construct a random error statistical feature solution model.

[0122] In a specific embodiment, the elevation residual, first-direction slope data, and second-direction slope data corresponding to each lidar observation data are input into a random error statistical feature solution model to obtain the statistical variance of the planar and elevation random errors. Optionally, based on the statistical variance of the planar random error and the statistical variance of the elevation random error, the statistical variance of the planar and elevation random errors (total elevation random error) of each lidar observation data can be determined as shown in the following formula:

[0123] ;

[0124] in, represents the statistical variance of the plane and elevation random errors (total elevation random error) of the i-th lidar observation data; Represents the statistical variance of random errors of multiple lidar observation data in the X direction, Represents the statistical variance of random errors of multiple lidar observation data in the Y direction, Represents the statistical variance of random errors of multiple lidar observation data in the Z direction; and They respectively represent the first direction slope data and the second direction slope data corresponding to the i-th laser radar observation data.

[0125] In the above embodiment, the elevation residual, first direction slope data and second direction slope data corresponding to the lidar observation data are input into the random error statistical characteristic solution model, and the statistical characteristics of the plane and elevation random errors corresponding to the multiple lidar observation data are obtained by solution, which facilitates the determination of the statistical characteristics of the subsequent pointing and ranging random errors, and then realizes the estimation of the pointing and ranging random errors of the satellite-borne lidar.

[0126] S104: Inputting the statistical characteristics of the plane and elevation random errors and the spatial measurement and positioning data into the pointing and ranging random error estimation model to obtain the statistical characteristics of the pointing and ranging random errors of the spaceborne lidar.

[0127] In a specific embodiment, the spatial measurement and positioning data can represent data measured by a satellite-borne laser radar and data used for satellite-borne laser radar positioning. Optionally, the spatial measurement and positioning data can include a rotation matrix for coordinate system conversion, a unit vector of the laser beam pointing in the satellite body coordinate system, and a ranging value from the laser radar emission reference point to the target laser radar observation point. The rotation matrix for coordinate system conversion is a rotation matrix for converting from the satellite body coordinate system to the geocentric inertial coordinate system and a rotation matrix for converting from the coordinate system where the reference grid data is located to the geocentric inertial coordinate system. Optionally, the statistical characteristics of the pointing and ranging random errors can be the statistical variance of the pointing and ranging random errors. The statistical variance of the pointing and ranging random errors can include the statistical variance of the pointing random errors and the statistical variance of the ranging random errors. The statistical variance of the pointing random errors can be the statistical variance of the random errors of the pointing angles of the laser radar observation data in the roll and pitch directions. The pointing and ranging random error estimation model can be a model for estimating the pointing and ranging random errors.

[0128] In a specific embodiment, the statistical variance of the pointing and ranging random error can reflect the magnitude of the pointing and ranging random error, thereby effectively evaluating the positioning accuracy of spaceborne lidar data. Specifically, the lower the statistical variance of the pointing and ranging random error, the smaller the pointing and ranging random error, and thus the higher the positioning accuracy of the spaceborne lidar data. The higher the statistical variance of the pointing and ranging random error, the larger the pointing and ranging random error, and thus the lower the positioning accuracy of the spaceborne lidar data. Furthermore, the statistical variance of the pointing and ranging random error can provide theoretical guidance for laser payload system design and performance evaluation. Specifically, based on the statistical characteristics of the pointing and ranging random error, it can be determined whether the pointing and ranging random error meets the application requirements of the lidar observation data. If it does not meet the application requirements of the lidar observation data, corresponding adjustments can be made in the laser payload system design. Furthermore, after the spaceborne lidar is launched, the statistical variance of the pointing and ranging random error can be obtained by real-time estimation of the pointing and ranging random error corresponding to the on-orbit operation of the spaceborne lidar, and the performance of the spaceborne lidar can be evaluated to determine whether the application of the lidar observation data will be affected.

[0129] In an optional embodiment, the spatial measurement and positioning data includes a unit vector of the laser beam pointing in the satellite body coordinate system; the pointing and ranging random error estimation model is obtained based on the pointing and ranging random error model;

[0130] The pointing and ranging random error model is established in the following way:

[0131] Determine the distance value from the satellite-borne lidar emission reference point to the target lidar observation point;

[0132] Based on the ranging value and the unit vector of the laser beam pointing in the satellite coordinate system, a positioning model for the target lidar observation point measured by the satellite-borne lidar is established, as shown in formula (3):

[0133] ; (3)

[0134] in, Represents the position vector of the target lidar observation point in the geocentric inertial coordinate system; represents the position vector of the satellite-borne laser radar emission reference point in the Earth-centered inertial coordinate system; l represents the distance value from the satellite-borne laser radar emission reference point to the target laser radar observation point; The unit vector representing the direction of the laser beam in the satellite body coordinate system; Represents the rotation matrix from the satellite body coordinate system to the geocentric inertial coordinate system;

[0135] Based on the positioning model, a positioning model with random errors in pointing and ranging is established, as shown in formula (4):

[0136] ; (4)

[0137] in, The position vector of the target lidar observation point with random errors in pointing and ranging in the Earth-centered inertial coordinate system; δ l Represents the random error of ranging; The resultant rotation matrix representing the random error in pointing;

[0138] Combining Equations (3) and (4), a pointing and ranging random error model is established, as shown in Equation (5):

[0139] ; (5)

[0140] in, Represents the random positioning error of the target lidar observation point in the geocentric inertial coordinate system; is the identity matrix; represents the rotation matrix of the random error of the pointing angle including the roll and pitch directions, Indicates the random error of the pointing angle in the roll direction corresponding to multiple lidar observation data, Indicates the random error of the pointing angle in the pitch direction corresponding to multiple lidar observation data.

[0141] In a specific embodiment, the ranging value is the distance from the satellite-borne laser radar emission reference point to the target laser radar observation point. Optionally, the emission reference point can be a fixed point of the satellite-borne laser radar or the starting point of the laser beam. Specifically, the ranging value can be the actual distance from the emission reference point to the target laser radar observation point calculated by the satellite-borne laser radar after receiving the reflected light after emitting a laser beam from the emission reference point to the target laser radar observation point. Optionally, the satellite coordinate system and the geographic coordinate system need to be considered when converting the ranging value. In order to achieve corresponding conversion and matching, a rotation matrix for coordinate system conversion can be used to achieve more accurate positioning and measurement.

[0142] The unit vector pointing to the laser beam in the satellite body coordinate system can be used to describe the direction of the laser beam relative to the satellite body. This unit vector is a three-dimensional vector that points to the emission direction of the laser beam. Optionally, the unit vector pointing to the laser beam in the satellite body coordinate system can be transformed with the geocentric inertial coordinate system for more accurate positioning and measurement. This transformation can usually be achieved through the rotation matrix used for coordinate system transformation.

[0143] In a specific embodiment, a positioning model with random errors in pointing and ranging is established based on corrections for systematic errors in lidar observation data (ranging and pointing) as well as atmospheric delay and solid tide.

[0144] In a specific embodiment, , 、 、 Respectively represent the projection coordinates of the target lidar observation point in the x-axis, y-axis, and z-axis directions of the geocentric inertial coordinate system; , 、 、 Indicates the projection coordinates of the satellite-borne laser radar emission reference point in the x, y, and z axis directions of the Earth-centered inertial coordinate system. Ranging random error , Represents the statistical variance of the random error in ranging corresponding to the i-th lidar observation data; the unit matrix .

[0145] In a specific embodiment, The synthetic rotation matrix represents the random pointing error. Since the influence of the yaw angle on the laser data positioning is negligible, The random errors of the pointing angles in the roll (i.e., rotation around the X-axis of the satellite's coordinate system) and pitch (i.e., rotation around the Y-axis of the satellite's coordinate system) directions can be simplified by taking a small angle approximation form:

[0146] ;

[0147] in, Indicates the random error of the pointing angle in the roll direction (i.e., rotation around the X-axis of the satellite's coordinate system); It represents the random error of the pointing angle in the pitch direction (i.e., rotation around the Y-axis of the satellite body coordinate system).

[0148] In a specific embodiment, Figure 5 3D positioning diagram of a target laser radar observation point measured by a space-borne laser radar according to an embodiment of the present invention; Figure 5 As shown in the figure, the surface target is used as the target lidar observation point, and the Earth-Centered Inertial Frame (ECI) takes the center of mass of the earth as the coordinate origin O, including the X-axis X ECI , Y axis ECI and Z axis ECI The satellite body fixed (SBF) coordinate system takes the satellite as the coordinate origin O and includes the X-axis X SBF , Y axisSBF and Z axis SBF , the Earth-centered inertial coordinate system and the satellite body coordinate system can be combined to perform three-dimensional positioning of the target lidar observation point by the spaceborne lidar.

[0149] In the above embodiment, by establishing the pointing and ranging random error model of the satellite-borne laser radar, it is possible to facilitate the establishment of the pointing and ranging random error estimation model, and then the estimation of the pointing and ranging random errors of the satellite-borne laser radar can be realized.

[0150] In an optional embodiment, the space measurement and positioning data also includes a rotation matrix converted from the satellite body coordinate system to the geocentric inertial coordinate system, and a rotation matrix converted from the coordinate system where the reference grid data is located to the geocentric inertial coordinate system; the statistical characteristics of the pointing and ranging random errors of the satellite-borne laser radar include the statistical variances of the pointing and ranging random errors corresponding to multiple laser radar observation data; the statistical variances of the pointing and ranging random errors include the statistical variances of the pointing angle random errors in the roll direction, the statistical variances of the pointing angle random errors in the pitch direction, and the statistical variances of the ranging random errors corresponding to multiple laser radar observation data;

[0151] The pointing and ranging random error estimation model is established in the following way:

[0152] According to the pointing and ranging random error model shown in formula (5), let: , , Represents the random errors of pointing and ranging corresponding to multiple lidar observation data, and determines the covariance matrix of the random errors of positioning of the target lidar observation point in the geocentric inertial coordinate system, as shown in formula (6):

[0153] ; (6)

[0154] in, Represents the covariance matrix of the random positioning error of the target lidar observation point in the geocentric inertial coordinate system; represents the target matrix, , , They represent the projection coordinates of the laser beam pointing to the x-axis, y-axis, and z-axis directions of the satellite body coordinate system respectively;

[0155] represents the covariance matrix of the pointing and ranging random errors, Represents the statistical variance of the random error of the pointing angle in the roll direction corresponding to multiple lidar observation data, Represents the statistical variance of the random error of the pointing angle in the pitch direction corresponding to multiple lidar observation data, Represents the statistical variance of the ranging random errors corresponding to multiple lidar observation data; represents the cross-covariance between the random errors of the pointing angles in the roll direction and the random errors of the pointing angles in the pitch direction corresponding to multiple lidar observation data, Represents the cross-covariance between the random error of the pointing angle and the random error of the ranging in the roll direction corresponding to multiple lidar observation data, It represents the cross-covariance between the random errors of the pointing angles in the pitch direction and the random errors of the pointing angles in the roll direction corresponding to multiple lidar observation data. Represents the cross-covariance between the random error of the pointing angle in the pitch direction and the random error of the ranging in the pitch direction corresponding to multiple lidar observation data, It represents the cross-covariance between the random error of ranging and the random error of pointing angle in the pitch direction corresponding to multiple lidar observation data, Represents the cross-covariance between the random errors of ranging and the random errors of pointing angles in the rolling direction corresponding to multiple lidar observation data;

[0156] According to the statistical characteristics of the plane and elevation random errors, the covariance matrix of the positioning random error of the target lidar observation point in the geocentric inertial coordinate system is determined, as shown in formula (7):

[0157] ; (7)

[0158] in, Represents the rotation matrix from the coordinate system of the reference grid data to the geocentric inertial coordinate system; Represents the statistical variance of the random error in the X direction of multiple lidar observation data, Represents the statistical variance of the Y-direction random error of multiple lidar observation data, Represents the statistical variance of the Z-direction random errors of multiple lidar observation data;

[0159] Based on Equations (6) and (7), a pointing and ranging random error estimation model is established, as shown in Equation (8):

[0160] (8).

[0161] In a specific embodiment, the covariance matrix of the pointing and ranging random errors is The diagonal elements are the statistical variance of the random errors of pointing and ranging, and the target matrix Has no practical meaning and is used to simplify formulas.

[0162] In a specific embodiment, the statistical variance of the pointing and ranging random errors can be used to determine whether the pointing and ranging random errors meet the requirements for the application of the lidar observation data; and the changes in the pointing and ranging random errors can be determined by real-time estimation of the pointing and ranging random errors, thereby determining whether the changes in the pointing and ranging random errors will affect the application of subsequent lidar observation data.

[0163] It can be seen from the technical solutions provided in the above embodiments of this specification that this specification determines the first coordinate information sequence, the second coordinate information sequence and the reference elevation sequence corresponding to the longitude, latitude and elevation of the laser radar observation data on the acquired reference grid data by obtaining a geographic positioning data sequence corresponding to the satellite-borne laser radar that has been calibrated for system errors and corrected for atmospheric delays and solid tides, that is, the longitude, latitude and elevation of multiple laser radar observation data. On the basis of a unified vertical benchmark between the reference grid data and the multiple laser radar observation data, according to the elevations of the multiple laser radar observation data, the reference elevation sequence corresponding to the reference grid data, the first coordinate information sequence and the second coordinate information sequence, and using a random error statistical feature solution model, the statistical characteristics of the plane and elevation random errors corresponding to the multiple laser radar observation data are determined; then the statistical characteristics of the plane and elevation random errors and the acquired spatial measurement and positioning data are input into the pointing and ranging random error estimation model to obtain the corresponding satellite-borne laser radar. The statistical characteristics of pointing and ranging random errors realize the estimation of pointing and ranging random errors of spaceborne lidar, and can improve the accuracy of estimating pointing and ranging random errors of spaceborne lidar, as well as the efficiency of estimating pointing and ranging random errors of spaceborne lidar, thereby effectively evaluating the positioning accuracy of spaceborne lidar data, and providing a reliable basis for subsequent data processing and application. Furthermore, the statistical characteristics of the estimated pointing and ranging random errors can provide theoretical guidance for the design and performance evaluation of laser payload systems, that is, according to the statistical characteristics of pointing and ranging random errors, determine whether the pointing and ranging random errors meet the requirements for the application of lidar observation data. If the requirements for the application of lidar observation data cannot be met, corresponding adjustments are made in the design of the laser payload system; and after the spaceborne lidar is launched, the performance of the spaceborne lidar can be evaluated through real-time estimation of pointing and ranging random errors to determine whether the lidar observation data can be applied.

[0164] The embodiment of the present invention also provides a device for estimating random errors of pointing and ranging of a space-borne laser radar. Figure 6 is a schematic structural diagram of a device for estimating random errors of pointing and ranging of a space-borne laser radar provided by an embodiment of the present invention; Figure 6 As shown, the above device includes:

[0165] Data acquisition module 610 is configured to acquire a geolocation data sequence, reference grid data, and spatial measurement and positioning data corresponding to a spaceborne lidar; the geolocation data sequence includes the longitude, latitude, and elevation of a plurality of lidar observation data; the reference grid data includes a plurality of grid cells, each grid cell including a corresponding cell elevation; the reference grid data and the plurality of lidar observation data share a common vertical reference;

[0166] An information determination module 620 is configured to determine a first coordinate information sequence, a second coordinate information sequence, and a reference elevation sequence corresponding to the reference raster data based on the longitude, latitude, and elevation of the plurality of lidar observation data;

[0167] A module 630 for determining the statistical characteristics of random errors in plane and elevation corresponding to the plurality of LiDAR observation data, configured to determine the statistical characteristics of random errors in plane and elevation corresponding to the plurality of LiDAR observation data based on the elevations of the plurality of LiDAR observation data, the reference elevation sequence corresponding to the reference grid data, the first coordinate information sequence, and the second coordinate information sequence, using a random error statistical characteristic solution model;

[0168] The module 640 for determining the statistical characteristics of the random errors of pointing and ranging is used to input the statistical characteristics of the planar and elevation random errors and the spatial measurement and positioning data into the random error estimation model of pointing and ranging to obtain the statistical characteristics of the random errors of pointing and ranging of the spaceborne lidar.

[0169] In an optional embodiment, the module 630 for determining the statistical characteristics of the planar and elevation random errors includes:

[0170] an elevation residual determining unit, configured to determine an elevation residual corresponding to each laser radar observation data based on the elevations of the plurality of laser radar observation data and a reference elevation sequence corresponding to the reference grid data;

[0171] a slope data determining unit, configured to determine first direction slope data and second direction slope data corresponding to each laser radar observation data based on a first coordinate information sequence and a second coordinate information sequence corresponding to the reference grid data;

[0172] The statistical characteristic determination unit of plane and elevation random errors is used to input the elevation residual, first direction slope data and second direction slope data corresponding to each lidar observation data into the random error statistical characteristic solution model, and solve to obtain the statistical characteristics of the plane and elevation random errors corresponding to the multiple lidar observation data.

[0173] In an optional embodiment, the statistical characteristics of the plane and elevation random errors corresponding to the multiple laser radar observation data include the statistical variances of the plane and elevation random errors corresponding to the multiple laser radar observation data; the statistical variances of the plane and elevation random errors include the statistical variances of the random errors of the multiple laser radar observation data in the X direction, the Y direction, and the Z direction;

[0174] The device also includes: a random error statistical feature solution model construction module for

[0175] According to the first direction slope data and the second direction slope data corresponding to each lidar observation data, a plane and elevation random error model is established, as shown in formula (1):

[0176] ; (1)

[0177] in, represents the plane and elevation random errors corresponding to the i-th lidar observation data; the random variable δx represents the random error of multiple lidar observation data in the X direction; δy represents the random error of multiple lidar observation data in the Y direction, and the random variable δz represents the random error of multiple lidar observation data in the Z direction; and Respectively represent the first direction slope data and the second direction slope data corresponding to the i-th laser radar observation data;

[0178] Based on the plane and elevation random error model, combined with the elevation residual corresponding to each lidar observation data, the first direction slope data and the second direction slope data, a random error statistical characteristic solution model is constructed, as shown in formula (2):

[0179] ; (2)

[0180] in, represents the elevation residual corresponding to the i-th lidar observation data; Represents the statistical mean of the elevation residuals corresponding to multiple lidar observation data; Represents the statistical variance of random errors of multiple lidar observation data in the X direction, Represents the statistical variance of random errors of multiple lidar observation data in the Y direction, It represents the statistical variance of the random errors of multiple lidar observation data in the Z direction, and N represents the number of multiple lidar observation data.

[0181] In an optional embodiment, the spatial measurement and positioning data includes a unit vector of the laser beam pointing in the satellite body coordinate system; the pointing and ranging random error estimation model is obtained based on the pointing and ranging random error model;

[0182] The device also includes: a pointing and ranging random error model establishment module for

[0183] Determine the distance value from the satellite-borne laser radar emission reference point to the target laser radar observation point;

[0184] Based on the ranging value and the unit vector of the laser beam pointing in the satellite body coordinate system, a positioning model for the satellite-borne laser radar to measure the target laser radar observation point is established, as shown in formula (3):

[0185] ; (3)

[0186] in, Represents the position vector of the target lidar observation point in the geocentric inertial coordinate system; represents the position vector of the satellite-borne laser radar emission reference point in the Earth-centered inertial coordinate system; l represents the distance value from the satellite-borne laser radar emission reference point to the target laser radar observation point; A unit vector representing the direction of the laser beam in the satellite body coordinate system; Represents the rotation matrix from the satellite body coordinate system to the geocentric inertial coordinate system;

[0187] Based on the positioning model, a positioning model with random errors in pointing and ranging is established, as shown in formula (4):

[0188] ; (4)

[0189] in, Indicates the position vector of the target lidar observation point with random errors in pointing and ranging in the Earth-centered inertial coordinate system; δ l Represents the random error of ranging; The resultant rotation matrix representing the random error in pointing;

[0190] Combining equations (3) and (4), the pointing and ranging random error model is established, as shown in equation (5):

[0191] ; (5)

[0192] in, represents the random positioning error of the target lidar observation point in the geocentric inertial coordinate system; is the identity matrix; represents the rotation matrix of the random error of the pointing angle including the roll and pitch directions, Indicates the random error of the pointing angle in the roll direction corresponding to multiple lidar observation data, Indicates the random error of the pointing angle in the pitch direction corresponding to multiple lidar observation data.

[0193] In an optional embodiment, the space measurement and positioning data also includes the rotation matrix converted from the satellite body coordinate system to the geocentric inertial coordinate system, and the rotation matrix converted from the coordinate system where the reference grid data is located to the geocentric inertial coordinate system; the statistical characteristics of the pointing and ranging random errors of the satellite-borne laser radar include the statistical variances of the pointing and ranging random errors corresponding to the multiple laser radar observation data; the statistical variances of the pointing and ranging random errors include the statistical variances of the pointing angle random errors in the roll direction, the statistical variances of the pointing angle random errors in the pitch direction, and the statistical variances of the ranging random errors corresponding to the multiple laser radar observation data;

[0194] The device also includes: a pointing and ranging random error estimation establishment module, which is used to

[0195] According to the pointing and ranging random error model shown in formula (5), let: , , Represents the random errors of pointing and ranging corresponding to multiple lidar observation data, and determines the covariance matrix of the random errors of positioning of the target lidar observation point in the geocentric inertial coordinate system, as shown in formula (6):

[0196] ; (6)

[0197] in, The covariance matrix representing the random positioning error of the target lidar observation point in the geocentric inertial coordinate system; represents the target matrix, , , They represent the projection coordinates of the laser beam pointing to the x-axis, y-axis, and z-axis directions of the satellite body coordinate system respectively;

[0198] represents the covariance matrix of the pointing and ranging random errors, Represents the statistical variance of the random error of the pointing angle in the roll direction corresponding to multiple lidar observation data, Represents the statistical variance of the random error of the pointing angle in the pitch direction corresponding to multiple lidar observation data, Represents the statistical variance of the ranging random errors corresponding to multiple lidar observation data; represents the cross-covariance between the random errors of the pointing angles in the roll direction and the random errors of the pointing angles in the pitch direction corresponding to multiple lidar observation data, Represents the cross-covariance between the random error of the pointing angle and the random error of the ranging in the roll direction corresponding to multiple lidar observation data, It represents the cross-covariance between the random errors of the pointing angles in the pitch direction and the random errors of the pointing angles in the roll direction corresponding to multiple lidar observation data. Represents the cross-covariance between the random error of the pointing angle in the pitch direction and the random error of the ranging in the pitch direction corresponding to multiple lidar observation data, It represents the cross-covariance between the random error of ranging and the random error of pointing angle in the pitch direction corresponding to multiple lidar observation data, Represents the cross-covariance between the random errors of ranging and the random errors of pointing angles in the rolling direction corresponding to multiple lidar observation data;

[0199] According to the statistical characteristics of the plane and elevation random errors, the covariance matrix of the positioning random error of the target lidar observation point in the geocentric inertial coordinate system is determined, as shown in formula (7):

[0200] ; (7)

[0201] in, A rotation matrix representing the transformation from the coordinate system of the reference grid data to the geocentric inertial coordinate system; Represents the statistical variance of the random error in the X direction of multiple lidar observation data, Represents the statistical variance of the Y-direction random error of multiple lidar observation data, Represents the statistical variance of the Z-direction random errors of multiple lidar observation data;

[0202] Based on equations (6) and (7), the pointing and ranging random error estimation model is established, as shown in equation (8):

[0203] (8).

[0204] In an optional embodiment, the slope data determination unit is specifically configured to

[0205] Based on the first coordinate information sequence, the second coordinate information sequence and formula (9),

[0206] The formula (9) is as follows:

[0207] ; (9)

[0208] in, represents the first direction slope data, represents the second direction slope data; Indicates the reference elevation corresponding to the first coordinate information and the second coordinate information of the reference grid data, where x represents the first coordinate information and y represents the second coordinate information; Indicates the grid interval corresponding to the reference grid data;

[0209] The first directional slope data and the second directional slope data are determined.

[0210] In an optional embodiment, the information determination module 620 includes:

[0211] a coordinate information sequence determining unit, configured to perform coordinate system conversion on the longitudes and latitudes of the plurality of laser radar observation data to obtain a first coordinate information sequence and a second coordinate information sequence corresponding to the plane coordinate system of the reference grid data;

[0212] The reference elevation sequence determining unit is configured to perform data processing on the first coordinate information sequence and the second coordinate information sequence using a bilinear interpolation method to obtain the reference elevation sequence.

[0213] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0214] 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 random errors of pointing and ranging of a space-borne laser radar as described in any one of the method embodiments.

[0215] 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 pointing and ranging random error estimation method as described in any of the method embodiments.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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 random errors in pointing and ranging of a spaceborne laser radar, characterized in that: The method comprises: Obtaining a geolocation data sequence, reference grid data, and spatial measurement and positioning data corresponding to a spaceborne lidar; the geolocation data sequence includes the longitude, latitude, and elevation of a plurality of lidar observation data; the reference grid data includes a plurality of grid cells, each grid cell including a corresponding cell elevation; and the reference grid data and the plurality of lidar observation data share a unified vertical reference. Determine, based on the longitude, latitude, and elevation of the plurality of laser radar observation data, a first coordinate information sequence, a second coordinate information sequence, and a reference elevation sequence corresponding to the reference raster data; Determining statistical characteristics of planar and elevation random errors corresponding to the plurality of lidar observation data based on the elevations of the plurality of lidar observation data, a reference elevation sequence corresponding to the reference grid data, a first coordinate information sequence, and a second coordinate information sequence, and utilizing a random error statistical characteristic solution model; The statistical characteristics of the plane and elevation random errors and the spatial measurement and positioning data are input into a pointing and ranging random error estimation model to obtain the statistical characteristics of the pointing and ranging random errors of the spaceborne laser radar.

2. The method for estimating random errors of pointing and ranging of a spaceborne laser radar according to claim 1, wherein: The determining of statistical characteristics of plane and elevation random errors corresponding to the plurality of lidar observation data based on the elevations of the plurality of lidar observation data, the reference elevation sequence corresponding to the reference grid data, the first coordinate information sequence, and the second coordinate information sequence, and utilizing a random error statistical characteristic solution model, includes: Determining the elevation residual corresponding to each lidar observation data according to the elevations of the plurality of lidar observation data and a reference elevation sequence corresponding to the reference grid data; Determining first direction slope data and second direction slope data corresponding to each laser radar observation data based on the first coordinate information sequence and the second coordinate information sequence corresponding to the reference grid data; The elevation residual, first direction slope data and second direction slope data corresponding to each laser radar observation data are input into the random error statistical feature solution model to obtain the statistical features of the plane and elevation random errors corresponding to the multiple laser radar observation data.

3. The method for estimating random errors of pointing and ranging of a spaceborne laser radar according to claim 2, wherein: The statistical characteristics of the plane and elevation random errors corresponding to the multiple laser radar observation data include the statistical variances of the plane and elevation random errors corresponding to the multiple laser radar observation data; the statistical variances of the plane and elevation random errors include the statistical variances of the random errors in the X direction, the Y direction, and the Z direction of the multiple laser radar observation data; The random error statistical characteristic solution model is constructed in the following way: According to the first direction slope data and the second direction slope data corresponding to each lidar observation data, a plane and elevation random error model is established, as shown in formula (1): ； （1） in, represents the plane and elevation random errors corresponding to the i-th lidar observation data; the random variable δx represents the random error of multiple lidar observation data in the X direction; δy represents the random error of multiple lidar observation data in the Y direction, and the random variable δz represents the random error of multiple lidar observation data in the Z direction; and Respectively represent the first direction slope data and the second direction slope data corresponding to the i-th laser radar observation data; Based on the plane and elevation random error model, combined with the elevation residual corresponding to each lidar observation data, the first direction slope data and the second direction slope data, a random error statistical characteristic solution model is constructed, as shown in formula (2): ；（2） in, represents the elevation residual corresponding to the i-th lidar observation data; Represents the statistical mean of the elevation residuals corresponding to multiple lidar observation data; Represents the statistical variance of random errors of multiple lidar observation data in the X direction, Represents the statistical variance of random errors of multiple lidar observation data in the Y direction, It represents the statistical variance of the random errors of multiple lidar observation data in the Z direction, and N represents the number of multiple lidar observation data.

4. The method for estimating random errors of pointing and ranging of a spaceborne laser radar according to claim 1, wherein: The spatial measurement and positioning data includes a unit vector of the laser beam pointing in the satellite body coordinate system; the pointing and ranging random error estimation model is obtained based on the pointing and ranging random error model; The pointing and ranging random error model is established in the following way: Determine the distance value from the satellite-borne laser radar emission reference point to the target laser radar observation point; Based on the ranging value and the unit vector of the laser beam pointing in the satellite body coordinate system, a positioning model for the satellite-borne laser radar to measure the target laser radar observation point is established, as shown in formula (3): ；（3） in, Represents the position vector of the target lidar observation point in the geocentric inertial coordinate system; represents the position vector of the satellite-borne laser radar emission reference point in the Earth-centered inertial coordinate system; l represents the distance value from the satellite-borne laser radar emission reference point to the target laser radar observation point; A unit vector representing the direction of the laser beam in the satellite body coordinate system; Represents the rotation matrix from the satellite body coordinate system to the geocentric inertial coordinate system; Based on the positioning model, a positioning model with random errors in pointing and ranging is established, as shown in formula (4): ；（4） in, Indicates the position vector of the target lidar observation point with random errors in pointing and ranging in the Earth-centered inertial coordinate system; δ l represents the random error of ranging; The resultant rotation matrix representing the random error in pointing; Combining equations (3) and (4), the pointing and ranging random error model is established, as shown in equation (5): ；（5） in, represents the random positioning error of the target lidar observation point in the geocentric inertial coordinate system; is the identity matrix; represents the rotation matrix of the random error of the pointing angle including the roll and pitch directions, Indicates the random error of the pointing angle in the roll direction corresponding to multiple lidar observation data, Indicates the random error of the pointing angle in the pitch direction corresponding to multiple lidar observation data.

5. The method for estimating random errors of pointing and ranging of a spaceborne laser radar according to claim 4, wherein: The spatial measurement and positioning data also includes the rotation matrix converted from the satellite body coordinate system to the geocentric inertial coordinate system, and the rotation matrix converted from the coordinate system where the reference grid data is located to the geocentric inertial coordinate system; the statistical characteristics of the pointing and ranging random errors of the satellite-borne laser radar include the statistical variances of the pointing and ranging random errors corresponding to the multiple laser radar observation data; the statistical variances of the pointing and ranging random errors include the statistical variances of the pointing angle random errors in the roll direction, the statistical variances of the pointing angle random errors in the pitch direction, and the statistical variances of the ranging random errors corresponding to the multiple laser radar observation data; The pointing and ranging random error estimation model is established in the following way: According to the pointing and ranging random error model shown in formula (5), let: , , Represents the random errors of pointing and ranging corresponding to multiple lidar observation data, and determines the covariance matrix of the random errors of positioning of the target lidar observation point in the geocentric inertial coordinate system, as shown in formula (6): ；（6） in, The covariance matrix representing the random positioning error of the target lidar observation point in the geocentric inertial coordinate system; represents the target matrix, , , They represent the projection coordinates of the laser beam pointing to the x-axis, y-axis, and z-axis directions of the satellite body coordinate system respectively; represents the covariance matrix of the pointing and ranging random errors, Represents the statistical variance of the random error of the pointing angle in the roll direction corresponding to multiple lidar observation data, Represents the statistical variance of the random error of the pointing angle in the pitch direction corresponding to multiple lidar observation data, Represents the statistical variance of the ranging random errors corresponding to multiple lidar observation data; represents the cross-covariance between the random errors of the pointing angles in the roll direction and the random errors of the pointing angles in the pitch direction corresponding to multiple lidar observation data, Represents the cross-covariance between the random error of the pointing angle and the random error of the ranging in the roll direction corresponding to multiple lidar observation data, It represents the cross-covariance between the random errors of the pointing angles in the pitch direction and the random errors of the pointing angles in the roll direction corresponding to multiple lidar observation data. Represents the cross-covariance between the random error of the pointing angle in the pitch direction and the random error of the ranging in the pitch direction corresponding to multiple lidar observation data, It represents the cross-covariance between the random error of ranging and the random error of pointing angle in the pitch direction corresponding to multiple lidar observation data, Represents the cross-covariance between the random errors of ranging and the random errors of pointing angles in the rolling direction corresponding to multiple lidar observation data; According to the statistical characteristics of the plane and elevation random errors, the covariance matrix of the positioning random error of the target lidar observation point in the geocentric inertial coordinate system is determined, as shown in formula (7): ；（7） in, A rotation matrix representing the transformation from the coordinate system of the reference grid data to the geocentric inertial coordinate system; Represents the statistical variance of the random error in the X direction of multiple lidar observation data, Represents the statistical variance of the Y-direction random error of multiple lidar observation data, Represents the statistical variance of the Z-direction random errors of multiple lidar observation data; Based on equations (6) and (7), the pointing and ranging random error estimation model is established, as shown in equation (8): （8）。 6. The method for estimating random errors of pointing and ranging of a spaceborne laser radar according to claim 2, wherein: The determining, based on the first coordinate information sequence and the second coordinate information sequence corresponding to the reference grid data, the first direction slope data and the second direction slope data corresponding to each laser radar observation data includes: Based on the first coordinate information sequence, the second coordinate information sequence and formula (9), The formula (9) is as follows: ；（9） in, represents the first direction slope data, represents the second direction slope data; Indicates the reference elevation corresponding to the first coordinate information and the second coordinate information of the reference grid data, where x represents the first coordinate information and y represents the second coordinate information; Indicates the grid interval corresponding to the reference grid data; The first directional slope data and the second directional slope data are determined.

7. The method for estimating random errors of pointing and ranging of a spaceborne laser radar according to claim 1, wherein: The determining, based on the longitude, latitude, and elevation of the plurality of laser radar observation data, a first coordinate information sequence, a second coordinate information sequence, and a reference elevation sequence corresponding to the reference grid data includes: Performing coordinate system conversion on the longitudes and latitudes of the plurality of laser radar observation data to obtain a first coordinate information sequence and a second coordinate information sequence corresponding to the plane coordinate system of the reference raster data; The first coordinate information sequence and the second coordinate information sequence are processed using a bilinear interpolation method to obtain the reference elevation sequence.

8. A device for estimating random errors of pointing and ranging of a spaceborne laser radar, characterized in that: The device comprises: A data acquisition module is configured to acquire a geolocation data sequence, reference grid data, and spatial measurement and positioning data corresponding to a spaceborne lidar; the geolocation data sequence includes the longitude, latitude, and elevation of a plurality of lidar observation data; the reference grid data includes a plurality of grid cells, each grid cell including a corresponding cell elevation; the reference grid data and the plurality of lidar observation data share a unified vertical reference; An information determination module, configured to determine a first coordinate information sequence, a second coordinate information sequence, and a reference elevation sequence corresponding to the reference raster data based on the longitude, latitude, and elevation of the plurality of lidar observation data; a module for determining the statistical characteristics of the plane and elevation random errors, for determining the statistical characteristics of the plane and elevation random errors corresponding to the plurality of lidar observation data based on the elevations of the plurality of lidar observation data, the reference elevation sequence corresponding to the reference grid data, the first coordinate information sequence, and the second coordinate information sequence, and using a random error statistical characteristic solution model; The module for determining the statistical characteristics of the random errors of pointing and ranging is used to input the statistical characteristics of the plane and elevation random errors and the spatial measurement and positioning data into the random error estimation model of pointing and ranging to obtain the statistical characteristics of the random errors of pointing and ranging of the spaceborne laser radar.

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 random errors of pointing and ranging of a space-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, wherein the at least one instruction and the at least one program are loaded and executed by a processor to implement the method for estimating random errors of pointing and ranging of a space-borne laser radar as described in any one of claims 1 to 7.

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