High-precision combined navigation system

Abstract

The invention relates to the field of precise navigation, in particular to a high-precision integrated navigation system which comprises a signal transmitting module for transmitting position signals at preset time intervals, a satellite monitoring module for receiving the position signals to form corresponding high-orbit information and low-orbit information, and a positioning module for preprocessing the high-orbit information and the low-orbit information and transmitting the preprocessed high-orbit information and the preprocessed low-orbit information to the satellite monitoring module. The processing module is used for acquiring the motion information of the moving target, forming corresponding preprocessing information, learning the preprocessing information by utilizing an error learning model, determining a receiving error and determining three-dimensional information of the moving target, and the matching module is used for acquiring the motion information of the moving target and analyzing in combination with the three-dimensional information to form a corresponding navigation result. By integrating various information sources and an advanced data processing technology, the precision and reliability of navigation are improved, accurate navigation of a moving target is realized, and the safety and the navigation efficiency are remarkably improved.

Description

Technical Field

[0001] The invention relates to the field of precise navigation, and in particular to a high-precision integrated navigation system. Background Art

[0002] With the rapid development of intelligent driving technology, high-precision integrated navigation systems have become the focus of the market. High-precision integrated navigation systems improve the accuracy and reliability of navigation by integrating multiple sensors and technologies. At the same time, the application of machine learning technology, especially deep learning and reinforcement learning, in navigation systems has further improved the level of intelligent navigation and provided strong technical support for intelligent driving and robot navigation. By integrating multiple sensor data, high-precision positioning and navigation in various complex environments are achieved, providing solid technical support for intelligent driving and being able to maintain high-precision and high-reliability positioning services in complex environments.

[0003] China Patent Authorization Announcement No.: CN104180807B discloses a high-precision attitude determination method for a combined navigation system, which includes the following steps: solving the direction cosine matrix at the current moment according to the gyroscope angular velocity signal and the current state information; calculating the projection of the current column vector of the matrix on the previous column vector, and normalizing the current column under the optimal conditions; solving the attitude angle according to the new direction cosine matrix after orthogonal normalization. The invention solves the direction cosine matrix at the current moment by the quaternion method, and the amount of calculation is small; after obtaining the direction cosine matrix at the current moment, the Schmidt orthogonal method is used to normalize the direction cosine matrix at the current moment, which greatly reduces the error of subsequent attitude information solution and improves the attitude determination accuracy; under the optimal conditions, that is, the cost function based on the error bi-norm is minimized, the Schmidt orthogonal method is used to normalize the direction cosine matrix at the current moment, and a stable and efficient analytical solution can be obtained.

[0004] China Patent Authorization Announcement No.: CN104297773B discloses a high-precision Beidou three-frequency SINS deep integrated navigation system, which takes into account the error elimination method and ambiguity resolution method of Beidou receiver navigation, as well as the problem of mutually assisted combined navigation of Beidou navigation and strapdown inertial navigation SINS, and uses the TCAR method to accurately and efficiently resolve the integer ambiguity, thereby performing high-precision carrier phase measurement; through the three-frequency data signals, according to the equation group composed of the ionospheric refraction error calculation formula, the ionospheric refraction error can be solved to improve positioning accuracy. The invention focuses on solving the problem of the deep integrated navigation system's dependence on the accuracy of SINS, improves the navigation accuracy of the receiver through the Beidou three-frequency data fusion, reduces the accuracy requirements for the SINS's own attitude, speed, and position, and improves the anti-interference ability and comprehensive accuracy of the Beidou three-frequency receiver and SINS combined navigation system, which can improve the performance of weapons and important civilian carriers.

[0005] However, the above method has the following problems: it is impossible to obtain more accurate positioning information of the moving target, thereby reducing the accuracy of navigation. Summary of the invention

[0006] To this end, the present invention provides a high-precision integrated navigation system to overcome the problem in the prior art that more accurate positioning information of a moving target cannot be obtained, thereby reducing the accuracy of navigation.

[0007] To achieve the above object, the present invention provides a high-precision integrated navigation system, comprising: A signal transmitting module, which transmits a position signal at a preset time interval; A satellite monitoring module, which is connected to the signal transmission module and is used to receive the position signal to form corresponding high orbit information and low orbit information; A positioning module, connected to the satellite monitoring module, for preprocessing the high orbit information and the low orbit information to form corresponding preprocessed information, and learning the preprocessed information using an error learning model to determine a receiving error, and combining the high orbit information and the low orbit information to determine the three-dimensional information of the moving target; A matching module, connected to the positioning module, for acquiring the motion information of the moving target, and analyzing the three-dimensional information and the motion information of the moving target in combination with the three-dimensional information to form a corresponding navigation result; Wherein, the high-orbit information is the position information formed by the high-orbit remote sensing satellite receiving the position signal; The low-orbit information is position information formed by a low-orbit remote sensing satellite receiving the position signal; The pre-processing information includes high orbit processing information and low orbit processing information; The receiving error is a ranging error generated by the high-orbit remote sensing satellite and the low-orbit remote sensing satellite receiving the position signal; The error learning model is generated by training and debugging the training set formed by the preprocessing information; The motion information includes the speed, acceleration and / or real-time motion direction of the moving target.

[0008] Furthermore, the signal transmission module includes: A collector, which is used to collect the position data of the moving target in real time; a transmitter connected to the collector and configured to convert the position data into a position signal; A timing device, connected to the transmitter, for sending a transmission instruction to the transmitter at preset time intervals; The location data includes the latitude and longitude coordinates of the moving target and / or the height information relative to the sea level.

[0009] Furthermore, the positioning module includes: A preprocessor, used for preprocessing the high orbit information and the low orbit information, and generating corresponding preprocessing information; A learner, connected to the preprocessor, for selecting a number of indicator features and learning the preprocessing information, and generating a corresponding receiving error; A filter is connected to the learner to screen and filter erroneous reception errors, and perform three-dimensional calculations in combination with the high orbit information and the low orbit information to generate three-dimensional information corresponding to the moving target.

[0010] Furthermore, the matching module includes: A measuring device, which is used to measure the motion information of the moving target in real time; An analysis device, connected to the measuring device, learns the three-dimensional information and the motion information and generates a number of corresponding navigation results; An interactive device, connected to the analyzing device, for displaying the navigation result and selecting by the moving target; Wherein, the analysis device is provided with a comprehensive matching model for calculating the best path according to the three-dimensional information and the motion information to form a corresponding navigation result.

[0011] Furthermore, the transmitter obtains the data length of the position data and determines the byte order of the position data, performs signal calculation according to the data length and the byte order, converts the position data into a position signal, and the satellite monitoring module accesses the high-orbit remote sensing satellite and the low-orbit remote sensing satellite, obtains the received position signals, and marks them as corresponding high-orbit information and low-orbit information.

[0012] Furthermore, the preprocessor cuts the high orbit information and the low orbit information according to the sampling rate to form a plurality of preprocessed information with the sampling rate being the standard sampling rate, and selects a plurality of indicator features according to the preprocessed information, and the preprocessed information enters the error learning model for learning, and generates a corresponding receiving error. The standard sampling rate is a sampling rate that can be recognized by the error learning model, and during the learning process of the error learning model, the standard sampling rate remains unchanged; The indicator features include latitude and longitude coordinates, altitude information and / or distance.

[0013] Further, the filter receives the reception error and compares the reception error with an error threshold, wherein: When the receiving error is less than the error threshold, retaining the receiving error; When the receiving error is greater than the error threshold, filtering the corresponding receiving error; The error threshold is the maximum value of the reception error generated when the high-orbit information and the low-orbit information are received, and is related to the performance of the high-orbit remote sensing satellite and the low-orbit remote sensing satellite.

[0014] Furthermore, the average value of the high orbit information and the low orbit information is obtained and recorded as the mean information, the filtered receiving error is matched with the time-synchronized mean information, and a three-dimensional calculation is performed, wherein, The three-dimensional calculation is to perform a multi-point perspective algorithm on the mean information and the receiving error to generate corresponding three-dimensional trajectory points, and connect several three-dimensional trajectory points in time sequence to form three-dimensional information corresponding to the moving target.

[0015] Furthermore, the measuring device queries the dynamic behavior of the moving target, obtains the motion information corresponding to the moving target, and inputs the motion information and the three-dimensional information into the comprehensive matching model.

[0016] Furthermore, the comprehensive matching model performs path planning on the motion information and the three-dimensional information, generates the navigation result, and transmits the navigation result to the interactive device, wherein: The path planning includes obstacle avoidance algorithm and road condition retrieval analysis.

[0017] Compared with the prior art, the present invention utilizes a signal transmitting module to transmit a position signal of a moving target at a preset interval, a satellite monitoring module to receive the position signal to form corresponding high-orbit information and low-orbit information, a positioning module to pre-process the high-orbit information and the low-orbit information to form corresponding pre-processed information, and utilizes an error learning model to learn the pre-processed information, determine the receiving error, and determine the three-dimensional information of the moving target, a matching module to obtain the motion information of the moving target, and analyze it in combination with the three-dimensional information to form a corresponding navigation result, and integrates multiple information sources and advanced data processing technologies to improve the accuracy and reliability of navigation, realize accurate navigation of the moving target, and significantly improve safety and navigation efficiency.

[0018] Furthermore, by setting up a signal transmission module, the position data is converted into a position signal, so as to realize real-time tracking and precise positioning of the moving target. By periodically transmitting a position signal containing latitude, longitude and altitude information, the system is able to provide continuous position updates for the moving target, which is essential to ensure the accuracy and reliability of navigation. In addition, this modular design also allows the system to be customized and optimized according to different application requirements. For example, in some applications, more frequent position updates may be required for more accurate navigation, while in other applications, longer transmission intervals may be required to save energy or meet other technical requirements. By adjusting the settings of the timing device, these different needs can be flexibly adapted.

[0019] Furthermore, the learner selects several indicator features and learns the preprocessed information, generates the corresponding receiving error, and combines the high-orbit information and low-orbit information for three-dimensional calculation to generate the three-dimensional information corresponding to the moving target. Through the collaborative work of the preprocessor, learner and filter, the high-orbit and low-orbit information is deeply processed and analyzed, and finally the accurate three-dimensional position information of the moving target is generated. The design of this system takes into account the preprocessing, error learning and screening of the signal to ensure that continuous, stable and reliable high-precision positioning results can be provided in various complex environments, and is suitable for a variety of scenarios.

[0020] Furthermore, the motion information is measured in real time by the measuring device, and the corresponding navigation results are generated in combination with the three-dimensional information, so that the navigation system can respond to the dynamic changes of the moving target in real time and provide timely navigation decision support. Through the intelligent analysis of the comprehensive matching model, the system can take into account a variety of factors, such as real-time traffic conditions, terrain changes, obstacles, etc., to generate the best navigation solution. This system is particularly suitable for fields such as autonomous vehicles, drones, robots, etc., which require highly automated and intelligent navigation solutions to improve efficiency and safety.

[0021] Furthermore, the data length of the position data is obtained through the transmitter, and the byte order of the position data is determined. Signal calculation is performed based on the data length and byte order, and the position data is converted into a position signal, ensuring the efficiency and accuracy of the entire chain from data acquisition to signal processing, which is crucial to the performance of high-precision navigation systems.

[0022] Furthermore, by cutting the high-orbit information and the low-orbit information according to the sampling rate, a number of pre-processed information with a standard sampling rate is formed, and a number of indicator features are selected according to the pre-processed information to generate corresponding receiving errors, so that the system can accurately identify and correct errors caused by satellite signal reception, thereby providing more accurate positioning and navigation services.

[0023] Furthermore, by comparing the received error with the error threshold, the erroneous received error is filtered out to ensure that only reasonable errors are used in the final navigation calculation, thereby improving the accuracy and reliability of the navigation system and helping to reduce navigation deviations due to abnormal errors, especially when the performance differences between high-orbit and low-orbit remote sensing satellites may lead to different error characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the structure of a high-precision integrated navigation system of the present invention; Figure 2 This is a schematic diagram of the structure of the signal transmission module of the present invention; Figure 3 This is a schematic diagram of the positioning module structure of the present invention; Figure 4 This is a schematic diagram of the matching module structure of the present invention. DETAILED DESCRIPTION

[0025] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0027] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0028] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0029] See also Figure 1 As shown, it is a structural schematic diagram of a high-precision integrated navigation system of the present invention, comprising: A signal transmitting module, which transmits a position signal at a preset time interval; A satellite monitoring module is connected to the signal transmission module to receive position signals to form corresponding high orbit information and low orbit information; The positioning module is connected to the satellite monitoring module to pre-process the high-orbit information and the low-orbit information to form corresponding pre-processed information, and use the error learning model to learn the pre-processed information to determine the receiving error, and combine the high-orbit information and the low-orbit information to determine the three-dimensional information of the moving target; A matching module, which is connected to the positioning module, is used to obtain the motion information of the moving target, and analyzes the three-dimensional information and the motion information of the moving target in combination with the three-dimensional information to form a corresponding navigation result; Among them, the high-orbit information is the position information formed by the position signal received by the high-orbit remote sensing satellite; Low-orbit information is the position information formed by receiving position signals from low-orbit remote sensing satellites; The pre-processing information includes high-orbit processing information and low-orbit processing information; The receiving error is the ranging error caused by the high-orbit remote sensing satellite and the low-orbit remote sensing satellite receiving the position signal; The error learning model is generated by training and debugging the training set formed by the preprocessing information; The motion information includes the speed, acceleration and / or real-time motion direction of the moving object.

[0030] In practice, the orbital altitude of a high-orbit satellite refers to the altitude of the geosynchronous orbit, which is about 36,000 kilometers, while the orbital altitude of a low-orbit satellite is generally between 200 and 2,000 kilometers.

[0031] By setting up a signal transmission module to transmit a position signal of a moving target at a preset interval, a satellite monitoring module to receive the position signal to form corresponding high-orbit information and low-orbit information, a positioning module to pre-process the high-orbit information and the low-orbit information to form corresponding pre-processed information, and use an error learning model to learn the pre-processed information, determine the receiving error, and determine the three-dimensional information of the moving target, a matching module to obtain the motion information of the moving target, and analyze it in combination with the three-dimensional information to form a corresponding navigation result, the integration of multiple information sources and advanced data processing technologies improves the accuracy and reliability of navigation, realizes precise navigation of moving targets, and significantly improves safety and navigation efficiency.

[0032] See also Figure 2 As shown, it is a schematic diagram of the structure of the signal transmission module of the present invention, including: A collector, which is used to collect the position data of the moving target in real time; A transmitter connected to the collector for converting the position data into a position signal; A timing device connected to the transmitter to send a transmission instruction to the transmitter at preset time intervals; The location data includes the latitude and longitude coordinates of the moving target and / or the height information relative to the sea level.

[0033] In a specific implementation, the specific features of the signal transmission module are as follows: Collector: The collector is responsible for collecting the location data of the moving target in real time. This data usually includes the latitude and longitude coordinates of the moving target and the height information relative to the sea level, which is crucial for determining the precise location of the moving target.

[0034] Transmitter: The transmitter is connected to the collector and its function is to convert the collected position data into position signals. These signals can then be received by the satellite monitoring module for subsequent positioning and navigation calculations.

[0035] Timing device: The timing device is connected to the transmitter and is responsible for sending transmission instructions to the transmitter at preset intervals. This means that the transmitter will periodically transmit position signals at preset time intervals to ensure that the signal transmission module can update the position information at a certain frequency, which is very important for maintaining continuous and real-time navigation tracking.

[0036] By setting up a signal transmission module, the position data is converted into a position signal, which can achieve real-time tracking and precise positioning of moving targets. By periodically transmitting a position signal containing latitude, longitude and altitude information, the system is able to provide continuous position updates for moving targets, which is essential to ensure the accuracy and reliability of navigation. In addition, this modular design also allows the system to be customized and optimized according to different application requirements. For example, in some applications, more frequent position updates may be required for more accurate navigation, while in other applications, longer transmission intervals may be required to save energy or meet other technical requirements. By adjusting the settings of the timing device, these different needs can be flexibly adapted.

[0037] See also Figure 3 As shown, it is a schematic diagram of the positioning module structure of the present invention, including: A preprocessor, which is used to preprocess the high orbit information and the low orbit information and generate corresponding preprocessing information; A learner, which is connected to the preprocessor, is used to select several indicator features and learn the preprocessing information, and generate a corresponding receiving error; The filter is connected to the learner to screen and filter erroneous reception errors, and perform three-dimensional calculations in combination with high-orbit information and low-orbit information to generate three-dimensional information corresponding to the moving target.

[0038] In the specific implementation, the positioning module includes the following key components: Preprocessor: The preprocessor is responsible for preprocessing the received high orbit information and low orbit information and generating corresponding preprocessed information. This step is the basis of the entire positioning process and ensures the data quality of subsequent processing.

[0039] Learner: The learner is connected to the preprocessor, and its function is to select several indicator features and learn the preprocessed information and generate the corresponding receiving error. This process involves machine learning algorithms to identify and predict error patterns in the signal, thereby improving the accuracy of positioning.

[0040] Filter: The filter is connected to the learner, and its task is to screen and filter the wrong reception errors, and combine the high orbit information and low orbit information for three-dimensional calculation to generate three-dimensional information corresponding to the moving target. The role of the filter is to ensure that only accurate error data is used for the final positioning calculation, thereby improving the reliability and accuracy of the overall system.

[0041] The learner selects several indicator features and learns the preprocessed information, generates the corresponding receiving error, and combines the high-orbit information and low-orbit information for three-dimensional calculation to generate the three-dimensional information corresponding to the moving target. Through the collaborative work of the preprocessor, learner and filter, the high-orbit and low-orbit information is deeply processed and analyzed, and finally the accurate three-dimensional position information of the moving target is generated. The design of this system takes into account the preprocessing, error learning and screening of the signal to ensure that continuous, stable and reliable high-precision positioning results can be provided in various complex environments, and it is suitable for a variety of scenarios.

[0042] See also Figure 4 As shown, it is a schematic diagram of the matching module structure of the present invention, including: A measuring device, which is used to measure the motion information of the moving target in real time; An analysis device, which is connected to the measuring device, learns the three-dimensional information and motion information, and generates a number of corresponding navigation results; An interactive device, which is connected to the analysis device and is used to display the navigation results and select them by the moving target; Among them, a comprehensive matching model is set in the analysis device to calculate the best path according to the three-dimensional information and motion information to form a corresponding navigation result.

[0043] In a specific implementation, the matching module includes the following components and their functions: Measuring device: The measuring device is responsible for measuring the motion information of the moving target in real time, including but not limited to key parameters such as speed, acceleration, direction, etc. This information is crucial for understanding the dynamic behavior of the moving target and is the basis for subsequent analysis and navigation decisions.

[0044] Analytical device: The analysis device is connected to the measurement device, receives the motion information provided by the measurement device, and learns and analyzes it in combination with the pre-acquired three-dimensional information (including terrain, obstacles, road information, etc.). This step involves processing and pattern recognition of a large amount of data to generate several possible navigation results.

[0045] Interactive device: The interactive device is connected to the analysis device, and its main function is to display the navigation results generated by the analysis device to the user (or the control system of the moving target) in the form of graphics or text. The user or the moving target can make choices based on these navigation results and decide the best action path.

[0046] Comprehensive matching model: The comprehensive matching model is the core algorithm in the analysis device, which calculates the best path based on the input three-dimensional information and motion information. This model integrates multiple algorithms, such as path planning algorithm, obstacle avoidance algorithm, road condition analysis, etc., to ensure that the optimal navigation path is found in a complex environment.

[0047] The motion information is measured in real time by the measuring device, and the corresponding navigation results are generated in combination with the three-dimensional information, so that the navigation system can respond to the dynamic changes of the moving target in real time and provide timely navigation decision support. Through the intelligent analysis of the comprehensive matching model, the system can take into account a variety of factors, such as real-time traffic conditions, terrain changes, obstacles, etc., to generate the best navigation plan. This system is particularly suitable for fields such as autonomous vehicles, drones, robots, etc., which require highly automated and intelligent navigation solutions to improve efficiency and safety.

[0048] Specifically, the transmitter obtains the data length of the position data and determines the byte order of the position data, performs signal calculation based on the data length and byte order, converts the position data into a position signal, and the satellite monitoring module accesses the high-orbit remote sensing satellite and the low-orbit remote sensing satellite, obtains the received position signals, and marks them as corresponding high-orbit information and low-orbit information.

[0049] The specific explanation is as follows: The transmitter obtains the data length and byte order of the position data: The transmitter first needs to determine the data length of the location data, that is, the scale or size of the data, which is critical to correctly interpreting the data. At the same time, the transmitter also needs to determine the byte order of the location data, that is, the order in which the data is stored in memory. This is because different computer systems use different byte orders (big endian or little endian) to store data, and the correct byte order is critical to the correct interpretation of the data.

[0050] Signal calculation: According to the acquired data length and byte order, the transmitter converts the position data into a position signal. This process involves signal calculation, including encoding and modulation of the data, to ensure that the signal can remain stable in different propagation environments and be accurately received by the satellite monitoring module.

[0051] The satellite monitoring module accesses high-orbit and low-orbit remote sensing satellites: The satellite monitoring module is responsible for receiving the location signal sent by the transmitter. This module accesses high-orbit remote sensing satellites and low-orbit remote sensing satellites, which operate in different orbits and can provide wider coverage and more stable signal reception services.

[0052] The transmitter obtains the data length of the position data and determines the byte order of the position data. Signal calculation is performed based on the data length and byte order, and the position data is converted into a position signal, ensuring the efficiency and accuracy of the entire chain from data acquisition to signal processing, which is crucial to the performance of high-precision navigation systems.

[0053] Specifically, the preprocessor cuts the high-orbit information and the low-orbit information according to the sampling rate to form a number of preprocessed information with a standard sampling rate, and selects a number of indicator features based on the preprocessed information. The preprocessed information enters the error learning model for learning and generates the corresponding receiving error. The standard sampling rate is a sampling rate that can be recognized by the error learning model, and the standard sampling rate remains unchanged during the learning process of the error learning model; The indicator features include latitude and longitude coordinates, altitude information and / or distance size.

[0054] In a specific implementation, the standard sampling rate is 160 / second to 180 / second. The error learning model has the best learning effect on the preprocessed information. Preferably, the standard sampling rate is usually 170 / second.

[0055] The specific steps are as follows: Sampling rate adjustment by preprocessor: The preprocessor cuts the received high-orbit information and low-orbit information according to a certain sampling rate. This step is to unify the information from different sources into a standard sampling rate for subsequent processing and analysis. This standardization is necessary because different satellite systems may have different data output frequencies.

[0056] Form some preprocessing information: After the sampling rate is adjusted, the preprocessor forms a series of preprocessed information with the standard sampling rate. These preprocessed information are preliminarily processed data sets, which will be used for further error learning and path calculation.

[0057] Select indicator features: Based on the preprocessing information, several key indicator features are selected, which are crucial for subsequent positioning and navigation. The indicator features include latitude and longitude coordinates, altitude information and / or distance. These features provide the position information of the moving target in three-dimensional space, which is the basis for determining the target position and planning the path.

[0058] The learning process of the error learning model: The preprocessed information enters the error learning model for learning. The error learning model is a well-trained machine learning model that can identify and predict reception errors based on the input preprocessed information. This model uses a standard sampling rate during training to ensure the consistency and accuracy of the model.

[0059] Generate the corresponding receive error: The error learning model generates the corresponding receiving error based on the preprocessing information and the selected indicator features. These error information reflects the ranging error generated by high-orbit and low-orbit remote sensing satellites when receiving position signals, which is crucial for correcting positioning results and improving navigation accuracy.

[0060] Maintaining the standard sampling rate: During the learning process of the error learning model, the standard sampling rate remains unchanged. This is to ensure that the model can stably learn and predict errors without being affected by changes in the sampling rate.

[0061] By cutting the high-orbit information and the low-orbit information according to the sampling rate, a number of pre-processed information with a standard sampling rate is formed, and a number of indicator features are selected according to the pre-processed information to generate corresponding receiving errors, so that the system can accurately identify and correct the errors caused by satellite signal reception, thereby providing more accurate positioning and navigation services.

[0062] Specifically, the filter receives an error and compares the received error to an error threshold, where When the receiving error is less than the error threshold, the receiving error is retained; When the receiving error is greater than the error threshold, the corresponding receiving error is filtered; The error threshold is the maximum value of the reception error generated when receiving high-orbit information and low-orbit information, and is related to the performance of high-orbit remote sensing satellites and low-orbit remote sensing satellites.

[0063] In the specific implementation, the specific process of processing the receiving error is as follows: Comparison of error thresholds: The filter compares each reception error with a preset error threshold. The error threshold refers to the maximum value of the reception error generated during the reception process, which is closely related to the performance of high-orbit remote sensing satellites and low-orbit remote sensing satellites.

[0064] Error screening: If the received error is less than the error threshold, the error is considered acceptable and the filter retains the received error.

[0065] If the received error is greater than the error threshold, the error is considered unacceptable and the filter will filter out the received error and will not use it for subsequent three-dimensional calculations.

[0066] Three-dimensional calculations: After being processed by the filter, the retained receiving errors will be used for three-dimensional calculation together with the high orbit information and the low orbit information to generate three-dimensional information corresponding to the moving target.

[0067] By comparing the received error with the error threshold, the erroneous received error is filtered out to ensure that only reasonable errors are used in the final navigation calculation, thereby improving the accuracy and reliability of the navigation system and helping to reduce navigation deviations due to abnormal errors, especially when the performance differences between high-orbit and low-orbit remote sensing satellites may lead to different error characteristics.

[0068] Specifically, the average value of the high orbit information and the low orbit information is calculated and recorded as the mean information. The filtered receiving error is matched with the time-synchronized mean information, and a three-dimensional calculation is performed, where: The three-dimensional calculation is to perform a multi-point perspective algorithm on the mean information and the receiving error to generate corresponding three-dimensional trajectory points, and connect several three-dimensional trajectory points in time sequence to form the three-dimensional information corresponding to the moving target.

[0069] In the specific implementation, the specific process of three-dimensional calculation is as follows: Get mean information: First, the system calculates the average value of high orbit information and low orbit information, which is recorded as mean information. This step helps to reduce the impact of fluctuations or errors in single satellite information and improve the stability of overall positioning.

[0070] Corresponding received error and mean information: The received errors filtered by the filter are matched with the time-synchronized mean information. This means that each received error is matched with the mean information at the same time point, providing an accurate time reference for subsequent 3D calculations.

[0071] Three-dimensional calculations: The multi-point perspective algorithm is applied to the mean information and the received error to generate the corresponding three-dimensional trajectory points. The multi-point perspective algorithm is a commonly used three-dimensional reconstruction technology, which calculates the position and shape of an object in three-dimensional space by analyzing images or information from different perspectives.

[0072] Connect several 3D trajectory points in time sequence to form the 3D information corresponding to the moving target. This step connects the discrete 3D points into a continuous trajectory, providing complete 3D position and motion information for the moving target.

[0073] This 3D calculation method can comprehensively consider the mean of high-orbit and low-orbit information and the reception error, and generate an accurate 3D trajectory through the connection of multi-point perspective algorithm and time sequence. Through this comprehensive calculation, the system can provide continuous, stable and accurate 3D navigation information to ensure the safety and efficiency of moving targets in complex environments.

[0074] Specifically, the measuring device queries the dynamic behavior of the moving target, obtains the motion information corresponding to the moving target, and inputs the motion information and three-dimensional information into the comprehensive matching model.

[0075] In the specific implementation, the measurement device and the comprehensive matching model work together to achieve accurate navigation of the moving target. The following are the specific steps of this process: Query dynamic behavior: The measurement device is responsible for real-time monitoring and querying the dynamic behavior of the moving target. This includes collecting key motion parameters such as the speed, acceleration, and direction change of the moving target, which are essential for understanding the current state of the moving target and predicting its future behavior.

[0076] Enter the comprehensive matching model: The collected motion information is fed into a comprehensive matching model together with the 3D information previously generated by the multi-point perspective algorithm. This model is the core of the navigation system and is responsible for processing and analyzing this data.

[0077] Comprehensive matching model processing: The comprehensive matching model calculates the best path based on the input three-dimensional information (including position, speed, acceleration, etc.) and motion information. This model integrates multiple algorithms, such as path planning algorithm, obstacle avoidance algorithm, road condition analysis, etc., to ensure that the optimal navigation path is found in complex environments.

[0078] Generate navigation results: The comprehensive matching model generates corresponding navigation results, which include recommended paths, speed recommendations, obstacle avoidance strategies, etc. for moving targets to help them reach their destinations safely and efficiently.

[0079] Through this integrated measurement and matching process, the high-precision integrated navigation system can respond to the dynamic changes of moving targets in real time and provide timely navigation decision support.

[0080] Specifically, the comprehensive matching model performs path planning on motion information and three-dimensional information, generates navigation results, and transmits the navigation results to the interactive device, wherein: Path planning includes obstacle avoidance algorithms and road condition retrieval and analysis.

[0081] In specific implementation, path planning is the core function of the navigation system, which ensures that the moving target can reach the destination safely and efficiently from the current location.

[0082] Obstacle avoidance algorithm is an important part of path planning. It predicts the dynamic changes of collision risk and makes obstacle avoidance decisions to achieve dynamic obstacle avoidance of moving targets.

[0083] Road condition analysis involves analyzing real-time data of the road environment, such as visibility, slope, road curvature, etc. These factors will affect the driving and obstacle avoidance strategies of the moving target. By integrating this information, the comprehensive matching model can provide the moving target with the best path that adapts to the current road conditions.

[0084] In 3D calculation, the multi-point perspective algorithm is used to process the mean information and the received error to generate the corresponding 3D trajectory points. These trajectory points are connected in time sequence to form the 3D information corresponding to the moving target.

[0085] The navigation results generated by the comprehensive matching model will be transmitted to the interactive device so that the moving target or user can make decisions or path selections accordingly.

[0086] The interactive device includes input devices such as touch screen, buttons, voice control, etc., which allow the user to interact with the system, such as selecting a recommended route, entering destination information or confirming navigation prompts, and supports multiple display modes, such as two-dimensional map, three-dimensional view, satellite view, etc., to meet the needs of different users and different navigation scenarios.

[0087] Through this integrated approach, the comprehensive matching model is able to provide accurate path planning and obstacle avoidance strategies while taking into account real-time road conditions to ensure the safety and efficiency of moving targets in complex environments.

[0088] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A high-precision integrated navigation system, characterized in that: include: A signal transmitting module, which is used to transmit a position signal at preset time intervals; A satellite monitoring module, which is connected to the signal transmission module and is used to receive the position signal to form corresponding high orbit information and low orbit information; a positioning module connected to the satellite monitoring module, configured to pre-process the high orbit information and the low orbit information to form corresponding pre-processed information, learn the pre-processed information using an error learning model to determine a receiving error, and determine the three-dimensional information of the moving target by combining the high orbit information and the low orbit information; A matching module, connected to the positioning module, for acquiring the motion information of the moving target, and analyzing the three-dimensional information and the motion information of the moving target in combination with the three-dimensional information to form a corresponding navigation result; Wherein, the high-orbit information is the position information formed by the high-orbit remote sensing satellite receiving the position signal; The low-orbit information is position information formed by a low-orbit remote sensing satellite receiving the position signal; The pre-processing information includes high orbit processing information and low orbit processing information; The receiving error is a ranging error generated when the high-orbit remote sensing satellite and the low-orbit remote sensing satellite receive the position signal.

2. The high-precision integrated navigation system according to claim 1, characterized in that: The signal transmission module comprises: A collector, which is arranged on the moving target and is used to collect the position data of the moving target in real time; a transmitter connected to the collector and configured to convert the position data into a position signal; A timing device, connected to the transmitter, for sending a transmission instruction to the transmitter at preset time intervals; The location data includes the latitude and longitude coordinates of the moving target and / or the height information relative to the sea level.

3. The high-precision integrated navigation system according to claim 2, characterized in that: The positioning module comprises: A preprocessor, used for preprocessing the high orbit information and the low orbit information, and generating corresponding preprocessing information; A learner, connected to the preprocessor, for selecting a number of indicator features and learning the preprocessing information, and generating a corresponding receiving error; A filter is connected to the learner to screen and filter erroneous reception errors, and perform three-dimensional calculations in combination with the high orbit information and the low orbit information to generate three-dimensional information corresponding to the moving target.

4. The high-precision integrated navigation system according to claim 3, characterized in that: The matching module comprises: A measuring device, which is used to measure the motion information of the moving target in real time; An analysis device, connected to the measuring device, learns the three-dimensional information and the motion information and generates a number of corresponding navigation results; An interactive device, connected to the analyzing device, for displaying the navigation result and selecting by the moving target; Wherein, the analysis device is provided with a comprehensive matching model for calculating the best path according to the three-dimensional information and the motion information to form a corresponding navigation result; The motion information includes the speed, acceleration and / or real-time motion direction of the moving target.

5. The high-precision integrated navigation system according to claim 4, characterized in that: The signal transmission module is provided with a transmitter for performing signal calculation according to the data length of the acquired position data and the byte order of the position data to convert the position data into a position signal. The satellite monitoring module accesses the high-orbit remote sensing satellite and the low-orbit remote sensing satellite, acquires the position signal, and marks it as corresponding high-orbit information and low-orbit information.

6. The high-precision integrated navigation system according to claim 5, characterized in that: The preprocessor cuts the high orbit information and the low orbit information according to the sampling rate to form a plurality of preprocessed information with the sampling rate being the standard sampling rate, and selects a plurality of indicator features according to the preprocessed information. The preprocessed information enters the error learning model for learning, and generates a corresponding receiving error. The standard sampling rate is a sampling rate that can be recognized by the error learning model, and during the learning process of the error learning model, the standard sampling rate remains unchanged; The error learning model is generated by training and debugging the training set formed by the preprocessing information; The indicator features include latitude and longitude coordinates, altitude information and / or distance.

7. The high-precision integrated navigation system according to claim 6, characterized in that: The filter receives the reception error and compares the reception error to an error threshold, wherein: When the receiving error is less than the error threshold, retaining the receiving error; When the receiving error is greater than the error threshold, filtering the corresponding receiving error; The error threshold is the maximum value of the reception error generated when the high-orbit information and the low-orbit information are received, and is related to the performance of the high-orbit remote sensing satellite and the low-orbit remote sensing satellite.

8. The high-precision integrated navigation system according to claim 7, characterized in that: The average value of the high orbit information and the low orbit information is obtained and recorded as the mean information, the filtered receiving error is matched with the time-synchronized mean information, and a three-dimensional calculation is performed, wherein, The three-dimensional calculation is to perform a multi-point perspective algorithm on the mean information and the receiving error to generate corresponding three-dimensional trajectory points, and connect several three-dimensional trajectory points in time sequence to form three-dimensional information corresponding to the moving target.

9. The high-precision integrated navigation system according to claim 8, characterized in that: The measuring device queries the dynamic behavior of the moving target, obtains the motion information corresponding to the moving target, and inputs the motion information and the three-dimensional information into the comprehensive matching model.

10. The high-precision integrated navigation system according to claim 9, characterized in that: The comprehensive matching model performs path planning on the motion information and the three-dimensional information, generates the navigation result, and transmits the navigation result to the interaction device, wherein: The path planning includes obstacle avoidance algorithm and road condition retrieval analysis.

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