A spaceborne lidar pointing and ranging random error estimation method, device, equipment and storage medium

Abstract

The application discloses a kind of spaceborne laser radar pointing and ranging random error estimation method, device, equipment and storage medium, belong to spaceborne laser radar technical field.The method includes: according to the longitude, latitude and elevation of the multiple laser radar observation data obtained, determine the first coordinate information sequence, second coordinate information sequence and reference height sequence of reference grid data;Reference grid data is unified with multiple laser radar observation data vertical datum;According to the elevation, reference height sequence, first coordinate information sequence and second coordinate information sequence, and using random error statistical characteristic solution model, determine the statistical characteristics of plane and elevation random error;The statistical characteristics of plane and elevation random error, space measurement and positioning data are input into pointing and ranging random error estimation model, to obtain the statistical characteristics of pointing and ranging random error.The precision of the technical scheme provided by the application can be improved for pointing and ranging random error estimation.

Description

Technical Field

[0001] This invention relates to the field of spaceborne lidar technology, and in particular to a method, apparatus, device, and storage medium for estimating random errors in pointing and ranging of spaceborne lidar. Background Technology

[0002] Spaceborne lidar is an active remote sensing device based on pulsed laser ranging technology. It boasts advantages such as wide observation range, high measurement accuracy, and all-weather observation, enabling it to play a crucial role in topographic surveying, forestry investigations, and polar and marine environmental monitoring. The core principle behind lidar's high-precision positioning even in long-range spaceborne detection scenarios is to emit laser pulses at a surface target, measure the time required for the pulse to travel from emission to the target's surface and back to the detector, and thus calculate the distance between the lidar and the target. By combining this with satellite position and attitude information, as well as the lidar's onboard position and beam pointing information, high-precision three-dimensional coordinates of the target can be effectively calculated.

[0003] However, spaceborne lidar is inevitably subject to various interferences during actual measurements, resulting in positioning errors. These positioning errors can be mainly categorized into two aspects: pointing error and ranging error. Pointing error refers to the deviation between the actual propagation direction of the lidar laser beam and the expected direction. This deviation may be caused by factors such as the lidar system's installation structure, temperature changes, vibration, and the refraction, scattering, and irregular disturbances of 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 lidar. Several methods have been developed to theoretically model and correct pointing and ranging system errors, essentially eliminating these errors. However, random pointing and ranging errors still exist in spaceborne lidar data, and these errors are crucial indicators of lidar payload system performance. Therefore, estimating these random pointing and ranging errors is of significant guiding importance for payload design. Consequently, a more reliable method for estimating these random pointing and ranging errors is needed. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, device, equipment and storage medium for estimating the random errors of pointing and ranging of spaceborne lidar, which can improve the accuracy and efficiency of estimating the random errors of pointing and ranging of spaceborne lidar, and can effectively evaluate the positioning accuracy of spaceborne lidar data.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] On one hand, the present invention provides a method for estimating random errors in pointing and ranging of a spaceborne lidar, the method comprising:

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

[0009] Based on the longitude, latitude, and elevation of the multiple lidar observation data, determine the first coordinate information sequence, the second coordinate information sequence, and the reference elevation sequence corresponding to the reference grid data;

[0010] Based on the elevation 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 using the random error statistical characteristic solution model, the statistical characteristics of the plane and elevation random errors corresponding to the multiple lidar observation data are determined.

[0011] The statistical characteristics of the plane and elevation random errors, along with the space measurement and positioning data, are input 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.

[0012] In some possible implementations, determining the statistical characteristics of the plane and elevation random errors corresponding to the multiple lidar observation data based on the elevation 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 using a random error statistical characteristic solution model, includes:

[0013] Based on the elevation of the multiple lidar observation data and the reference elevation sequence corresponding to the reference grid data, determine the elevation residual corresponding to each lidar observation data;

[0014] 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 lidar observation data are determined.

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

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

[0017] The random error statistical feature calculation model is constructed in the following manner:

[0018] Based on 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 equation (1):

[0019] (1)

[0020] in, Let δx represent the random errors in plane and elevation corresponding to the i-th lidar observation data; let δy represent the random errors in the X direction of multiple lidar observation data; let δz represent the random errors in the Z direction of multiple lidar observation data. and These represent the slope data in the first direction and the slope data in the second direction corresponding to the i-th lidar observation data, respectively.

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

[0022] (2)

[0023] in, This represents the elevation residual corresponding to the i-th lidar observation data; This represents the statistical mean of the elevation residuals corresponding to multiple lidar observation data. The statistical variance represents the random error in the X-direction of multiple lidar observation data. This represents the statistical variance of the random error in the Y direction of multiple lidar observation data. The variance of the random error in the Z direction of multiple lidar observation data is represented by N, where N represents the number of lidar observation data.

[0024] In some possible implementations, the space measurement and positioning data includes a unit vector indicating the direction of the laser beam 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 random error model for pointing and ranging is established in the following manner:

[0026] Determine the distance measurement value from the spaceborne lidar emission reference point to the target lidar observation point;

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

[0028] (3)

[0029] in, This represents the position vector of the target lidar observation point in the geocentric inertial coordinate system; The vector represents the position vector of the spaceborne lidar emission reference point in the geocentric inertial coordinate system; l represents the distance measured from the spaceborne lidar emission reference point to the target lidar observation point. This represents the unit vector pointing to the laser beam in the satellite's coordinate system; This represents the rotation matrix used to transform the satellite's body coordinate system to the geocentric inertial coordinate system.

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

[0031] (4)

[0032] in, This represents the position vector of the target lidar observation point in the geocentric inertial coordinate system, where there are random errors in pointing and ranging; δ l This indicates random error in distance measurement; Represents the composite rotation matrix pointing to the random error;

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

[0034] (5)

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

[0036] In some possible implementations, the space measurement and positioning data further includes the rotation matrix for transforming from the satellite body coordinate system to the geocentric inertial coordinate system, and the rotation matrix for transforming 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 spaceborne lidar include the statistical variance of the pointing and ranging random errors corresponding to the multiple lidar observation data; the statistical variance of the pointing and ranging random errors includes the statistical variance of the pointing angle random error in the roll direction, the statistical variance of the pointing angle random error in the pitch direction, and the statistical variance of the ranging random error corresponding to the multiple lidar observation data.

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

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

[0039] (6)

[0040] in, The covariance matrix represents the random error of the target lidar observation point in the geocentric inertial coordinate system. Represents the target matrix. , , These represent the projected coordinates of the laser beam pointing in the x, y, and z axes of the satellite's coordinate system, respectively.

[0041] The covariance matrix represents the random errors in pointing and ranging. This represents the statistical variance of the random error in the pointing angle in the roll direction corresponding to multiple lidar observation data. This represents the statistical variance of the random error in the pointing angle in the elevation direction corresponding to multiple lidar observation data. This represents the statistical variance of the random ranging error corresponding to multiple lidar observation data. This represents the cross-covariance between the random errors of the pointing angle in the roll direction and the random errors of the pointing angle in the pitch direction corresponding to multiple lidar observation data. This 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. This represents the cross-covariance between the random errors of the pointing angle in the pitch direction and the random errors of the pointing angle in the roll direction corresponding to multiple lidar observation data. This represents the cross-variance between the random error of the pointing angle and the random error of the ranging direction corresponding to multiple lidar observation data. This represents the cross-variance between the random error in ranging and the random error in pointing angle in the elevation direction corresponding to multiple lidar observation data. This represents the cross-variance between the random error in ranging and the random error in pointing angle in the roll direction corresponding to multiple lidar observation data.

[0042] Based on 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 equation (7):

[0043] (7)

[0044] in, This represents the rotation matrix used to transform the coordinate system of the reference raster data to the geocentric inertial coordinate system. This represents the statistical variance of the random error in the X-direction of multiple lidar observation data. This represents the statistical variance of the random error in the Y direction of multiple lidar observation data. The statistical variance of the random error in the Z direction of multiple lidar observation data;

[0045] Based on equations (6) and (7), the random error estimation model for pointing and ranging 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 equation (9).

[0049] Equation (9) is shown below:

[0050] (9)

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

[0052] Determine the slope data in the first direction and the slope data in the second direction.

[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 grid data based on the longitude, latitude, and elevation of the plurality of lidar observation data includes:

[0054] The longitude and latitude of the multiple lidar observation data are transformed to obtain the first coordinate information sequence and the second coordinate information sequence corresponding to the plane coordinate system where the reference grid data is located.

[0055] The reference high-order sequence is obtained by processing the first coordinate information sequence and the second coordinate information sequence using bilinear interpolation.

[0056] On the other hand, a device for estimating random errors in pointing and ranging of a spaceborne lidar is provided, the device comprising:

[0057] The data acquisition module is used to acquire the geolocation data sequence, reference grid data, and spatial measurement and positioning data corresponding to the spaceborne 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 including the corresponding cell elevation; the reference grid data and the multiple lidar observation data share a common vertical reference.

[0058] The information determination module is used to determine the first coordinate information sequence, the second coordinate information sequence, and the reference elevation sequence corresponding to the reference grid data based on the longitude, latitude, and elevation of the multiple lidar observation data;

[0059] The statistical characteristics determination module for plane and elevation random errors is used to determine the statistical characteristics of plane and elevation random errors corresponding to the multiple lidar observation data based on the elevation 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 by using a random error statistical characteristics solution model.

[0060] The module for determining the statistical characteristics of pointing and ranging random errors is used to input the statistical characteristics of the plane and elevation random errors and the space 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.

[0061] On the other hand, an electronic device is provided, the device including a processor and a memory, the memory storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by the processor to implement the random error estimation method for pointing and ranging of spaceborne lidar as described above.

[0062] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction and at least one program are stored in the computer storage medium, and the at least one instruction and the at least one program are loaded and executed by a processor to implement the random error estimation method for pointing and ranging of spaceborne lidar as described above.

[0063] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0064] In this invention, by acquiring the longitude, latitude, and elevation of multiple lidar observation data corresponding to the spaceborne lidar, a first coordinate information sequence, a second coordinate information sequence, and a reference elevation sequence are determined on the acquired reference grid data corresponding to the longitude, latitude, and elevation of the lidar observation data. Based on a unified vertical reference between the reference grid data and the multiple lidar observation data, and according to the elevation 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 using a random error statistical characteristic solution model, the statistical characteristics of the plane and elevation random errors corresponding to the multiple lidar observation data are determined. Then, the statistical characteristics of the plane and elevation random errors, along with 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 spaceborne lidar, thus realizing the estimation of the pointing and ranging random errors of the spaceborne lidar. The estimation of pointing and ranging random errors can improve the accuracy and efficiency of such estimation for spaceborne lidar, thereby effectively evaluating the positioning accuracy of spaceborne lidar data and providing a reliable foundation for subsequent data processing and applications. Furthermore, the statistical characteristics of the estimated pointing and ranging random errors can provide theoretical guidance for the design and performance evaluation of the lidar payload system. Specifically, based on the statistical characteristics of the pointing and ranging random errors, it can be determined whether the pointing and ranging random errors meet the requirements for the application of lidar observation data. If they do not meet the requirements, corresponding adjustments can be made in the design of the lidar payload system. Moreover, after the spaceborne lidar is launched, its performance can be evaluated by real-time estimation of pointing and ranging random errors, determining whether the lidar observation data is applicable. Attached Figure Description

[0065] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a flowchart illustrating a method for estimating random errors in pointing and ranging of a spaceborne lidar, as provided in an embodiment of the present invention.

[0067] Figure 2 This is a flowchart illustrating the process of determining the statistical characteristics of random errors in plane and elevation corresponding to multiple laser observation data provided in this embodiment of the invention.

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

[0069] Figure 4 This is a graph showing the results of elevation residuals and total elevation random errors provided in 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 for measuring the target provided in an embodiment of the present invention;

[0071] Figure 6 This is a schematic diagram of the structure of a random error estimation device for pointing and ranging of a spaceborne lidar provided in an embodiment of the present invention. Detailed Implementation

[0072] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0073] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0074] In this embodiment of the invention, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0075] Various exemplary embodiments, features, and aspects of the present invention will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0076] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0077] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0078] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without certain specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order to highlight the spirit of the invention.

[0079] Figure 1 This is a flowchart illustrating a method for estimating random errors in pointing and ranging of a spaceborne lidar according to an embodiment of the present invention. This specification provides the method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive methods, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or server product execution, the method can be executed sequentially according to the embodiments or drawings, or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as shown... Figure 1 As shown, the above method may include:

[0080] S101: Acquire the geolocation data sequence, reference grid data, and spatial measurement and positioning data corresponding to the spaceborne 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 including the corresponding cell elevation; the reference grid data and multiple lidar observation data are aligned with a unified vertical reference.

[0081] In one specific embodiment, the geolocation data sequence can be information obtained by spaceborne lidar to describe the spatial location of various geographic entities on Earth; the geolocation data sequence can include the longitude, latitude, and elevation of multiple lidar observation data.

[0082] Multiple lidar observation data can be data obtained by observing multiple target objects through spaceborne lidar. Specifically, lidar observation data can include the location information of the target objects on the Earth's surface, i.e., the longitude, latitude, and elevation of the target objects. Target objects can include the Earth's surface, atmosphere, vegetation, polar regions, and oceans, etc. Optionally, the longitude, latitude, and elevation of multiple lidar observation data can be the longitude, latitude, and elevation of multiple target objects on the Earth's surface. Optionally, the elevation can represent the distance from any point on the target object along the vertical direction to the absolute datum, which is usually chosen as a sea surface as the reference surface. Specifically, the longitude of multiple lidar observation data can be expressed as... , Let be the longitude of the Nth lidar observation data; the latitude of multiple lidar observation data can be expressed as , Let be the latitude of the Nth lidar observation data; the elevation of multiple lidar observation data can be expressed as... , The elevation of the Nth lidar observation data; the geographic location data sequence can be... .

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

[0084] In one specific embodiment, the reference raster data can be geographic space divided into regularly spaced grid cells, and each grid cell can include data representing geographic entities with corresponding attribute values. Specifically, each grid cell can represent a specific area on the target observation object, and the attribute value corresponding to each grid cell can be elevation. Optionally, the reference raster data can be a digital elevation model (DEM) or a digital surface model (DSM). The DEM uses raster data to represent terrain elevation information, and the DSM uses raster data to represent surface elevation information. Specifically, the reference raster data can be determined in conjunction with the actual implementation site.

[0085] In one specific embodiment, the reference grid data has been aligned with the vertical reference of the lidar data to ensure that the lidar data and the reference grid data are consistent in the vertical direction. This allows for the determination of random errors in the vertical direction of the lidar observation data after system error calibration. Optionally, by aligning the vertical reference with the lidar data, the reference grid data can provide an accurate spatial location reference for the lidar data, ensuring that each lidar observation data can be located within the reference grid data.

[0086] S102: Based on the longitude, latitude, and elevation of the multiple lidar observation data, determine the first coordinate information sequence, the second coordinate information sequence, and the reference elevation sequence corresponding to the reference grid data;

[0087] In one specific embodiment, the first coordinate information sequence corresponding to the reference grid data may include multiple first coordinate information items. These multiple first coordinate information items can be the abscissa information of the longitude of multiple lidar observation data in the plane coordinate system corresponding to the reference grid data, and can be... express, This refers to the abscissa information of the longitude of the Nth lidar observation data in the plane coordinate system corresponding to the reference grid data; the second coordinate information sequence corresponding to the reference grid data can include multiple second coordinate information, which can be the ordinate information of the latitude of multiple lidar observation data in the plane coordinate system corresponding to the reference grid data, and can be... express, This refers to the ordinate information of the latitude of the Nth lidar observation data in the plane coordinate system corresponding to the reference grid data. The reference elevation sequence corresponding to the reference grid data can be an elevation information sequence on the reference grid data corresponding to the first coordinate information sequence and the second coordinate information sequence. Specifically, the reference elevation sequence corresponding to the reference grid data can include multiple reference elevations. These multiple reference elevations can be the elevation information corresponding to the abscissa and ordinate information of the longitude and latitude of multiple lidar observation data in the plane coordinate system corresponding to the reference grid data, and can be... express, This refers to the elevation information corresponding to the abscissa and ordinate of the longitude and latitude of the Nth lidar observation data in the plane coordinate system corresponding to the reference grid data. Optionally, the longitude of multiple lidar observation data can correspond to a first coordinate information sequence corresponding to the reference grid data, and the latitude of multiple lidar observation data can correspond to a second coordinate information sequence corresponding to the reference grid data.

[0088] In an optional embodiment, determining the first coordinate information sequence, the second coordinate information sequence, and the reference elevation sequence corresponding to the reference grid data based on the longitude, latitude, and elevation of multiple lidar observation data includes:

[0089] The longitude and latitude of multiple lidar observation data are transformed to obtain the first coordinate information sequence and the second coordinate information sequence corresponding to the plane coordinate system where the reference grid data is located.

[0090] Using bilinear interpolation, the first coordinate information sequence and the second coordinate information sequence are processed to obtain a reference high-order sequence.

[0091] In one specific embodiment, multiple lidar observation data are located within the grid of reference raster data. The longitude and latitude of the multiple lidar observation data are transformed into a first coordinate information sequence and a second coordinate information sequence in the plane coordinate system of the reference raster data. The first and second coordinate information sequences are then processed using bilinear interpolation to obtain a reference height sequence. Optionally, the reference height sequence can be determined based on a bilinear interpolator generated according to the reference raster data. Determine the reference high-order column; the reference raster data can be... .

[0092] In the above embodiments, 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 multiple lidar observation data in the reference grid data provides a data basis for the subsequent determination of the statistical characteristics of plane and elevation random errors, which facilitates the subsequent determination of the statistical characteristics of plane and elevation random errors.

[0093] S103: Based on the elevation of 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 using the random error statistical characteristic solution model, determine the statistical characteristics of the plane and elevation random errors corresponding to multiple lidar observation data.

[0094] In one specific embodiment, the random error statistical characteristic calculation model is used to calculate the statistical characteristics of plane and elevation random errors. Optionally, the statistical characteristics of plane and elevation random errors can reflect the degree of fluctuation of plane and elevation random errors. Optionally, the statistical characteristics can include statistical mean, statistical variance, statistical standard deviation, etc., which can be set according to actual application requirements. Specifically, in this invention, the statistical characteristics of plane and elevation random errors 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 the random errors of lidar observation data in the X and Y directions, and the statistical variance of elevation random errors is the statistical variance of the random errors of lidar observation data in the Z direction.

[0095] In an optional embodiment, Figure 2 This is a flowchart illustrating the process of determining the statistical characteristics of random errors in plane and elevation corresponding to multiple laser observation data provided in this embodiment of the invention; for example... Figure 2 As shown, based on the elevation of 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 using a random error statistical characteristic solution model, the statistical characteristics of the plane and elevation random errors corresponding to multiple lidar observation data are determined, including:

[0096] S201: Determine the elevation residual corresponding to each lidar observation data based on the elevation of multiple lidar observation data and the reference elevation sequence corresponding to the reference grid data;

[0097] S202: Based on the first coordinate information sequence and the second coordinate information sequence corresponding to the reference grid data, determine the first direction slope data and the second direction slope data corresponding to each lidar observation data;

[0098] S203: Input 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 to calculate the statistical features of the plane and elevation random errors corresponding to multiple lidar observation data.

[0099] In one 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 one specific embodiment, the elevation residual for each lidar observation data point is determined by subtracting the corresponding reference elevation sequence from the elevations of multiple lidar observation data points and the reference grid data. The elevation residual sequence for multiple lidar observation data points can be represented as follows: , This represents the elevation residual corresponding to the Nth lidar observation data. Since the lidar observation data has been calibrated for systematic errors, the elevation residuals of multiple lidar observation data can be random errors, specifically expressed as... , This represents the total random elevation error from multiple lidar observation data. This represents the total elevation random error of the Nth lidar observation data.

[0101] In an optional embodiment, the determination 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 equation (9).

[0103] Equation (9) is shown in the following equation:

[0104] (9)

[0105] in, This represents the slope data in the first direction. This indicates the slope data in the second direction; The reference elevation represents the first and second coordinate information of the reference raster data, where x represents the first coordinate information and y represents the second coordinate information. Indicates the raster interval corresponding to the reference raster data;

[0106] Determine the slope data in the first direction and the slope data in the second direction.

[0107] In a specific embodiment, multiple first coordinate information from the first coordinate information sequence and multiple second coordinate information from the second coordinate information sequence can be substituted into equation (9) to determine the first-direction slope data and second-direction slope data corresponding to each lidar observation data. The sequence of first-direction slope data corresponding to multiple lidar observation data can be expressed as follows: , The slope data in the first direction corresponding to the Nth lidar observation data can be represented as follows; the slope data in the second direction corresponding to multiple lidar observation data can be represented as follows. , This represents 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 above-mentioned multiple lidar observation data include the statistical variance of the plane and elevation random errors corresponding to the multiple lidar observation data; the statistical variance of the plane and elevation random errors includes the statistical variance of the random errors of the multiple lidar observation data in the X, Y and Z directions.

[0109] The random error statistical characteristic solution model is constructed in the following manner:

[0110] Based on 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 equation (1):

[0111] (1)

[0112] in, Let δx represent the random errors in plane and elevation corresponding to the i-th lidar observation data; let δy represent the random errors in the X direction of multiple lidar observation data; let δz represent the random errors in the Z direction of multiple lidar observation data. and These represent the slope data in the first direction and the slope data in the second direction corresponding to the i-th lidar observation data, respectively.

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

[0114] (2)

[0115] in, This represents the elevation residual corresponding to the i-th lidar observation data; This represents the statistical mean of the elevation residuals corresponding to multiple lidar observation data. The statistical variance represents the random error in the X-direction of multiple lidar observation data. This represents the statistical variance of the random error in the Y direction of multiple lidar observation data. The variance of the random error in the Z direction of multiple lidar observation data is represented by N, where N represents the number of lidar observation data.

[0116] In one specific embodiment Figure 3This is a schematic diagram of random errors in plane and elevation affected by surface slope provided in an embodiment of the present invention; as shown. Figure 3 As shown in the figure, the influence of surface slope on random errors in both horizontal and vertical directions is illustrated. When a slope exists on the surface, random errors in the horizontal direction are coupled to random errors in the vertical direction due to the slope, causing changes in the random errors in the vertical direction obtained by comparing with the actual terrain (e.g., cell elevations in reference raster data). Figure 3 As shown, the elevation random error is δz. Due to the planar random error in lidar observation data, the elevation random error obtained by comparing it with the actual terrain (e.g., the unit elevation in the reference raster data) is actually the total elevation random error shown in the figure. This includes the elevation random error and the random error in the planar direction coupled with the random error in the elevation direction caused by the surface slope. Therefore, the planar and elevation random errors (total elevation random error) affected by the surface slope can include elevation random error and random error caused by the slope. The random error caused by the slope is the random error in the planar direction coupled with the random error in the elevation 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, by combining 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 slope data in the first direction, the slope data in the second direction, and the elevation random error.

[0117] Furthermore, the aforementioned plane and elevation random error models can be established based on the first and second direction slope data corresponding to the lidar observation data. Combining the first and second direction slope data (surface slope) to establish plane and elevation random error models can improve the accuracy and reliability of the plane and elevation random error models.

[0118] In a specific embodiment, the plane and elevation random errors include plane random errors and elevation random errors. The plane random error is the random error of the lidar observation data in the X and Y directions, and the elevation random error is the random error of the lidar observation data in the Z direction. Specifically, the random variable δ x and δ y Let δ represent the random errors in the lidar observation data in the X and Y directions, respectively. z The random error in the Z direction of lidar observation data is represented by δx, δy, and δz, which all follow a normal distribution and have an expected value of 0, i.e., E(δx) = E(δy) = E(δz) = 0. This can improve the stability and reliability of the random error model.

[0119] In one specific embodiment Figure 4This is a graph showing the results of elevation residuals and total elevation random errors provided in an embodiment of the present invention; as shown. Figure 4 As shown in the figure, the variation trends of the absolute elevation residuals and the standard deviation of the total elevation random error are illustrated. 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 aforementioned random error model. The figure reflects that the variation trends of the elevation residuals and the standard deviation of the total elevation random error are basically consistent. Furthermore, it indicates that since the laser observation data has undergone systematic error calibration, the elevation residuals of multiple lidar observation data can be considered as the total elevation random error. Optionally, the total elevation random residuals 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 aforementioned plane and elevation random error model.

[0120] In one specific embodiment, optionally, This represents the statistical mean of the elevation residuals from multiple lidar observation data points, and can represent the possible residual systematic errors after systematic error calibration. Optional, and Let X and Y represent the statistical variances of the random errors in the X and Y directions, respectively. This represents the statistical variance of the random error 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, i.e., the elevation residuals corresponding to multiple lidar observation data, the statistical variance of the random errors in plane and elevation can be estimated. Then, the method of moments can be used to construct a solution model for the statistical characteristics of random errors.

[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 the random error statistical feature calculation model to calculate the statistical variance of the plane and elevation random errors. Optionally, based on the statistical variance of the plane random error and the statistical variance of the elevation random error, the statistical variance of the plane 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, The statistical variance of the plane and elevation random errors (total elevation random errors) of the i-th lidar observation data is represented. The statistical variance represents the random error in the X-direction of multiple lidar observation data. This represents the statistical variance of the random error in the Y direction of multiple lidar observation data. The statistical variance of the random error in the Z direction of multiple lidar observation data; and These represent the slope data in the first direction and the slope data in the second direction corresponding to the i-th lidar observation data, respectively.

[0125] In the above embodiments, 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 feature calculation model to calculate the statistical features of the plane and elevation random errors corresponding to multiple lidar observation data. This facilitates the determination of the statistical features of subsequent pointing and ranging random errors, thereby realizing the estimation of pointing and ranging random errors of the spaceborne lidar.

[0126] S104: Input the statistical characteristics of plane and elevation random errors and 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 one specific embodiment, the space measurement and positioning data can characterize the data measured by the spaceborne lidar and the data used for spaceborne lidar positioning. Optionally, the space measurement and positioning data may include a rotation matrix for coordinate system transformation, a unit vector indicating the direction of the laser beam in the satellite body coordinate system, and the ranging value from the lidar emission reference point to the target lidar observation point. The rotation matrix for coordinate system transformation is a rotation matrix for transforming from the satellite body coordinate system to the geocentric inertial coordinate system and a rotation matrix for transforming from the coordinate system containing the reference grid data to the geocentric inertial coordinate system. Optionally, the statistical characteristics of pointing and ranging random errors can be the statistical variance of pointing and ranging random errors. The statistical variance of pointing random errors can include the statistical variance of pointing random errors and the statistical variance of ranging random errors. The statistical variance of pointing random errors can be the statistical variance of the random errors of the pointing angle of the lidar observation data in the roll and pitch directions. The pointing and ranging random error estimation model can be a model used for estimating pointing and ranging random errors.

[0128] In a specific embodiment, the statistical variance of pointing and ranging random errors can reflect the magnitude of these random errors, thus effectively evaluating the positioning accuracy of spaceborne lidar data. Specifically, the lower the statistical variance of pointing and ranging random errors, the smaller the pointing and ranging random errors, and the higher the positioning accuracy of the spaceborne lidar data; conversely, the higher the statistical variance of pointing and ranging random errors, the larger the pointing and ranging random errors, and the lower the positioning accuracy of the spaceborne lidar data. Furthermore, the statistical variance of pointing and ranging random errors can provide theoretical guidance for the design and performance evaluation of the lidar payload system. Specifically, based on the statistical characteristics of pointing and ranging random errors, it can be determined whether the pointing and ranging random errors meet the requirements for the application of lidar observation data. If they do not meet the requirements, corresponding adjustments can be made in the design of the lidar payload system. Moreover, after the spaceborne lidar is launched, the statistical variance of pointing and ranging random errors can be obtained by real-time estimation of the pointing and ranging random errors during on-orbit operation, which can then be used to evaluate the performance of the spaceborne lidar and determine whether the application of lidar observation data will be affected.

[0129] In an optional embodiment, the aforementioned space measurement and positioning data includes a unit vector indicating the direction of the laser beam 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 random error model for pointing and ranging is established in the following manner:

[0131] Determine the distance measurement value from the spaceborne lidar emission reference point to the target lidar observation point;

[0132] Based on the ranging value and the unit vector pointing to the laser beam in the satellite body coordinate system, a positioning model for the laser radar observation point of the target is established for the spaceborne laser radar measurement, as shown in equation (3):

[0133] (3)

[0134] in, This represents the position vector of the target lidar observation point in the geocentric inertial coordinate system; represents the position vector of the spaceborne lidar emission reference point in the geocentric inertial coordinate system; l represents the distance measured from the spaceborne lidar emission reference point to the target lidar observation point; This represents the unit vector pointing to the laser beam in the satellite's body coordinate system. This represents the rotation matrix used to transform the satellite's 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 equation (4):

[0136] (4)

[0137] in, δ represents the position vector of the target lidar observation point in the geocentric inertial coordinate system, which has random errors in pointing and ranging; l This indicates random error in distance measurement; Represents the composite rotation matrix pointing to the random error;

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

[0139] (5)

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

[0141] In one specific embodiment, the ranging value is the distance from the satellite-borne lidar's emission reference point to the target lidar's observation point. Optionally, the emission reference point can be a fixed point on the satellite-borne lidar 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 lidar observation point after the laser beam is emitted from the emission reference point to the target lidar observation point, reflected back after reaching the target lidar observation point, and received by the satellite-borne lidar. Optionally, the ranging value conversion needs to consider the satellite coordinate system and the geographic coordinate system. To achieve the corresponding conversion and matching, a rotation matrix for coordinate system conversion can be used, which can 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 a rotation matrix used for coordinate system transformation.

[0143] In one specific embodiment, the positioning model with random pointing and ranging errors is established based on the systematic errors of lidar observation data (ranging and pointing) and the fact that atmospheric delay and solid tides have been corrected.

[0144] In one specific embodiment , , , These represent the projected coordinates of the target lidar observation point in the x, y, and z axes of the geocentric inertial coordinate system, respectively. , , , This represents the projected coordinates of the spaceborne lidar's emission reference point in the x, y, and z axes of the geocentric inertial coordinate system. Ranging random error. , The identity matrix represents the statistical variance of the ranging random error corresponding to the i-th lidar observation data. .

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

[0146] ;

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

[0148] In one specific embodiment Figure 5 This is a three-dimensional positioning diagram of the laser radar observation point for measuring the target provided in an embodiment of the present invention; as shown below. Figure 5 As shown, the surface target is used as the observation point of the target lidar. The Earth-Centered Inertial Frame (ECI) is set with the Earth's center of mass as the origin O, including the X-axis X. ECI Y-axis ECI and Z-axis Z ECI The Satellite Body Fixed (SBF) coordinate system uses the satellite as its origin O and includes the X-axis X... SBF Y-axisSBF and Z-axis Z SBF It can combine the geocentric inertial coordinate system and the satellite body coordinate system to perform three-dimensional positioning of the target lidar observation point by the spaceborne lidar.

[0149] In the above embodiments, by establishing the random error model of the pointing and ranging of the spaceborne lidar, it is convenient to establish the random error estimation model of the pointing and ranging, thereby enabling the estimation of the random error of the pointing and ranging of the spaceborne lidar.

[0150] In an optional embodiment, the space measurement and positioning data further includes a rotation matrix for transforming from the satellite body coordinate system to the geocentric inertial coordinate system, and a rotation matrix for transforming 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 spaceborne lidar include the statistical variance of the pointing and ranging random errors corresponding to multiple lidar observation data; the statistical variance of the pointing and ranging random errors includes the statistical variance of the pointing angle random error in the roll direction, the statistical variance of the pointing angle random error in the pitch direction, and the statistical variance of the ranging random error corresponding to multiple lidar observation data;

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

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

[0153] (6)

[0154] in, The covariance matrix represents the random error of the target lidar observation point in the geocentric inertial coordinate system. Represents the target matrix. , , These represent the projected coordinates of the laser beam pointing in the x, y, and z axes of the satellite's coordinate system, respectively.

[0155] The covariance matrix represents the random errors in pointing and ranging. This represents the statistical variance of the random error in the pointing angle in the roll direction corresponding to multiple lidar observation data. This represents the statistical variance of the random error in the pointing angle in the elevation direction corresponding to multiple lidar observation data. This represents the statistical variance of the random ranging error corresponding to multiple lidar observation data. This represents the cross-covariance between the random errors of the pointing angle in the roll direction and the random errors of the pointing angle in the pitch direction corresponding to multiple lidar observation data. This 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. This represents the cross-covariance between the random errors of the pointing angle in the pitch direction and the random errors of the pointing angle in the roll direction corresponding to multiple lidar observation data. This represents the cross-variance between the random error of the pointing angle and the random error of the ranging direction corresponding to multiple lidar observation data. This represents the cross-variance between the random error in ranging and the random error in pointing angle in the elevation direction corresponding to multiple lidar observation data. This represents the cross-variance between the random error in ranging and the random error in pointing angle in the roll direction corresponding to multiple lidar observation data.

[0156] Based on the statistical characteristics of 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 equation (7):

[0157] (7)

[0158] in, This represents the rotation matrix used to transform the coordinate system of the reference raster data to the geocentric inertial coordinate system. This represents the statistical variance of the random error in the X-direction of multiple lidar observation data. This represents the statistical variance of the random error in the Y direction of multiple lidar observation data. The statistical variance of the random error in the Z direction of multiple lidar observation data;

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

[0160] (8).

[0161] In one specific embodiment, the covariance matrix of pointing and ranging random errors The diagonal elements are the statistical variances of the pointing and ranging random errors, and the target matrix. It has no practical significance; it's used to simplify formulas.

[0162] In one 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 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] As can be seen from the technical solutions provided in the embodiments of this specification above, this specification uses the acquired geolocation data sequence corresponding to the spaceborne lidar, which has undergone systematic error calibration and atmospheric delay and solid tidal correction, i.e., the longitude, latitude, and elevation of multiple lidar observation data, to determine the first coordinate information sequence, the second coordinate information sequence, and the reference altitude sequence on the acquired reference grid data corresponding to the longitude, latitude, and elevation of the lidar observation data. Based on a unified vertical reference between the reference grid data and the multiple lidar observation data, and according to the elevation of the multiple lidar observation data, the reference altitude 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 statistical characteristics of the plane and elevation random errors corresponding to the multiple lidar observation data are determined. Then, the statistical characteristics of the plane and elevation random errors, along with the acquired spatial measurement and positioning data, are input into the pointing and ranging random error estimation model to obtain the corresponding spaceborne lidar... The statistical characteristics of pointing and ranging random errors enable the estimation of these errors in spaceborne lidar, improving both the accuracy and efficiency of such estimation. This allows for effective evaluation of the positioning accuracy of spaceborne lidar data, providing a reliable foundation for subsequent data processing and applications. Furthermore, the estimated statistical characteristics of pointing and ranging random errors provide theoretical guidance for the design and performance evaluation of the lidar payload system. Specifically, based on these characteristics, it can be determined whether the pointing and ranging random errors meet the requirements for the application of lidar observation data. If not, appropriate adjustments can be made to the lidar payload system design. Moreover, after the spaceborne lidar is launched, real-time estimation of pointing and ranging random errors allows for the evaluation of its performance and the determination of the applicability of the lidar observation data.

[0164] This invention also provides a device for estimating random errors in pointing and ranging of a spaceborne lidar. Figure 6 This is a schematic diagram of a random error estimation device for pointing and ranging of a spaceborne lidar provided in an embodiment of the present invention; as shown below. Figure 6 As shown, the above-mentioned device includes:

[0165] The data acquisition module 610 is used to acquire the geolocation data sequence, reference grid data, and spatial measurement and positioning data corresponding to the spaceborne 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 including the corresponding cell elevation; the reference grid data and the multiple lidar observation data share a common vertical reference.

[0166] The information determination module 620 is used to determine the first coordinate information sequence, the second coordinate information sequence, and the reference elevation sequence corresponding to the reference grid data based on the longitude, latitude, and elevation of the multiple lidar observation data.

[0167] The statistical characteristics determination module 630 for plane and elevation random errors is used to determine the statistical characteristics of plane and elevation random errors corresponding to the multiple lidar observation data based on the elevation 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 by using a random error statistical characteristics solution model.

[0168] The statistical characteristics determination module 640 for pointing and ranging random errors is used to input the statistical characteristics of the plane and elevation random errors and the space 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.

[0169] In an optional embodiment, the statistical characteristic determination module 630 for plane and elevation random errors includes:

[0170] The elevation residual determination unit is used to determine the elevation residual corresponding to each lidar observation data based on the elevation of the multiple lidar observation data and the reference elevation sequence corresponding to the reference grid data.

[0171] The slope data determination unit is used to determine 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.

[0172] The statistical feature determination unit for 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 feature calculation model, and calculate the statistical features of 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 plurality of lidar observation data include the statistical variance of the plane and elevation random errors corresponding to the plurality of lidar observation data; the statistical variance of the plane and elevation random errors includes the statistical variance of the random errors of the plurality of lidar observation data in the X, Y and Z directions;

[0174] The device further includes: a random error statistical feature calculation model construction module, used for...

[0175] Based on 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 equation (1):

[0176] (1)

[0177] in, Let δx represent the random errors in plane and elevation corresponding to the i-th lidar observation data; let δy represent the random errors in the X direction of multiple lidar observation data; let δz represent the random errors in the Z direction of multiple lidar observation data. and These represent the slope data in the first direction and the slope data in the second direction corresponding to the i-th lidar observation data, respectively.

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

[0179] (2)

[0180] in, This represents the elevation residual corresponding to the i-th lidar observation data; This represents the statistical mean of the elevation residuals corresponding to multiple lidar observation data. The statistical variance represents the random error in the X-direction of multiple lidar observation data. This represents the statistical variance of the random error in the Y direction of multiple lidar observation data. The variance of the random error in the Z direction of multiple lidar observation data is represented by N, where N represents the number of lidar observation data.

[0181] In an optional embodiment, the space measurement and positioning data includes a unit vector indicating the direction of the laser beam 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 further includes: a pointing and ranging random error model establishment module, used for...

[0183] Determine the distance measurement value from the spaceborne lidar emission reference point to the target lidar observation point;

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

[0185] (3)

[0186] in, This represents the position vector of the target lidar observation point in the geocentric inertial coordinate system; The vector represents the position vector of the spaceborne lidar emission reference point in the geocentric inertial coordinate system; l represents the distance measured from the spaceborne lidar emission reference point to the target lidar observation point. This represents the unit vector pointing to the laser beam in the satellite's coordinate system; This represents the rotation matrix used to transform the satellite's body coordinate system to the geocentric inertial coordinate system.

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

[0188] (4)

[0189] in, This represents the position vector of the target lidar observation point in the geocentric inertial coordinate system, where there are random errors in pointing and ranging; δ l This indicates random error in distance measurement; Represents the composite rotation matrix pointing to the random error;

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

[0191] (5)

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

[0193] In an optional embodiment, the space measurement and positioning data further includes the rotation matrix for transforming from the satellite body coordinate system to the geocentric inertial coordinate system and the rotation matrix for transforming 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 spaceborne lidar include the statistical variance of the pointing and ranging random errors corresponding to the multiple lidar observation data; the statistical variance of the pointing and ranging random errors includes the statistical variance of the pointing angle random error in the roll direction, the statistical variance of the pointing angle random error in the pitch direction, and the statistical variance of the ranging random error corresponding to the multiple lidar observation data;

[0194] The device further includes: a pointing and ranging random error estimation and establishment module, used for...

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

[0196] (6)

[0197] in, The covariance matrix represents the random error of the target lidar observation point in the geocentric inertial coordinate system. Represents the target matrix. , , These represent the projected coordinates of the laser beam pointing in the x, y, and z axes of the satellite's coordinate system, respectively.

[0198] The covariance matrix represents the random errors in pointing and ranging. This represents the statistical variance of the random error in the pointing angle in the roll direction corresponding to multiple lidar observation data. This represents the statistical variance of the random error in the pointing angle in the elevation direction corresponding to multiple lidar observation data. This represents the statistical variance of the random ranging error corresponding to multiple lidar observation data. This represents the cross-covariance between the random errors of the pointing angle in the roll direction and the random errors of the pointing angle in the pitch direction corresponding to multiple lidar observation data. This 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. This represents the cross-covariance between the random errors of the pointing angle in the pitch direction and the random errors of the pointing angle in the roll direction corresponding to multiple lidar observation data. This represents the cross-variance between the random error of the pointing angle and the random error of the ranging direction corresponding to multiple lidar observation data. This represents the cross-variance between the random error in ranging and the random error in pointing angle in the elevation direction corresponding to multiple lidar observation data. This represents the cross-variance between the random error in ranging and the random error in pointing angle in the roll direction corresponding to multiple lidar observation data.

[0199] Based on 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 equation (7):

[0200] (7)

[0201] in, This represents the rotation matrix used to transform the coordinate system of the reference raster data to the geocentric inertial coordinate system. This represents the statistical variance of the random error in the X-direction of multiple lidar observation data. This represents the statistical variance of the random error in the Y direction of multiple lidar observation data. The statistical variance of the random error in the Z direction of multiple lidar observation data;

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

[0203] (8).

[0204] In an optional embodiment, the slope data determination unit is specifically used for

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

[0206] Equation (9) is shown below:

[0207] (9)

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

[0209] Determine the slope data in the first direction and the slope data in the second direction.

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

[0211] The coordinate information sequence determination unit is used to perform coordinate system transformation on the longitude and latitude of the multiple lidar observation data to obtain the first coordinate information sequence and the second coordinate information sequence corresponding to the plane coordinate system where the reference grid data is located.

[0212] The reference high-order sequence determination unit is used to process the first coordinate information sequence and the second coordinate information sequence using bilinear interpolation to obtain the reference high-order sequence.

[0213] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0214] This invention also provides an electronic device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, a code set, or an instruction set. 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 random error estimation method for pointing and ranging of a spaceborne lidar as described in any of the method embodiments.

[0215] Embodiments of the present invention also provide a computer storage medium, which can be disposed in a server to store at least one instruction, at least one program, code set, or instruction set for implementing the method embodiments. 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 random error estimation method for pointing and ranging of spaceborne lidar as described in any of the method embodiments.

[0216] Optionally, in embodiments of the present invention, the storage medium may be located at at least one of a plurality of network servers in a computer network. Optionally, in embodiments of the present invention, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0217] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0218] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more flowcharts and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0219] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more flowcharts and / or boxes Figure 1 The function specified in one or more boxes.

[0220] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more flowcharts and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0221] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0222] Finally, it should be noted that the embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for estimating random pointing and ranging errors of a space-borne lidar, characterized in that, The method comprises: acquiring a geographic positioning data sequence corresponding to a spaceborne lidar, reference grid data, and spatial measurement and positioning data; the geographic positioning data sequence comprises longitude, latitude, and elevation of a plurality of lidar observation data; the reference grid data comprises a plurality of grid cells, each grid cell comprising a corresponding cell elevation; the reference grid data is unified with a vertical datum with the plurality of lidar observation data; determining a first coordinate information sequence, a second coordinate information sequence, and a reference elevation sequence corresponding to the reference grid data according to the longitude, latitude, and elevation of the plurality of lidar observation data; determining statistical characteristics of planar and elevation random errors corresponding to the plurality of lidar observation data according to the elevation 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 solving model; inputting the statistical characteristics of the planar and elevation random errors, the spatial measurement and positioning data into a pointing and ranging random error estimation model to obtain statistical characteristics of pointing and ranging random errors of the spaceborne lidar.

2. The method of claim 1, wherein, The determining of the statistical characteristics of the planar and elevation random errors corresponding to the plurality of lidar observation data according to the elevation 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 the random error statistical characteristic solving model comprises: determining an elevation residual corresponding to each lidar observation data according to the elevation of the plurality of lidar observation data and the reference elevation sequence corresponding to the reference grid data; determining first directional slope data and second directional slope data corresponding to the each lidar observation data based on the first coordinate information sequence and the second coordinate information sequence corresponding to the reference grid data; inputting the elevation residual, the first directional slope data, and the second directional slope data corresponding to the each lidar observation data into a random error statistical characteristic solving model to obtain the statistical characteristics of the planar and elevation random errors corresponding to the plurality of lidar observation data.

3. The method of claim 2, wherein, The statistical characteristics of the planar and elevation random errors corresponding to the plurality of lidar observation data comprise statistical variances of the planar and elevation random errors corresponding to the plurality of lidar observation data; the statistical variances of the planar and elevation random errors comprise statistical variances of random errors of the plurality of lidar observation data in X, Y, and Z directions; The random error statistical characteristic solving model is constructed in the following manner: establishing a planar and elevation random error model according to the first directional slope data and the second directional slope data corresponding to the each lidar observation data, as shown in formula (1): ； （1） wherein, represents the planar and elevation random errors corresponding to the i-th laser radar observation data; the random variable δx represents the random errors of the plurality of laser radar observation data in the X direction; δy represents the random errors of the plurality of laser radar observation data in the Y direction, and the random variable δz represents the random errors of the plurality of laser radar 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; constructing a random error statistical characteristic solving model based on the planar and elevation random error model, in combination with the elevation residual, the first directional slope data, and the second directional slope data corresponding to the each lidar observation data, as shown in formula (2): ；（2） wherein, represents the elevation residual corresponding to the i-th lidar observation data; represents the statistical mean of the elevation residuals corresponding to the plurality of lidar observation data; represents the statistical variance of the random errors in the X direction of the plurality of lidar observation data, represents the statistical variance of the random errors in the Y direction of the plurality of lidar observation data, represents the statistical variance of the random errors in the Z direction of the plurality of lidar observation data, and N represents the number of the plurality of lidar observation data.

4. The space-borne lidar pointing and ranging random error estimation method of claim 1, wherein, The space measurement and positioning data include a unit vector of a laser beam pointing in a satellite body coordinate system; and the pointing and ranging random error estimation model is based on a pointing and ranging random error model. The pointing and ranging random error model is established in the following manner: A ranging value from a reference point of the spaceborne lidar to an observation point of the target lidar is determined; 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 spaceborne lidar to measure the observation point of the target lidar is established, as shown in equation (3): ；（3） wherein, represents a position vector of the target laser radar observation point in the geocentric inertial coordinate system; represents a position vector of the satellite-borne laser radar emission reference point in the geocentric inertial coordinate system; and l represents a ranging value from the satellite-borne laser radar emission reference point to the target laser radar observation point; represents a unit vector of the laser beam pointing in the satellite body coordinate system; represents a rotation matrix converted from the satellite body coordinate system to the geocentric inertial coordinate system; Based on the positioning model, a positioning model with pointing and ranging random errors is established, as shown in equation (4): ；（4） wherein, represents the position vector of the target laser radar observation point in the geocentric inertial coordinate system with pointing and ranging random errors; δ l represents the ranging random error; represents the combined rotation matrix of the pointing random error; In combination with equation (3) and equation (4), the pointing and ranging random error model is established, as shown in equation (5): ；（5） wherein, represents a positioning random error of the target laser radar observation point in the earth-centered inertial coordinate system; is a unit matrix; represents a rotation matrix including a pointing angle random error in the roll direction and the pitch direction, represents a pointing angle random error in the roll direction corresponding to the plurality of laser radar observation data, represents a pointing angle random error in the pitch direction corresponding to the plurality of laser radar observation data.

5. The space-borne lidar pointing and ranging random error estimation method according to claim 4, wherein, The space measurement and positioning data further include a rotation matrix converted from the satellite body coordinate system to an earth-centered inertial coordinate system, and a rotation matrix converted from a coordinate system in which the reference grid data is located to the earth-centered inertial coordinate system; statistical characteristics of the pointing and ranging random errors of the spaceborne lidar include statistical variances of the pointing and ranging random errors corresponding to the plurality of lidar observation data; and the statistical variances of the pointing and ranging random errors include statistical variances of a roll direction pointing angle random error, statistical variances of a pitch direction pointing angle random error, and statistical variances of a ranging random error corresponding to the plurality of lidar observation data; The pointing and ranging random error estimation model is established in the following manner: According to the pointing and ranging random error model shown in formula (5), let , , indicate the pointing and ranging random errors corresponding to the plurality of laser radar observation data, and determine the covariance matrix of the positioning random error of the target laser radar observation point in the Earth-Centered Inertial coordinate system, as shown in formula (6): ；（6） wherein, denotes a covariance matrix of the positioning random error of the target laser radar observation point in the earth-centered inertial coordinate system; denotes a target matrix, , , denote the projection coordinates of the laser beam pointing in the x, y, z axis direction of the satellite body coordinate system, respectively; a covariance matrix representing pointing and ranging random errors, a statistical variance representing random errors of the pointing angle in the roll direction corresponding to the plurality of lidar observation data, a statistical variance representing random errors of the pointing angle in the pitch direction corresponding to the plurality of lidar observation data, a statistical variance representing random errors of the ranging corresponding to the plurality of lidar observation data; a cross-covariance between the random errors of the pointing angle in the roll direction and the random errors of the pointing angle in the pitch direction corresponding to the plurality of lidar observation data, a cross-covariance between the random errors of the pointing angle in the roll direction and the random errors of the ranging corresponding to the plurality of lidar observation data, a cross-covariance between the random errors of the pointing angle in the pitch direction and the random errors of the pointing angle in the roll direction corresponding to the plurality of lidar observation data, a cross-covariance between the random errors of the pointing angle in the pitch direction and the random errors of the ranging corresponding to the plurality of lidar observation data, a cross-covariance between the random errors of the ranging and the random errors of the pointing angle in the pitch direction corresponding to the plurality of lidar observation data, a cross-covariance between the random errors of the ranging and the random errors of the pointing angle in the roll direction corresponding to the plurality of lidar observation data; According to the statistical characteristics of the plane and elevation random errors, a covariance matrix of positioning random errors of the observation point of the target lidar in the earth-centered inertial coordinate system is determined, as shown in equation (7): ；（7） wherein, represents a rotation matrix converted from the coordinate system where the reference grid data is located to the geocentric inertial coordinate system; represents a statistical variance of X-direction random error of the plurality of laser radar observation data, represents a statistical variance of Y-direction random error of the plurality of laser radar observation data, represents a statistical variance of Z-direction random error of the plurality of laser radar observation data; Based on equation (6) and equation (7), the pointing and ranging random error estimation model is established, as shown in equation (8): （8）。 6. The space-borne lidar pointing and ranging random error estimation method of claim 2, wherein, The determination 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: Based on the first coordinate information sequence, the second coordinate information sequence, and equation (9), The equation (9) is as shown in the following equation: ；（9） wherein, represents the first direction slope data, represents the second direction slope data; represents the reference elevation corresponding to the first coordinate information and the second coordinate information of the reference grid data, x represents the first coordinate information, and y represents the second coordinate information; represents the grid interval corresponding to the reference grid data; The first direction slope data and the second direction slope data are determined.

7. The space-borne lidar pointing and ranging random error estimation method of claim 1, wherein, The determination of the first coordinate information sequence, the second coordinate information sequence, and the reference elevation sequence corresponding to the reference grid data based on the longitude, the latitude, and the elevation of the plurality of lidar observation data includes: The longitude and the latitude of the plurality of lidar observation data are converted in a coordinate system to obtain the first coordinate information sequence and the second coordinate information sequence corresponding to a plane coordinate system in which the reference grid data is located; The first coordinate information sequence and the second coordinate information sequence are processed by using a bilinear interpolation method to obtain the reference elevation sequence.

8. A space-borne lidar pointing and ranging random error estimation apparatus, characterized by, The device includes: The data acquisition module is configured to acquire a geographic positioning data sequence corresponding to the spaceborne lidar, reference grid data, and spatial measurement and positioning data. The geographic positioning data sequence includes longitude, latitude, and elevation of a plurality of lidar observation data. The reference grid data includes a plurality of grid cells, each of which includes a corresponding cell elevation. The reference grid data and the plurality of lidar observation data are unified in a vertical datum. The information determination module is configured to determine a first coordinate information sequence, a second coordinate information sequence, and a reference elevation sequence corresponding to the reference grid data according to the longitude, latitude, and elevation of the plurality of lidar observation data. The plane and elevation random error statistical feature determination module is configured to determine statistical features of plane and elevation random errors corresponding to the plurality of lidar observation data according to the elevation 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 by using a random error statistical feature solving model. The pointing and ranging random error statistical feature determination module is configured to input the statistical features 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 statistical features of pointing and ranging random errors of the spaceborne lidar.

9. An electronic device, the device comprising a processor and a memory, the memory storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by the processor to implement the spaceborne lidar pointing and ranging random error estimation method according to any one of claims 1 to 7.

10. A computer storage medium, the computer storage medium storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by a processor to implement the spaceborne lidar pointing and ranging random error estimation method according to any one of claims 1 to 7.

Images