Track error compensation method, device, equipment and storage medium

Abstract

The application discloses a kind of track error compensation method, device, equipment and storage medium, it is related to satellite remote sensing data processing technical field, comprising: obtaining radar original image parameter file and extracting orbit state vector sequence;Eliminate first and last unreliable state vector, obtain reliable state vector interval;Local polynomial fitting compensation calculation is carried out to reliable state vector interval, and initial error compensation value is obtained;Based on kinematics equation, end trajectory extrapolation recovery is carried out, and complete compensation sequence is obtained;Uniformity constraint is applied to complete compensation sequence, and target error compensation state vector is obtained;Parameter file is updated according to target error compensation state vector, to complete track error compensation.The application can effectively inhibit non-physical high-frequency disturbance in orbit data without relying on external precise orbit product, and eliminate the problem of end instability.

Description

Technical Field

[0001] This application relates to the field of satellite remote sensing data processing technology, and in particular to an orbital error compensation method, device, equipment and storage medium. Background Technology

[0002] The orbital state vector recorded in the synthetic aperture radar image parameter file is a key input for interferometric phase simulation and surface deformation inversion. Currently, obtaining high-precision orbital parameters mainly relies on external precision orbital products. These products are generated by ground-based telemetry and control systems through complex orbit determination calculations. The release of these products has a time lag of several hours to several weeks, and they depend on data from ground tracking stations, which forces the interruption of real-time or near-real-time processing.

[0003] In existing technologies, conventional orbit interpolation methods are only used to densify time sampling points and cannot correct the errors of the original state vector itself; traditional low-pass filtering smooths each component independently and lacks kinematic constraints between position and velocity, so the velocity and position derivatives may not be consistent after filtering; conventional local polynomial filtering uses a fixed window, which makes it difficult to balance noise suppression and orbit trend preservation.

[0004] Therefore, how to eliminate errors in the existing orbital state vectors in spaceborne synthetic aperture radar image parameter files without relying on external precision orbital products is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The main objective of this application is to provide a method, apparatus, device, and storage medium for orbital error compensation, aiming to solve the technical problem of how to eliminate errors in existing orbital state vectors in spaceborne synthetic aperture radar image parameter files without relying on external precision orbital products.

[0006] To achieve the above objectives, this application provides a track error compensation method, comprising: Obtain the raw radar image parameter file; Extract the orbital state vector sequence from the original radar image parameter file; Based on the orbital state vector sequence, unreliable state vectors at the beginning and end are removed to obtain a reliable state vector interval composed of the middle segment state vectors. Compensation calculations are performed based on the reliable state vector interval to obtain the initial error compensation value at each time step; Based on the initial error compensation value, the trajectory is extrapolated and recovered to obtain the complete compensation sequence; The original radar image parameter file is updated according to the complete compensation sequence to complete the orbital error compensation.

[0007] In one embodiment, updating the original radar image parameter file according to the complete compensation sequence to complete orbital error compensation includes: Based on the complete compensation sequence, consistency constraint processing is performed to obtain the target error compensation state vector; The target error compensation state vector is written back to the original radar image parameter file to obtain the target parameter file, thereby completing the orbital error compensation.

[0008] In one embodiment, the step of extracting the orbital state vector sequence based on the original radar image parameter file includes: Based on the original radar image parameter file, the orbital state vector field region is obtained by positioning. Information is obtained by acquiring information from the orbital state vector field region to obtain orbital state vector information; The validity of the orbital state vector information is verified to obtain the orbital state vector sequence.

[0009] In one embodiment, the step of removing unreliable state vectors at the beginning and end of the orbital state vector sequence to obtain a reliable state vector interval composed of intermediate state vectors includes: Obtain the preset first and last removal parameters; The track state vector is removed according to the first and last removal parameters to obtain the middle segment state vector; The validity is determined based on the intermediate segment state vector to obtain the reliable state vector interval.

[0010] In one embodiment, the step of performing compensation calculations based on the reliable state vector interval to obtain the initial error compensation value at each time step includes: The vector sequence of the reliable state vector interval is split according to the dimension to obtain a one-dimensional component sequence; Candidate windows are set based on the number of reliable state vectors to obtain a candidate window set; Local polynomial fitting is performed based on the one-dimensional component sequence and the candidate window set to obtain smoothed predicted values ​​under each candidate window. Based on the smoothed estimates under each candidate window, the optimal window is selected to obtain the smoothed predicted value under the optimal window. Based on the smoothed prediction value under the optimal window, the initial error compensation value at each time step is obtained.

[0011] In one embodiment, the step of extrapolating the trajectory based on the initial error compensation value to obtain the complete compensation sequence includes: Based on the initial error compensation value, parameter calculations are performed to obtain the end motion state parameters; Based on the end motion state parameters, Taylor series is used to extrapolate the front trajectory to obtain the front extrapolated state vector. Based on the end motion state parameters, Taylor series is used to extrapolate the rear trajectory to obtain the rear extrapolated state vector. Based on the front-end extrapolation state vector and the back-end extrapolation state vector, a complete compensation sequence is obtained.

[0012] In one embodiment, the step of performing consistency constraint processing based on the complete compensation sequence to obtain the target error compensation state vector includes: The position differential velocity component is obtained by performing differential calculation on the position component of the complete compensation sequence. The target velocity component is obtained by weighted fusion of the velocity component of the complete compensation sequence and the position differential velocity component. Based on the target velocity and position components, the target error compensation state vector is obtained.

[0013] In addition, to achieve the above objectives, this application also provides a track error compensation device, comprising: The acquisition module is used to acquire raw radar image parameter files; The extraction module is used to extract the orbital state vector sequence based on the original radar image parameter file; The elimination module is used to eliminate unreliable state vectors at the beginning and end of the orbital state vector sequence to obtain a reliable state vector interval composed of the middle segment state vectors. The calculation module is used to perform compensation calculations based on the reliable state vector interval to obtain the initial error compensation value at each time step. The extrapolation module is used to extrapolate the trajectory based on the initial error compensation value to obtain the complete compensation sequence; The update module is used to update the original radar image parameter file according to the complete compensation sequence to complete the orbital error compensation.

[0014] In addition, to achieve the above objectives, this application also proposes a track error compensation device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the track error compensation method as described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the track error compensation method described above.

[0016] This application provides a method for orbital error compensation. It employs a technique of acquiring the original radar image parameter file and extracting the orbital state vector sequence, removing unreliable state vectors at the beginning and end points, extrapolating and recovering the end trajectory, and applying consistency constraints to the complete compensation sequence. Finally, it updates the parameter file based on the target error compensation state vector. This method solves the problem in existing technologies of simultaneously eliminating multiple types of errors, such as local noise, end instability, and position-velocity inconsistency, from existing orbital state vectors in spaceborne synthetic aperture radar image parameter files without relying on external precision orbital products. Compared with existing technologies, this invention restricts the filtering modeling to the reliable interval after removing the beginning and end points, avoiding the propagation of end-point errors into the filtering model. Furthermore, by extrapolating and recovering end-point data and applying consistency constraints, it eliminates end-point instability and physical inconsistency errors, thus achieving the effect of simultaneously eliminating local noise, end-point instability, and physical inconsistency errors without relying on external precision orbital products. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the first embodiment of the track error compensation method of this application; Figure 2 This is a flowchart illustrating the second embodiment of the track error compensation method of this application; Figure 3 This is a flowchart illustrating the third embodiment of the track error compensation method of this application; Figure 4 This is a structural block diagram of the first embodiment of the track error compensation device of this application; Figure 5 This is a schematic diagram of the structure of the track error compensation device in the hardware operating environment involved in the embodiments of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application is as follows: First, obtain the original radar image parameter file; extract the orbital state vector sequence based on the original radar image parameter file; remove unreliable state vectors at the beginning and end of the orbital state vector sequence to obtain a reliable state vector interval composed of the intermediate state vectors; perform compensation calculation based on the reliable state vector interval to obtain the initial error compensation value at each time moment; perform trajectory extrapolation recovery based on the initial error compensation value to obtain a complete compensation sequence; perform consistency constraint processing based on the complete compensation sequence to obtain the target error compensation state vector; finally, update the original radar image parameter file based on the target error compensation state vector to complete the orbital error compensation.

[0024] In this embodiment, for ease of description, the following description uses a computing device as the execution subject.

[0025] Because existing conventional orbit interpolation methods only apply to encrypted time sampling points and cannot correct errors in the original state vector itself; traditional low-pass filtering smooths each component independently, lacking kinematic constraints between position and velocity, potentially resulting in inconsistencies between the velocity and position derivatives after filtering; and conventional local polynomial filtering uses a fixed window, making it difficult to balance noise suppression and orbit trend preservation. Therefore, how to eliminate errors in existing orbit state vectors in spaceborne synthetic aperture radar image parameter files without relying on external precision orbit products is a pressing technical problem that needs to be solved in this field.

[0026] This application achieves multi-objective collaborative optimization of limited state vector data by removing unreliable data at the beginning and end, adaptive local polynomial fitting, end extrapolation recovery based on Newton's kinematic equations, and weighted fusion of position and velocity consistency. This solves the problem that existing technologies cannot simultaneously eliminate various types of errors such as local noise, end instability, and position and velocity inconsistency without relying on external precision track products. It achieves the technical effect of significantly improving the smoothness, continuity, and physical consistency of track state vectors based solely on the information in the SAR image parameter file itself.

[0027] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or track error compensation program capable of performing the above functions. The following description uses a computing device as an example to illustrate this embodiment and the subsequent embodiments.

[0028] Based on this, this application proposes a track error compensation method according to a first embodiment. Please refer to [link / reference]. Figure 1 The track error compensation method includes steps S10 to S60: Step S10: Obtain the original radar image parameter file.

[0029] Understandably, during the preprocessing stage of spaceborne synthetic aperture radar (SAR) data, as the data entry point for subsequent orbital error compensation processing, in scenarios such as single-scene image processing, batch automated production, and emergency rapid processing, the terminal computer, local workstation, or server reads the original image parameter file from the input data to provide a data source for subsequent orbital state vector extraction and error compensation calculation.

[0030] It should be noted that the radar raw image parameter file refers to the metadata file that accompanies the single-view complex data or multi-view intensity data of the spaceborne synthetic aperture radar. It is used to record the system parameters, geometric parameters and orbital parameters of the radar sensor during imaging. The file format varies depending on the satellite platform and includes, but is not limited to, XML format, text format and dedicated binary format. The orbital state vector sequence is composed of state vectors at multiple equally timed sampling moments. Each state vector is a six-dimensional vector describing the satellite's spatial position and velocity at a specific moment, including three position components X, Y, and Z, in meters and three velocity components Vx, Vy, and Vz, in meters per second.

[0031] By acquiring the raw radar image parameter files, a unified data source is provided for subsequent orbital state vector extraction and error compensation processing. Furthermore, it is compatible with various file formats (including XML, text, and proprietary binary formats) and parameter files from different satellite platforms (e.g., Sentinel-1, LuTan-1, ALOS-2), avoiding processing interruptions caused by file format or platform differences. This makes the method applicable to SAR image processing tasks from different data sources, exhibiting good versatility and scalability. In addition, this acquisition step only creates a data copy in memory, without changing the storage path or file structure of the original parameter files, ensuring the traceability of the original data and the repeatability of the processing.

[0032] Step S20: Extract the orbital state vector sequence based on the original radar image parameter file.

[0033] Understandably, in the preprocessing stage of spaceborne SAR data, as a key data conversion link in the orbital error compensation process, in application scenarios such as single-scene image fine processing, batch automated production, and emergency rapid processing, the terminal computer, local workstation, or server extracts the orbital state vector sequence from the acquired radar raw image parameter file, providing standardized numerical input for subsequent unreliable data removal at the beginning and end, filtering compensation calculation, and consistency constraint processing.

[0034] It should be noted that the orbital state vector sequence refers to a data set consisting of multiple state vectors arranged in chronological order. This includes the total number of state vectors, the time corresponding to each state vector, and the three-dimensional position and velocity components at each time. The units for the position and velocity components are meters and meters per second, respectively. The time interval between adjacent state vectors is usually fixed at one second or 0.5 seconds. The field mapping table refers to a set of pre-configured parsing rules for different satellite platforms. It records the names, paths, data types, and offsets of the orbital state vector-related fields in the parameter files of each platform, and is used to achieve multi-platform compatible parsing. For example, for Sentinel satellite data, the orbital state vector is stored under a specific label, while for LuTan-1 data it is stored under another label. The field mapping table ensures that this method can accurately locate and extract orbital state vector information in data from various platforms.

[0035] The above orbital state vector sequence is calculated as follows:

[0036] in, The first position in the orbital state vector sequence A state vector; For the first Each state vector is The position components of the axis; For the first Each state vector is The position components of the axis; For the first Each state vector is The position components of the axis; For the first Each state vector is The velocity component of the shaft; For the first Each state vector is The velocity component of the shaft; For the first Each state vector is The velocity component of the shaft.

[0037] By extracting structured orbital state vector sequences and their time parameters from the original image parameter files, the raw data is converted from a file format into a numerical sequence form that the algorithm can process, providing standardized input for subsequent head and tail removal, filtering compensation, and consistency constraints. At the same time, by calling a preset field mapping table, it is compatible with parameter file formats of different satellite platforms (including Extensible Markup Language format, text format, and dedicated binary format), avoiding manual preprocessing due to format differences and improving processing efficiency and automation.

[0038] Step S30: Based on the orbital state vector sequence, remove the unreliable state vectors at the beginning and end to obtain the reliable state vector interval composed of the middle segment state vectors.

[0039] Unreliable state vectors refer to those located at the beginning and end of the orbital state vector sequence, exhibiting significant systematic errors due to the orbit determination system not yet converging or data truncation. Specifically, in the initial stage, the orbit determination system lacks sufficient observational data support, and the filtering solution has not reached a stable state. In the final stage, data truncation causes the terminal state vector to deviate from the true orbit. The beginning and end rejection parameters refer to preset values ​​used to control the rejection quantity, including the rejection quantity at the beginning and the rejection quantity at the end. For example, for typical data from the LuTan-1 satellite with approximately 120 state vectors and a time interval of one second, experimental verification shows that rejecting five state vectors at each end can effectively eliminate unstable data in the early stages of orbit determination convergence. For Sentinel satellite data, the rejection quantity at the beginning and end can be set to three. The rejection quantity should not be too large or too small, with a recommended range of three to ten. The reliable state vector interval refers to the data interval formed by the intermediate state vectors after rejecting the unreliable state vectors at the beginning and end. The state vectors within this interval are of stable quality and are used as the modeling data source for subsequent local polynomial fitting.

[0040] By removing unreliable state vectors at the beginning and end, subsequent filtering modeling is based solely on stable intermediate data, fundamentally preventing the propagation of end-point errors to the global filtering results and improving the accuracy and reliability of the compensation model. Simultaneously, since only the data range for filtering modeling is determined without altering the storage structure of the original sequence, the original position and numerical information of the removed state vectors are retained for subsequent end-point extrapolation recovery, ensuring that the final output file maintains consistency with the original input file in terms of the total number of state vectors and time sampling.

[0041] Step S40: Perform compensation calculations based on the reliable state vector interval to obtain the initial error compensation value at each time step.

[0042] Compensation calculation refers to performing local polynomial fitting on the component sequences within the reliable state vector interval to suppress random noise and preserve the true trajectory change trend. Specifically, this involves sliding a window across the sequence and establishing a low-order polynomial with the time offset as the independent variable within each window. After fitting the data points within the window using the least squares method, the constant term of the fitted polynomial at the center time is taken as the compensation value for that time. The initial error compensation value at each time step refers to the noise-suppressed position and velocity component estimates obtained by local polynomial fitting at the sampling time corresponding to each state vector within the reliable state vector interval. Its purpose is to provide a noise-filtered trajectory data basis for subsequent end-point extrapolation recovery and position-velocity consistency constraint processing.

[0043] By performing systematic compensation calculations on the orbital data within the reliable state vector interval, the original state vector containing random noise is converted into initial error compensation values ​​at each time step. This allows subsequent end extrapolation and consistency constraint processing to be performed on the data after noise filtering, avoiding the direct impact of the original noise on the accuracy of subsequent processing. At the same time, the initial error compensation value retains the complete time correspondence and numerical structure of each time step within the reliable interval, ensuring that subsequent steps can be seamlessly connected.

[0044] Step S50: Perform trajectory extrapolation recovery based on the initial error compensation value to obtain the complete compensation sequence.

[0045] Trajectory extrapolation recovery refers to the process of calculating the state vectors at each moment within the eliminated first and last intervals based on the known motion states at the ends of the reliable intervals, thereby restoring the compensated sequence to its full length. The front-end extrapolation direction is from the beginning of the valid interval forward to the start time of the sequence, and the back-end extrapolation direction is from the end of the valid interval backward to the end time of the sequence. The complete compensated sequence refers to the compensated trajectory data sequence obtained by concatenating the initial error compensation values ​​at each moment within the reliable state vector interval with the front-end and back-end extrapolation recovery values ​​in their original chronological order. This sequence covers the entire number of state vectors and is completely consistent with the sampling times of the original parameter file.

[0046] The state vectors in the first and last eliminated intervals were extrapolated to obtain corresponding compensation values, so that the compensation sequence was restored to be consistent with the original input file in terms of the total number of state vectors. This ensured that the final output parameter file had a complete data structure and could be correctly recognized and called by the subsequent synthetic aperture radar interferometry processing software.

[0047] Step S60: Update the original radar image parameter file according to the complete compensation sequence to complete the orbital error compensation.

[0048] It should be noted that parameter file update refers to the process of writing the position and velocity components from the complete compensation sequence stored in memory into the corresponding state vector fields of the original image parameter file to replace the original orbital state vectors. The updated parameter file is completely identical to the original file in terms of the number of state vectors, time intervals, and file structure. Only the values ​​of the state vectors are replaced with the compensated results. For example, for the X-direction position component of the i-th state vector in the original file, the original value is a certain value, and the compensated value is the corresponding value in the complete compensation sequence. After writing, the value of this field is updated to the compensated value.

[0049] The complete compensation sequence is converted from the calculation results in memory into a target parameter file usable in engineering, enabling the compensation results to be correctly recognized and called by subsequent synthetic aperture radar interferometric processing software, thus achieving seamless integration between the error compensation process and the downstream interferometric processing process. Simultaneously, only the values ​​of the state vector fields are updated, without changing the structure of the original parameter file, the number of state vectors, or the time sampling interval, ensuring full compatibility between the updated target parameter file and the downstream processing software, and avoiding processing interruptions due to file format incompatibility.

[0050] refer to Figure 2 In one feasible implementation, step S60 may include steps A11-A12: Step A11: Perform consistency constraint processing based on the complete compensation sequence to obtain the target error compensation state vector.

[0051] Consistency constraint processing refers to the process of verifying and correcting the kinematic relationship between the position and velocity components in the complete compensation sequence. Its core is to ensure that the velocity component is strictly equal to the first derivative of the position component with respect to time, thereby satisfying the physical self-consistency requirement of the satellite's orbital motion. The target error compensation state vector refers to the final state vector after consistency constraint processing, where the position component retains its position value from the complete compensation sequence, and the velocity component is replaced by a weighted fused velocity. This state vector simultaneously satisfies numerical smoothness and physical kinematic consistency.

[0052] Understandably, the consistency constraint processing effectively eliminates the physical inconsistency error introduced by the independent processing of position and velocity, so that the velocity component of the final output strictly satisfies the kinematic derivative relationship with the position component while retaining the smoothness of the filter, thereby improving the physical reliability of the compensated orbital state vector.

[0053] Step A12: Write the target error compensation state vector back to the original radar image parameter file to obtain the target parameter file, thereby completing the orbit error compensation.

[0054] Write-back refers to the process of writing the position and velocity components of the target error compensation state vector stored in memory into the corresponding state vector fields of the original image parameter file, replacing the original trajectory state vectors. This operation only updates the values ​​of the state vector fields without changing the structure, number of state vectors, or time sampling interval of the original parameter file. For example, for the position component of a certain direction of the i-th state vector in the original file, the original value is a certain value, and the compensated value is the target position component. After write-back, the value of this field is updated. The target parameter file refers to the updated parameter file generated after the write-back operation, which maintains the same file format, structure, number of state vectors, and time sampling interval as the original input parameter file, but the state vector values ​​have been replaced with the compensated results. This file can be directly recognized and used by subsequent synthetic aperture radar interferometry processing software.

[0055] The target error compensation state vector is converted from the calculation results in memory into a target parameter file usable in engineering, ensuring that the compensation results can be correctly recognized and called by the subsequent synthetic aperture radar interferometric processing software, and realizing the connection between the error compensation process and the downstream interferometric processing process. At the same time, only the values ​​of the state vector fields are updated, without changing the structure of the original parameter file, the number of state vectors, and the time sampling interval, ensuring that the updated target parameter file is fully compatible with the downstream processing software and avoiding processing interruptions caused by file format incompatibility.

[0056] By writing the target error compensation state vector back to the original image parameter file to replace the original position and velocity components, a target parameter file with the same number of state vectors, time intervals, and file structure as the original file is obtained. This target parameter file can directly replace the original parameter file for subsequent synthetic aperture radar data preprocessing, interferometric phase simulation, terrain phase removal, precise image registration, and surface deformation inversion. It can also be embedded as an orbit parameter quality control module in the batch automated processing flow of multi-source, multi-scene, long-time-series SAR data. It is especially suitable for situations where precise orbit products have not yet been released, external orbit files are missing, or emergency missions have high timeliness requirements. In this case, by eliminating head and tail anomalies, improving Savitzky-Golay local polynomial filtering, extrapolating end trends, and constraining position and velocity kinematic consistency, the system suppresses random noise, local anomaly jumps, head and tail instabilities, and non-physical high-frequency disturbances in the original state vector. This makes the filtered orbital position, velocity, acceleration, and rate of change of acceleration more consistent with the actual motion of the satellite platform. The filtering effect is quantitatively evaluated using residual mean, standard deviation, root mean square error, anomaly state vector index, and derivative quality map. Finally, the target parameter file is used as the output carrier to achieve orbital error compensation without relying on external precision orbital products.

[0057] In one feasible implementation, step S20 further includes: locating the orbital state vector field region based on the original radar image parameter file; acquiring information based on the orbital state vector field region to obtain orbital state vector information; and performing a validity check on the orbital state vector information to obtain an orbital state vector sequence.

[0058] It's important to note that locating the orbital state vector field area refers to the process of determining the specific storage location of the orbital state vector data in the file by calling a pre-defined field mapping table based on the format type of the input parameter file and the satellite platform it belongs to. For example, the orbital state vector in the Sentinel satellite's parameter file is located under a specific tag, while in the LuTan-1 satellite's parameter file it is located under another tag. The purpose of this location is to ensure that subsequent information acquisition can accurately read the orbital data. The field mapping table is a set of pre-configured parsing rules for different satellite platforms, recording the names, paths, data types, and offsets of the orbital state vector-related fields in the parameter files of each platform. For example, the Extensible Markup Language (XML) format records specific field names, the text format records specific key names, and the dedicated binary format records byte offsets and data lengths. The field mapping table enables unified parsing for different platforms and formats. Information acquisition refers to the process of reading the orbital state vector-related information in the located field area according to the file format type and in the appropriate manner. The acquired information includes the total number of state vectors, the time corresponding to each state vector, and the three-dimensional position and three-dimensional velocity components at each time. The XML format is read through tag parsing, the text format is read through key-value pair parsing, and the dedicated binary format is read through offsets. Legality verification refers to the process of checking the validity of the extracted information. This includes quantity verification such as whether the total number of state vectors is greater than zero, time consistency verification such as whether the time interval between adjacent state vectors conforms to a fixed interval, and numerical range verification such as whether the position and velocity components are within the reasonable range of the satellite's orbital motion. For example, the position components of near-Earth orbit synthetic aperture radar satellites are usually on the order of more than 6,000 kilometers, and the velocity components are usually on the order of 7 to 8,000 meters per second. If the extracted position components are only a few meters or tens of thousands of meters, it is obviously outside the reasonable range and should be marked as abnormal and a warning message should be generated. The purpose of verification is to promptly discover problems in the original data and prevent erroneous data from entering subsequent processing.

[0059] The structured orbital state vector sequence and its time parameters are accurately located and extracted from the heterogeneous raw parameter files, transforming the raw data from a file format into a numerical sequence that the algorithm can process. This provides a standardized input for subsequent head and tail removal, filtering compensation, and consistency constraints. At the same time, the validity check promptly detects problems such as abnormal quantity, inconsistent time, or out-of-range values ​​in the raw data, preventing erroneous data from entering the subsequent processing flow and ensuring the reliability and stability of the processing link.

[0060] In one feasible implementation, step S30 further includes: obtaining preset head and tail rejection parameters; performing track state vector rejection based on the head and tail rejection parameters to obtain intermediate segment state vectors; and performing validity determination based on the intermediate segment state vectors to obtain reliable state vector intervals.

[0061] It should be noted that the "head and tail removal parameters" refer to preset values ​​used to control the number of removals, including the number of head removals and tail removals. The number of removals should not be too large or too small; a range of three to ten is recommended. Parameter sources include manual input by the user, default values ​​automatically loaded by the system based on the satellite platform type, or automatic determination by the computing device based on data quality. The intermediate state vector refers to the intermediate portion of the state vectors retained after removing unreliable head and tail state vectors from the original orbital state vector sequence. The reliable state vector interval refers to the data interval confirmed to meet the requirements of quantity, temporal continuity, and numerical range after the validity of the intermediate state vectors, and can be safely used for subsequent filtering modeling. The number of effective state vectors refers to the number of state vectors remaining after removing unreliable head and tail state vectors from the original orbital state vector sequence, which can be used for subsequent filtering modeling.

[0062] The above reliable state vector interval is calculated as follows:

[0063]

[0064] in, The number of valid state vectors; This represents the total number of original state vectors; The number of items removed from the first part; This refers to the number of items removed from the tail section. This represents the reliable state vector interval.

[0065] Unreliable beginning and end portions of the original orbital state vector sequence are pre-excluded, ensuring that subsequent filtering modeling is based solely on stable intermediate data. This prevents the propagation of end-point errors into the filtering model and improves the reliability of the compensation model. Simultaneously, validity checks ensure that the reliable state vector interval meets the requirements for subsequent processing, avoiding processing failures due to insufficient data.

[0066] In one feasible implementation, step S40 further includes: splitting the vector sequence of the reliable state vector interval by dimension to obtain a one-dimensional component sequence; setting candidate windows according to the number of reliable state vectors to obtain a candidate window set; performing local polynomial fitting based on the one-dimensional component sequence and the candidate window set to obtain a smoothed prediction value under each candidate window; selecting the optimal window based on the smoothed estimate value under each candidate window to obtain a smoothed prediction value under the optimal window; and obtaining the initial error compensation value at each time step based on the smoothed prediction value under the optimal window.

[0067] It is understandable that in application scenarios such as single-scene image fine processing, batch automated production, and emergency rapid processing, after the terminal computer, local workstation, or server completes the determination of the reliable state vector interval and before outputting the initial error compensation value, it is necessary to perform local polynomial fitting and adaptive window selection on each component sequence within the reliable interval in order to suppress high-frequency random noise and obtain the initial error compensation value at each time.

[0068] It should be noted that dimensional splitting refers to decomposing the six-dimensional state vector at each moment within the reliable state vector interval into six independent one-dimensional component sequences, allowing subsequent local polynomial fitting to process each component separately. The one-dimensional component sequence refers to the data sequence of individual physical components obtained after dimensional splitting, arranged in chronological order, including the X-direction position sequence, Y-direction position sequence, Z-direction position sequence, X-direction velocity sequence, Y-direction velocity sequence, and Z-direction velocity sequence. The candidate window set refers to a candidate list composed of multiple windows with different half-widths. A window half-width that is too small will result in insufficient noise suppression, while a window that is too wide may weaken the true trend of the trajectory. The candidate window half-width is usually selected from two to ten. Local polynomial fitting refers to the process of establishing a low-order polynomial with the time offset as the independent variable within each sliding window, fitting the data points within the window using the least squares method, and taking the constant term of the fitted polynomial at the center moment as the smoothed predicted value for that moment. The polynomial order is usually second to fourth, preferably third. The smoothed predicted value refers to the component estimate obtained through local polynomial fitting at the corresponding moment after high-frequency noise suppression. Optimal window selection refers to the process of evaluating the fitting results of each candidate window through a comprehensive evaluation function and selecting the window that minimizes the evaluation function value as the optimal window for that component. The comprehensive evaluation function consists of three weighted terms: the first term is the root mean square error of the filtering residual, the second term is the ratio of the acceleration dispersion after compensation to the acceleration dispersion before compensation, and the third term is the ratio of the acceleration dispersion after compensation to the acceleration dispersion before compensation.

[0069] The calculation method for the above local polynomial fitting is as follows:

[0070] in, For the first in the window The component values ​​of each point; This is the offset relative to the center point; , , These are the coefficients of the local polynomial.

[0071] The calculation method for the above comprehensive evaluation function is as follows:

[0072] in, To comprehensively evaluate the function value; The length of the window; The root mean square error of the filter residual; The ratio of the degree of dispersion of acceleration; To increase the dispersion ratio of the jerk; The weights are the root mean square error of the filter residuals; The weights for the degree of dispersion of acceleration; The weighting of the jerk dispersion ratio.

[0073] The six-dimensional state vector within the reliable state vector interval is split into six independent one-dimensional component sequences, enabling subsequent fitting to process the different physical characteristics of each component separately, avoiding suboptimal compensation that a unified window may introduce for components with different characteristics. By adaptively selecting the optimal window through a comprehensive evaluation function, the shortcomings of a fixed window in balancing noise suppression and trend preservation are overcome, ensuring that the compensation result achieves the best balance between the two under different data quality conditions, and outputting the initial error compensation value after noise filtering at each time step.

[0074] refer to Figure 3 In one possible implementation, step S50 may include steps B11-B14: Step B11: Calculate the parameters based on the initial error compensation value to obtain the end motion state parameters.

[0075] It should be noted that the end motion state parameters refer to the initial conditions used for extrapolation calculations, including the position, velocity, and acceleration at the beginning and end of the reliable state vector interval. The position and velocity are directly read from the initial error compensation values, while the acceleration is obtained by calculating the second difference between the position or velocity sequences. Their accuracy directly affects the reliability of the extrapolation results. The beginning of the reliable state vector interval refers to the region containing the earliest state vectors within the interval, i.e., the interval boundary near the start of the sequence. The motion state parameters at the beginning are used for front-end extrapolation. The end of the reliable state vector interval refers to the region containing the latest state vectors within the interval, i.e., the interval boundary near the end of the sequence. The motion state parameters at the end are used for back-end extrapolation. Multi-point averaging refers to the method of selecting multiple points at the end of the reliable state vector interval, calculating the position, velocity, and acceleration of each point separately, and then averaging the results as the end motion state parameters. The number of points selected is usually three to six, preferably five. This method improves the stability and reliability of the extrapolation initial conditions by smoothing the motion state information of multiple points near the end.

[0076] Understandably, extracting motion state parameters such as position, velocity, and acceleration from the initial error compensation values ​​at the ends of the reliable state vector interval provides accurate starting conditions for subsequent trajectory extrapolation based on Newton's kinematic equations. Simultaneously, this step calculates the end-point motion state parameters using a multi-point averaging method, effectively suppressing the interference of single-point noise at the ends on the extrapolation starting conditions and improving the stability of the extrapolation results.

[0077] Step B12: Extrapolate the front-end trajectory using Taylor series based on the end motion state parameters to obtain the front-end extrapolated state vector.

[0078] It should be noted that front-end trajectory extrapolation refers to the process of calculating the motion state of each discarded head state vector point by point along the decreasing time direction, starting from the beginning of the reliable state vector interval. The extrapolation step size is one state vector time interval, and the number of extrapolation points is equal to the number of head state vectors discarded. This process extends the motion state at the beginning of the reliable interval to an earlier time, allowing the discarded head state vectors to obtain extrapolation compensation values. Taylor series serves as the mathematical basis for end-end trajectory extrapolation. It is used to calculate the discarded head and tail state vectors based on the motion state at the end of the reliable state vector interval. It uses the known position, velocity, and acceleration at the beginning of the reliable interval to calculate the position and velocity after extrapolating forward by a specific time interval. This expansion preserves the kinematic derivative relationship between position, velocity, and acceleration, making the extrapolation result conform to the physical laws of satellite inertial flight. The expansion includes first-order and second-order terms of the time interval, reflecting the displacement correction caused by uniform motion displacement and acceleration, respectively. The front-end extrapolated state vector refers to the set of compensated state vectors obtained by extrapolating the front-end trajectory, covering each moment in the eliminated head interval. It includes the position and velocity components at each moment. For example, if the number of head eliminations is five, the front-end extrapolated state vector contains five state vectors from the start of the sequence to the moment before the head of the reliable interval. Each state vector contains three position components and three velocity components. This extrapolation result is used to subsequently splice with the initial error compensation value to form a complete compensation sequence.

[0079] The above calculation method for front-end trajectory extrapolation is as follows:

[0080] in, To push forward and outward Position in seconds; The location of the first end of the reliable interval; For the reliable interval's initial velocity; This is the extrapolation time interval; For the acceleration at the beginning of the reliable interval; This represents the time at the beginning of the reliable interval.

[0081] Understandably, the discarded head state vectors were compensated by extrapolation based on Taylor expansion of Newtonian kinematics, thus restoring the head region in terms of the number of state vectors. Simultaneously, extrapolation was performed using Taylor expansion based on Newtonian kinematics equations, and the extrapolation direction was constrained by the physical laws of satellite inertial flight. This ensured that the extrapolated values ​​strictly satisfied the kinematic derivative relationships between position, velocity, and acceleration, guaranteeing the physical rationality of the end-point extrapolation results.

[0082] Step B13: Extrapolate the rear trajectory using Taylor series based on the end motion state parameters to obtain the rear extrapolated state vector.

[0083] It should be noted that back-end trajectory extrapolation refers to the process of calculating the motion state of each discarded tail state vector point by point along the time-increasing direction, starting from the end of the reliable state vector interval. The extrapolation step size is one state vector time interval, and the number of extrapolation points is equal to the number of tail discards. This process extends the motion state at the end of the reliable interval to a later time, so that the discarded tail state vectors obtain extrapolation compensation values. Taylor series serves as the mathematical basis for end-end trajectory extrapolation. It is used to calculate the discarded first and last state vectors based on the motion state at the end of the reliable state vector interval. It uses the known position, velocity, and acceleration at the end of the reliable interval to calculate the position and velocity after extrapolating for a specific time interval. This expansion preserves the kinematic derivative relationship between position, velocity, and acceleration, making the extrapolation result conform to the physical laws of satellite inertial flight. Positive values ​​are taken for the back-end extrapolation time interval. The back-end extrapolated state vector refers to the set of compensated state vectors obtained by extrapolating the back-end trajectory, covering each moment in the eliminated tail interval. It includes the position and velocity components at each moment. For example, if the number of tail eliminations is five, the back-end extrapolated state vector contains five state vectors from the moment after the end of the reliable interval to the end of the sequence. Each state vector contains three position components and three velocity components. This extrapolation result is used to subsequently concatenate with the initial error compensation value to form a complete compensation sequence.

[0084] The calculation method for the above back-end trajectory extrapolation is as follows:

[0085] in, To push backwards Position in seconds; The location at the end of the reliable interval; For the reliable end-of-range velocity; This is the extrapolation time interval; Acceleration at the end of the reliable interval; This represents the end time of the reliable interval.

[0086] Understandably, the discarded tail state vectors were compensated by extrapolation based on Taylor expansion of Newtonian kinematics, thus restoring the tail region in terms of the number of state vectors. Simultaneously, extrapolation was performed using Taylor expansion based on Newtonian kinematics equations, and the extrapolation direction was constrained by the physical laws of satellite inertial flight. This ensured that the extrapolated values ​​strictly satisfied the kinematic derivative relationships between position, velocity, and acceleration, guaranteeing the physical rationality of the end-point extrapolation results.

[0087] Step B14: Obtain the complete compensation sequence based on the front-end extrapolation state vector and the back-end extrapolation state vector.

[0088] It should be noted that the complete compensation sequence refers to the compensated orbital data sequence obtained by concatenating the front-end extrapolated state vector, the initial error compensation values ​​at each moment within the reliable state vector interval, and the back-end extrapolated state vector in the original chronological order, covering all state vectors and completely consistent with the sampling times of the original parameter file. This sequence contains position and velocity components at all moments and serves as the input data for subsequent consistency constraint processing. Concatenation refers to the operation of joining the three parts of data into a continuous sequence in chronological order. The concatenation order is as follows: first, place the front-end extrapolated state vector; then, place the initial error compensation values ​​at each moment within the reliable state vector interval; and finally, place the back-end extrapolated state vector. During the concatenation process, it is necessary to ensure that adjacent parts are continuous in time and match the length of the original sequence in terms of quantity.

[0089] It is understandable that the three parts of data—the front-end extrapolated state vector, the initial error compensation value, and the back-end extrapolated state vector—are concatenated into a complete compensation sequence in their original time order. This restores the segmented compensation data to a continuous sequence form that completely corresponds to the sampling time of the original parameter file, providing a complete data foundation for subsequent consistency constraint processing.

[0090] In one feasible implementation, step A11 further includes: performing differential calculations on the position components of the complete compensation sequence to obtain position differential velocity components; performing weighted fusion on the velocity components of the complete compensation sequence and the position differential velocity components to obtain target velocity components; and obtaining target error compensation state vectors based on the target velocity components and the position components.

[0091] It should be noted that differential calculation refers to the process of calculating the first-order numerical derivatives of the position components in the three directions of the complete compensation sequence. For points within the sequence, the central difference method is used, which calculates the instantaneous velocity estimate at the current moment using the position values ​​of adjacent moments. For the beginning of the sequence, the forward difference method is used, and for the end, the backward difference method is used. This calculation is entirely derived from the position sequence and naturally satisfies the derivative relationship with the position. The position differential velocity components refer to the velocity estimates in the three directions obtained through differential calculation, including the X-direction position differential velocity, Y-direction position differential velocity, and Z-direction position differential velocity. Weighted fusion refers to the process of linearly combining the existing velocity components in the complete compensation sequence with the position differential velocity components according to preset weights to obtain a fused velocity component that balances filtering smoothness and physical consistency. The fusion weight is denoted as λ, ranging from 0 to 1. When λ equals 0, the original velocity components are completely retained; when λ equals 1, the position differential velocity is used entirely; and when λ takes an intermediate value, a compromise is reached between the two. The target velocity component refers to the final velocity component obtained after weighted fusion processing, including the target velocity in the X direction, the target velocity in the Y direction, and the target velocity in the Z direction. This velocity component retains the filtering smoothness of the original velocity component and satisfies the kinematic derivative relationship with the position component by introducing the position differential velocity.

[0092] The above-mentioned position differential velocity component is calculated as follows:

[0093]

[0094]

[0095] in, for Directional position differential velocity; The position of X at the next moment; The position of X at the previous moment; for Directional position differential velocity; The position of Y at the next moment; The position of Y at the previous moment; for Directional position differential velocity; The Z position at the next moment; This represents the Z position at the previous moment.

[0096] The above target velocity components are calculated as follows:

[0097]

[0098]

[0099] in, for Direction and target speed; for Original direction filtering speed; for Directional position differential velocity; For weighted fusion weights; for Direction and target speed; for Original direction filtering speed; for Directional position differential velocity; for Direction and target speed; for Original direction filtering speed; for Direction, position, differential velocity.

[0100] Understandably, differentiating the position components of the complete compensation sequence yields the position differential velocity, enabling subsequent fusion processing to correct the velocity components based on physical constraints. By weighted fusion of the original velocity components and the position differential velocity, the physical inconsistency error introduced by processing position and velocity independently is effectively eliminated. This ensures that the final output velocity component strictly satisfies the kinematic derivative relationship with the position component while retaining the smoothness of the filter, thus improving the physical reliability of the compensated orbital state vector. Simultaneously, the weighted fusion mechanism avoids the numerical noise amplification problem that might be introduced by solely using the position differential velocity, guaranteeing the numerical stability of the velocity components.

[0101] This application also provides a track error compensation device; please refer to... Figure 4 The track error compensation device includes: Module 10 is used to acquire the original radar image parameter file; Extraction module 20 is used to extract the orbital state vector sequence based on the original radar image parameter file; The elimination module 30 is used to eliminate unreliable state vectors at the beginning and end of the orbital state vector sequence to obtain a reliable state vector interval composed of the intermediate segment state vectors. The calculation module 40 is used to perform compensation calculations based on the reliable state vector interval to obtain the initial error compensation value at each time step. Extrapolation module 50 is used to perform trajectory extrapolation recovery based on the initial error compensation value to obtain a complete compensation sequence; The update module 60 is used to update the original radar image parameter file according to the complete compensation sequence to complete the orbital error compensation.

[0102] The orbital error compensation device provided in this application, employing the orbital error compensation method described in the above embodiments, can solve the technical problem of eliminating errors in existing orbital state vectors in spaceborne synthetic aperture radar image parameter files without relying on external precision orbital products. Compared with the prior art, the beneficial effects of the orbital error compensation device provided in this application are the same as those of the orbital error compensation method provided in the above embodiments, and other technical features in the orbital error compensation device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0103] This application provides a track error compensation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the track error compensation method in Embodiment 1 above.

[0104] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing the track error compensation device in the embodiments of this application. The track error compensation device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The track error compensation device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0105] like Figure 5As shown, the track error compensation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the track error compensation device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the track error compensation device to communicate wirelessly or wiredly with other devices to exchange data. Although track error compensation devices with various systems are shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0106] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0107] The orbital error compensation device provided in this application, employing the orbital error compensation method described in the above embodiments, can solve the technical problem of eliminating errors in existing orbital state vectors in spaceborne synthetic aperture radar image parameter files without relying on external precision orbital products. Compared with the prior art, the beneficial effects of the orbital error compensation device provided in this application are the same as those of the orbital error compensation method provided in the above embodiments, and other technical features of this orbital error compensation device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0108] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0109] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0110] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the track error compensation method in the above embodiments.

[0111] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0112] The aforementioned computer-readable storage medium may be included in the track error compensation device; or it may exist independently and not assembled into the track error compensation device.

[0113] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the track error compensation device, cause the track error compensation device to: Obtain the raw radar image parameter file; Extract the orbital state vector sequence from the original radar image parameter file; Based on the orbital state vector sequence, unreliable state vectors at the beginning and end are removed to obtain a reliable state vector interval composed of the middle segment state vectors. Compensation calculations are performed based on the reliable state vector interval to obtain the initial error compensation value at each time step; Based on the initial error compensation value, the trajectory is extrapolated and recovered to obtain the complete compensation sequence; The original radar image parameter file is updated according to the complete compensation sequence to complete the orbital error compensation.

[0114] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0115] 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 this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated 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, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0116] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0117] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described orbital error compensation method. This enables the elimination of errors in existing orbital state vectors in spaceborne synthetic aperture radar image parameter files without relying on external precision orbital products. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the orbital error compensation method provided in the above embodiments, and will not be elaborated upon here.

[0118] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A method for compensating for track errors, characterized in that, include: Obtain the raw radar image parameter file; Extract the orbital state vector sequence from the original radar image parameter file; Based on the orbital state vector sequence, unreliable state vectors at the beginning and end are removed to obtain a reliable state vector interval composed of the middle segment state vectors. Compensation calculations are performed based on the reliable state vector interval to obtain the initial error compensation value at each time step; Based on the initial error compensation value, the trajectory is extrapolated and recovered to obtain the complete compensation sequence; The original radar image parameter file is updated according to the complete compensation sequence to complete the orbital error compensation.

2. The track error compensation method according to claim 1, characterized in that, The step of updating the original radar image parameter file according to the complete compensation sequence to complete the orbital error compensation includes: Based on the complete compensation sequence, consistency constraint processing is performed to obtain the target error compensation state vector; The target error compensation state vector is written back to the original radar image parameter file to obtain the target parameter file, thereby completing the orbital error compensation.

3. The track error compensation method according to claim 1, characterized in that, The step of extracting the orbital state vector sequence based on the original radar image parameter file includes: Based on the original radar image parameter file, the orbital state vector field region is obtained by positioning. Information is obtained by acquiring information from the orbital state vector field region to obtain orbital state vector information; The validity of the orbital state vector information is verified to obtain the orbital state vector sequence.

4. The track error compensation method according to claim 1, characterized in that, The process of removing unreliable state vectors at the beginning and end of the orbital state vector sequence to obtain a reliable state vector interval consisting of the intermediate segment state vectors includes: Obtain the preset first and last removal parameters; The track state vector is removed according to the first and last removal parameters to obtain the middle segment state vector; The validity is determined based on the intermediate segment state vector to obtain the reliable state vector interval.

5. The track error compensation method according to claim 1, characterized in that, The step of performing compensation calculations based on the reliable state vector interval to obtain the initial error compensation value at each time step includes: The vector sequence of the reliable state vector interval is split according to the dimension to obtain a one-dimensional component sequence; Candidate windows are set based on the number of reliable state vectors to obtain a candidate window set; Local polynomial fitting is performed based on the one-dimensional component sequence and the candidate window set to obtain smoothed predicted values ​​under each candidate window. Based on the smoothed estimates under each candidate window, the optimal window is selected to obtain the smoothed predicted value under the optimal window. Based on the smoothed prediction value under the optimal window, the initial error compensation value at each time step is obtained.

6. The track error compensation method according to claim 1, characterized in that, The step of extrapolating the trajectory based on the initial error compensation value to obtain the complete compensation sequence includes: Based on the initial error compensation value, parameter calculations are performed to obtain the end motion state parameters; Based on the end motion state parameters, Taylor series is used to extrapolate the front trajectory to obtain the front extrapolated state vector. Based on the end motion state parameters, Taylor series is used to extrapolate the rear trajectory to obtain the rear extrapolated state vector. Based on the front-end extrapolation state vector and the back-end extrapolation state vector, a complete compensation sequence is obtained.

7. The track error compensation method according to claim 2, characterized in that, The step of performing consistency constraint processing based on the complete compensation sequence to obtain the target error compensation state vector includes: The position differential velocity component is obtained by performing differential calculation on the position component of the complete compensation sequence. The target velocity component is obtained by weighted fusion of the velocity component of the complete compensation sequence and the position differential velocity component. Based on the target velocity and position components, the target error compensation state vector is obtained.

8. A track error compensation device, characterized in that, include: The acquisition module is used to acquire raw radar image parameter files; The extraction module is used to extract the orbital state vector sequence based on the original radar image parameter file; The elimination module is used to eliminate unreliable state vectors at the beginning and end of the orbital state vector sequence to obtain a reliable state vector interval composed of the middle segment state vectors. The calculation module is used to perform compensation calculations based on the reliable state vector interval to obtain the initial error compensation value at each time step. The extrapolation module is used to extrapolate the trajectory based on the initial error compensation value to obtain the complete compensation sequence; The update module is used to update the original radar image parameter file according to the complete compensation sequence to complete the orbital error compensation.

9. A track error compensation device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the orbital error compensation method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the orbital error compensation method as described in any one of claims 1 to 7.

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