Satellite positioning acquisition architecture, method and medium
By adopting multi-source signal fusion technology in the GNSS positioning system, combining inertial navigation and real-time dynamic differential algorithm, the problem of degradation of positioning accuracy in complex environments is solved, and high-precision, strong robustness and continuous positioning is achieved.
Patent Information
- Application Number
- CN202510281565.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-11
- Publication Date
- 2025-06-13
AI Technical Summary
The existing GNSS positioning technology has reduced positioning accuracy in complex environments, RTK is highly dependent on the base station network, error accumulation during long-term use of INS, and multimodal signal fusion technology is difficult to achieve high-precision positioning.
The satellite positioning acquisition architecture is adopted, through the coordinated work of the communication module, the microprocessor unit and at least two positioning modules, the GNSS satellite signal, the RTK network signal and the map matching algorithm signal are received, and the multi-source signal fusion is combined with the inertial navigation algorithm and the real-time dynamic differential algorithm.
It realizes high-precision positioning in complex environments, improves the robustness and reliability of the positioning system, and outputs standardized packets for system integration.
Smart Images
Figure CN120143205A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of satellite positioning, and more particularly to a satellite positioning acquisition architecture, method, and medium. Background Art
[0002] Existing GNSS positioning technologies mainly rely on global navigation satellite systems (such as GPS, Beidou, GLONASS, and Galileo), combined with RTK (Real-Time Kinematic) technology, to achieve relatively high positioning accuracy. RTK corrects the errors in satellite signals by receiving differential signals from base stations and is widely used in fields such as autonomous driving, drones, and precision agriculture. At the same time, INS (Inertial Navigation System), as a supplement to GNSS, uses data from accelerometers and gyroscopes to provide high-precision position and heading information without relying on signals in a short period of time. In addition, some solutions introduce map matching algorithms to align positioning data with high-precision maps, thereby further optimizing navigation accuracy and path recognition.
[0003] However, the existing technologies have significant deficiencies when facing complex environments. For example, a single positioning signal is vulnerable to occlusion and multipath effects in urban canyons, under overpasses, or in tunnels, resulting in a decrease in positioning accuracy. At the same time, RTK is highly dependent on the base station network and cannot work properly in weak signal or signal-free areas. In addition, the errors of INS will accumulate during long-term use, and it cannot provide reliable high-precision positioning alone. Existing multi-modal signal fusion technologies are not sufficient to effectively process multi-source data and are difficult to achieve high-precision positioning simultaneously in dynamic and static scenarios, which limits their wide application in fields such as vehicle networking and autonomous driving. Summary of the Invention
[0004] In view of the above-mentioned disadvantages of the existing technologies, the purpose of the present invention is to provide a satellite positioning acquisition architecture, method, and medium to achieve high-precision, strong robustness, and continuous positioning, which can be widely applied to fields such as autonomous driving, intelligent transportation, and high-precision navigation.
[0005] To achieve the above purpose, the present invention adopts the following technical solutions.
[0006] In a first aspect, a satellite positioning acquisition architecture provided by the present invention adopts the following technical solutions: A communication module, which includes an application service support layer and a basic software layer; A microprocessor unit, which includes an inertial navigation algorithm and a real-time kinematic algorithm; At least two positioning modules, each positioning module respectively includes a global navigation satellite system engine; Wherein, the architecture is configured to receive real-time kinematic network signals, map matching algorithm signals, and satellite positioning signals, and output preset standardized message signals by processing the above-mentioned signals.
[0007] Further, in the satellite positioning acquisition architecture, the at least two positioning modules include a first positioning module built into the communication module and at least one external second positioning module.
[0008] Further, in the satellite positioning acquisition architecture, in the second positioning module, at least one is configured to provide a static heading angle.
[0009] Further, in the satellite positioning acquisition architecture, the real-time kinematic differential algorithm is preferentially arranged in the microprocessor unit.
[0010] Further, in the satellite positioning acquisition architecture, the inertial navigation algorithm is configured to receive signals from an inertial measurement unit, an inertial sensor, an accelerometer, or a gyroscope.
[0011] Further, in the satellite positioning acquisition architecture, the preset standardized message signal includes longitude and latitude, the number of valid satellites, and signal ratio information.
[0012] Further, in the satellite positioning acquisition architecture, the satellite positioning signal is simultaneously transmitted to the first positioning module and the second positioning module.
[0013] Further, in the satellite positioning acquisition architecture, the architecture processes the satellite positioning signal by combining the real-time kinematic differential algorithm, the map matching algorithm, and the inertial navigation algorithm to obtain a multi-source global navigation satellite system signal.
[0014] In a second aspect, a satellite positioning acquisition method provided by the present invention is applied to the satellite positioning acquisition architecture according to any one of the above first aspects, and the method includes: Receiving a real-time kinematic differential network signal, a map matching algorithm signal, and a satellite positioning signal; In the communication module, preliminarily processing the satellite positioning signal and the real-time kinematic differential network signal to obtain a first positioning signal; In the positioning module, processing the satellite positioning signal to obtain a second positioning signal; In the microprocessor unit, integrating the first positioning signal and the second positioning signal to obtain a multi-source positioning signal; And performing matching and calibration with the map matching algorithm signal in the communication module, and outputting the standardized message signal.
[0015] Further, in the above satellite positioning acquisition method, it further includes: In the microprocessor unit, using the inertial navigation algorithm to perform positioning compensation on the multi-source positioning signal.
[0016] In a third aspect, a readable storage medium provided by the present invention adopts the following technical solution: A readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method described in any one of the above second aspects is implemented.
[0017] In summary, compared with the prior art, the present invention includes at least one of the following beneficial technical effects: The satellite positioning acquisition architecture described in the present invention realizes multi-source fusion, high-precision positioning, and enhanced robustness through the collaborative work of a communication module, a microprocessor unit (MPU), and at least two positioning modules. First, the architecture simultaneously receives GNSS satellite signals, RTK network signals, and map matching algorithm signals, and combines the inertial navigation algorithm (INS) and the real-time kinematic algorithm (RTK) in the MPU to maintain high-precision positioning in complex environments (such as tunnels and urban canyons). Second, at least two independent positioning modules provide redundant positioning information, further improving the positioning stability and reliability. Finally, the architecture outputs a standardized message to ensure compatibility and system integration convenience, and can be widely applied to fields such as vehicle networking, intelligent driving, and high-precision navigation. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.
[0019] Figure 1 is a network topology diagram of a specific embodiment of a satellite positioning acquisition architecture of the present invention.
[0020] Figure 2 is a flowchart of a specific embodiment of a satellite positioning acquisition method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application. In addition, it should be understood that the specific embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application.
[0022] It should be noted that the description order of the following embodiments does not limit the preferred order of the embodiments of the present application. And in the following embodiments, each embodiment is described with its own emphasis. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.
[0023] For the method steps described in the embodiments of the present invention, their execution order can be the order described in the specific implementation manner, or can be adjusted according to actual needs on the premise of being able to solve the technical problem. The execution orders are not listed one by one here.
[0024] Refer to Figure 1 , a satellite positioning acquisition architecture, including a communication module, a microprocessor unit, and multiple positioning modules.
[0025] The communication module includes an application service support layer and a basic software layer, and is used to process and manage external input signals and data interaction within the system. The basic software layer deploys the RTK (Real-Time Kinematic) algorithm to preprocess the received GNSS satellite signals, RTK network signals, and map matching algorithm signals, and provides high-precision positioning data to the upper layer. The application service support layer interacts with the microprocessor unit (MPU) and the positioning module through the signal interface API, transmits the fused data information, and finally outputs a standardized message (such as an NMEA protocol message). In addition, the communication module can be connected to a remote server or a base station through a wireless communication module to obtain differential correction data to ensure continuous optimization of the positioning accuracy.
[0026] The microprocessor unit contains an inertial navigation algorithm and a real-time kinematic algorithm. Specifically, the microprocessor unit (MPU) integrates the inertial navigation algorithm (INS) and the real-time kinematic (RTK) algorithm, and is responsible for fusing multi-source positioning data and improving the system positioning accuracy and robustness. INS obtains accelerometer and gyroscope data through the IMU, and provides short-time GNSS-free high-precision positioning when the GNSS signal is blocked (such as when the vehicle is in a tunnel or under an overpass). The RTK algorithm processes the RTK network signal and the GNSS satellite signal from the communication module, and greatly improves the positioning accuracy to the centimeter level through base station differential correction. The MPU is also responsible for coordinating the Global Navigation Satellite System Engine (GNSS Engine) of the positioning module to ensure the consistency of different data sources, and finally outputs an optimized standardized message for external use.
[0027] Each positioning module contains a Global Navigation Satellite System (GNSS) engine. Specifically, the positioning module includes at least two GNSS Engines, which are used to receive and process GNSS satellite signals and combine with other sensor data to achieve high-precision positioning. The first GNSS Engine can be used to calculate the static heading angle and provide the direction information of the vehicle or device, ensuring accurate determination of the orientation even at low speeds or in a stationary state. The second GNSS Engine is responsible for processing dynamic GNSS data, combining with the RTK algorithm and INS inertial navigation algorithm in the microprocessor unit (MPU) to improve the positioning accuracy and stability. In addition, the positioning module maintains data interaction with the communication module and the MPU to ensure the fusion processing of multi-source data and finally output a standardized message conforming to the NMEA protocol for navigation, autonomous driving or other high-precision positioning applications.
[0028] The architecture is configured to receive real-time kinematic (RTK) network signals, map matching algorithm signals, and satellite positioning signals, and output a preset standardized message signal by processing the above signals.
[0029] Specifically, the architecture realizes the reception, processing, and fusion of multi-source signals through the collaborative work of the communication module, the microprocessor unit (MPU), and the positioning module. The communication module is responsible for receiving RTK network signals, map matching algorithm signals, and GNSS satellite signals. The RTK algorithm inside the APSS basic software layer processes RTK differential correction data to improve the accuracy of GNSS signals. The MPU deploys the INS inertial navigation algorithm to perform short-term position prediction using IMU data when GNSS signals are blocked and combines with the RTK algorithm for multi-modal fusion calculation. The positioning module contains at least two GNSS Engines, which process the static heading angle and dynamic GNSS data respectively to further enhance the positioning accuracy and stability. Finally, the application layer API of the communication module integrates all the processed data and formats it according to the NMEA protocol to generate a standardized message signal for use in navigation, autonomous driving, and other applications.
[0030] The satellite positioning acquisition architecture of the present invention realizes multi-source fusion, high-precision positioning, and enhanced robustness through the collaborative work of the communication module, the microprocessor unit (MPU), and at least two positioning modules. First, the architecture simultaneously receives GNSS satellite signals, RTK network signals, and map matching algorithm signals, and combines the inertial navigation algorithm (INS) and real-time kinematic algorithm (RTK) in the MPU to maintain high-precision positioning even in complex environments (such as tunnels, urban canyons). Second, at least two independent positioning modules provide redundant positioning information to further improve the positioning stability and reliability. Finally, the architecture outputs a standardized message to ensure compatibility and convenience of system integration, and can be widely applied to fields such as vehicle networking, intelligent driving, and high-precision navigation.
[0031] Further, as an implementation manner of the present invention, the at least two positioning modules include a first positioning module built into the communication module and at least one external second positioning module. Among the second positioning modules, at least one is configured to provide a static heading angle.
[0032] The first positioning module is built into the communication module. Its core components include a GNSS Engine and an RTK algorithm, which are mainly responsible for preliminarily processing the received GNSS satellite signals and RTK network signals and providing high-precision positioning data. The first positioning module is directly integrated into the APSS basic software layer of the communication module and interacts with the signal interface API of the APSS application layer to achieve real-time transmission of positioning data. Due to the high integration of the first positioning module and the communication module, it can efficiently receive and parse GNSS signals, and at the same time call the RTK algorithm in APSS for differential correction, significantly improving the positioning accuracy. In addition, this positioning module can also cooperate with the INS inertial navigation algorithm of the MPU to provide short-term GNSS-free positioning compensation when satellite signals are blocked, and finally uniformly output the optimized positioning data to the communication module for generating standardized message signals.
[0033] The second positioning module is an external module, which at least includes an independent GNSS Engine for receiving and processing GNSS satellite signals and fusing with the data of the first positioning module to improve the overall positioning accuracy and system redundancy. The second positioning module can be deployed in the external antenna of the vehicle or within a specific sensor unit, which can provide an additional GNSS signal source and support static heading angle calculation to ensure accurate determination of the vehicle direction in a low-speed or stationary state. In addition, the second positioning module conducts data interaction with the RTK algorithm and INS inertial navigation algorithm of the MPU, so that when the RTK network signal or GNSS signal is blocked, it can still rely on inertial navigation and multi-modal data fusion to maintain the positioning accuracy. Finally, the second positioning module synthesizes the processed data with the data of the first positioning module and transmits it to the system core through the APSS application layer of the communication module to generate standardized message signals that conform to the NMEA protocol to provide high-precision positioning support.
[0034] Further, the real-time kinematic differential algorithm is preferentially arranged in the microprocessor unit.
[0035] Specifically, the real-time dynamic differential (RTK) algorithm is preferentially arranged in the microprocessor unit (MPU), using the high computing power of the MPU to perform differential calculations on the received RTK network signals and GNSS satellite signals to improve positioning accuracy. The MPU receives the differential data sent by the RTK base station through the communication module, and combines it with the original satellite signal provided by the GNSS Engine to calculate the error correction to achieve centimeter-level positioning accuracy. In addition, the MPU also integrates the INS inertial navigation algorithm, which can use the data of the inertial measurement unit (IMU) for short-term positioning compensation when the GNSS signal is blocked to ensure positioning stability. Finally, the MPU transmits the fused high-precision positioning data to the APSS application layer of the communication module, and outputs standardized messages that comply with the NMEA protocol through the signal interface API for use by external systems.
[0036] Further, as an embodiment of the present invention, the inertial navigation algorithm is configured to receive a signal from an inertial measurement unit, an inertial sensor, an accelerometer or a gyroscope.
[0037] Specifically, the inertial navigation algorithm (INS) is deployed in a microprocessor unit (MPU) and is configured to receive signals from an inertial measurement unit (IMU), inertial sensors, accelerometers, and gyroscopes, and to calculate the vehicle's attitude, speed, and position information. When the GNSS signal is stable, the INS is combined with the RTK algorithm to provide accurate inertial compensation; when the GNSS signal is blocked (such as in tunnels, underground parking lots, or areas blocked by high-rise buildings), the INS uses the angular velocity, acceleration, and other data collected by the accelerometer and gyroscope to calculate the vehicle's motion trajectory in a short period of time through integral operations, thereby achieving high-precision positioning in a non-GNSS environment. The algorithm performs data fusion with the GNSS Engine in real time, and uniformly outputs standardized messages that comply with the NMEA protocol through the APSS application layer of the communication module to ensure high accuracy and continuity of positioning data.
[0038] Furthermore, as an implementation mode of the present invention, the standardized message signal includes longitude and latitude, number of valid satellites and signal ratio information.
[0039] Specifically, the standardized message signal is generated by the APSS application layer of the communication module. Based on the NMEA protocol format, it can generate key positioning data such as latitude and longitude, the number of valid satellites, and signal ratio information. First, the GNSS Engine processes and analyzes satellite signals to calculate the current latitude and longitude coordinates. Secondly, the RTK algorithm and the INS inertial navigation algorithm further optimize the positioning data to improve accuracy and stability. The communication module also reads the number of valid satellites from the GNSS Engine to reflect the reliability of the current positioning and calculates the signal ratio (SNR, signal-to-noise ratio) to evaluate the GNSS signal quality. Finally, this data is output via the signal interface API to form a standardized message conforming to the NMEA 0183 protocol, which can be parsed and used by navigation systems, autonomous driving platforms, and other external devices.
[0040] Further, as an implementation manner of the present invention, the satellite positioning signal is simultaneously transmitted to the first positioning module and the second positioning module.
[0041] Specifically, after the satellite positioning signal is received by the GNSS antenna, it is first parsed by the communication module and simultaneously transmitted to the first positioning module (built into the communication module) and the second positioning module (external). The first positioning module relies on the GNSSEngine to process basic satellite positioning data and performs differential correction in combination with the RTK network signal to improve accuracy. The second positioning module, as an external independent GNSS receiving unit, receives the same satellite signal and is specifically used to calculate the static heading angle or provide redundant positioning information to enhance the robustness and anti-interference ability of the system. After both calculate and optimize the positioning data respectively, the results are transmitted to the microprocessor unit (MPU), and are further fused and optimized by the RTK algorithm and the INS inertial navigation algorithm to ensure that the final output standardized NMEA protocol message has higher positioning accuracy and stability.
[0042] Further, the architecture processes the satellite positioning signal by combining the real-time kinematic differential algorithm, the map matching algorithm, and the inertial navigation algorithm to obtain multi-source global navigation satellite system signals.
[0043] Specifically, the architecture realizes high-precision positioning of multi-source Global Navigation Satellite System (GNSS) signals through the fusion processing of Real-Time Kinematic (RTK), map matching algorithm, and Inertial Navigation System (INS). First, the GNSS antenna receives satellite signals and simultaneously transmits them to the first positioning module (built into the communication module) and the second positioning module (external) for preliminary analysis. Then, the RTK algorithm in the Micro-Processor Unit (MPU) combines the RTK base station network signal for differential correction to eliminate the errors in the GNSS signal and improve the centimeter-level positioning accuracy. Meanwhile, the INS inertial navigation algorithm receives data from the IMU, accelerometer, and gyroscope to provide short-time satellite-free signal positioning compensation when the GNSS signal is blocked, ensuring positioning continuity. Finally, the map matching algorithm is deployed on the backend server to match and correct the GNSS positioning data with the high-precision map, further improving the positioning accuracy and road constraint. All the fused data is processed by the APSS application layer of the communication module and output as a standardized message in the NMEA protocol format for use by navigation systems, autonomous driving platforms, and other applications.
[0044] Based on the satellite positioning acquisition architecture described in any of the above embodiments, referring to Figure 2 , the embodiment of the present invention also discloses a satellite positioning acquisition method, including the following sub-steps.
[0045] S1. Receive Real-Time Kinematic network signals, map matching algorithm signals, and satellite positioning signals.
[0046] Specifically, in step S1, the communication module receives Real-Time Kinematic (RTK) network signals through a wireless communication module. The signals come from a Ground-Based Augmentation System (CORS network or local RTK base station) and are used for differential correction of GNSS positioning errors. Meanwhile, the GNSS antenna receives satellite positioning signals, including positioning data provided by Global Navigation Satellite System (GNSS) such as GPS, Beidou, GLONASS, or Galileo, and transmits the data to the first positioning module (built into the communication module) and the second positioning module (external) for preliminary analysis. In addition, the APSS application layer of the communication module is connected to the backend map server through a network to obtain map matching algorithm signals, which contain the geographical information and road constraint data of the high-precision map and are used for subsequent positioning optimization. All the received signals are recorded by the communication module and transmitted to subsequent modules for further processing.
[0047] S2. In the communication module, preliminarily process the satellite positioning signals and the Real-Time Kinematic network signals to obtain a first positioning signal.
[0048] Specifically, in step S2, the communication module preliminarily processes the received satellite positioning signal and real-time kinematic (RTK) network signal to generate a first positioning signal. First, the satellite signal received by the GNSS antenna is transmitted to the first positioning module inside the communication module. This module includes a GNSS Engine for parsing satellite data and providing basic position information. Meanwhile, the RTK algorithm deployed in the APSS basic software layer of the communication module processes the differential data received from the RTK base station or CORS network to correct the GNSS positioning error and improve the positioning accuracy. The communication module integrates the GNSS parsing result and the RTK differential correction data to generate a preliminary first positioning signal, which is transmitted to the microprocessor unit (MPU) through the signal interface API for further fusion calculation.
[0049] S3. In the positioning module, process the satellite positioning signal to obtain a second positioning signal.
[0050] Specifically, in step S3, the second positioning module (external) independently processes the satellite positioning signal to generate a second positioning signal. First, the GNSS antenna transmits the received GNSS satellite signal to the GNSS Engine of the second positioning module. The second positioning module parses the satellite data and calculates the position information and static heading angle. Secondly, the second positioning module can operate independently, avoiding being affected by the signal processing of the communication module, thereby providing an additional GNSS data source and enhancing the positioning stability and redundancy of the system. In some embodiments, the second positioning module can also be combined with an IMU (inertial measurement unit) to provide inertial positioning compensation within a short period. Finally, the second positioning signal is transmitted to the microprocessor unit (MPU) through the data interface for fusion processing with the first positioning signal to improve the positioning accuracy and reliability of the overall system.
[0051] S4. In the microprocessor unit, integrate the first positioning signal and the second positioning signal to obtain a multi-source positioning signal.
[0052] Specifically, in step S4, the microprocessor unit (MPU) receives and fuses the first positioning signal (from the communication module) and the second positioning signal (from the external positioning module) to generate a multi-source positioning signal. First, the MPU runs the real-time kinematic (RTK) algorithm to further optimize the error correction result of the first positioning signal, and processes the data of the IMU (inertial measurement unit), accelerometer, and gyroscope in combination with the inertial navigation (INS) algorithm to achieve short-time positioning compensation when the GNSS signal is blocked. Subsequently, the MPU performs data comparison, error correction, and consistency optimization on the first positioning signal and the second positioning signal through a multi-sensor data fusion algorithm to ensure that the final positioning result has higher accuracy and stability. The fused multi-source positioning signal is transmitted to the APSS application layer of the communication module for subsequent map matching and standardized message output.
[0053] S5, and perform matching calibration with the map matching algorithm signal in the communication module, and output the standardized message signal.
[0054] Specifically, in step S5, the APSS application layer of the communication module receives the multi-source positioning signal from the microprocessor unit (MPU) and performs matching calibration with the map matching algorithm signal to generate a high-precision standardized message signal. First, the communication module obtains the map matching algorithm signal through the network interface, which includes high-precision map data, road constraint information, and historical trajectory data. Then, the map matching algorithm combines the current vehicle position and the high-precision map to perform error correction and road constraint optimization on the multi-source positioning signal to ensure that the final positioning result conforms to the road topology structure and avoid drift or jump. Finally, the standardized message signal after matching calibration is formatted into the NMEA protocol, including core positioning information such as latitude and longitude, the number of valid satellites, and the signal ratio, and is output to the navigation system, the autonomous driving platform, or other downstream applications through the signal interface API.
[0055] Further, as an implementation manner of the present invention, the satellite positioning acquisition method further includes: In the microprocessor unit, use the inertial navigation algorithm to perform positioning compensation on the multi-source positioning signal.
[0056] Specifically, the microprocessor unit (MPU) runs the inertial navigation algorithm (INS) to perform positioning compensation on the multi-source positioning signals, ensuring high-precision positioning even when GNSS signals are blocked or lost. First, the MPU receives data from the inertial measurement unit (IMU), accelerometer, and gyroscope, and calculates the motion state of the vehicle or device, including acceleration, angular velocity, heading angle, and position change, in combination with the INS algorithm. When GNSS signals are stable, INS data is used to assist GNSS positioning, improving positioning accuracy and robustness; when GNSS signals are blocked (such as in tunnels, underground parking lots, or under viaducts), INS uses inertial measurement data to estimate position information for a short time to ensure continuous positioning. Finally, the positioning result compensated by INS is fused into the multi-source positioning signals and transmitted to the APSS application layer of the communication module, ensuring that the output standardized message signals have high precision and stability in various complex environments.
[0057] The satellite positioning acquisition method described in the embodiments of the present invention significantly improves the accuracy, stability, and environmental adaptability of the positioning system by fusing GNSS satellite signals, RTK network signals, map matching algorithm signals, and inertial navigation data. Compared with traditional GNSS positioning methods, this method adopts a multi-positioning module architecture, including a first positioning module (built into the communication module) and a second positioning module (external), ensuring the redundancy of positioning data and improving the anti-interference ability. At the same time, the microprocessor unit (MPU) integrates the real-time kinematic (RTK) algorithm and the inertial navigation (INS) algorithm. When GNSS signals are blocked (such as when the vehicle is in a tunnel, urban canyon, or under a viaduct), INS can perform short-term trajectory estimation based on IMU data to ensure continuous positioning in a GNSS-free environment. In addition, through the map matching algorithm, the positioning data is fused and corrected with the high-precision map to improve the road constraint accuracy and effectively reduce the drift error. Finally, this method outputs standardized message signals compliant with the NMEA protocol, which can be applied to scenarios such as autonomous driving, intelligent transportation, and high-precision navigation, and has higher reliability, applicability, and engineering feasibility.
[0058] The embodiments of the present invention also disclose a readable storage medium.
[0059] A readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of a satellite positioning acquisition method described in any one of the above embodiments. The computer-readable storage medium may include: any entity or device capable of carrying the computer program, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), and a software distribution medium, etc. The computer program includes computer program code. The computer program code may be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), and a software distribution medium, etc.
[0060] Any process or method description shown in the flowchart or described in other ways herein can be understood to represent a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process. And the scope of the preferred embodiments of the present invention includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed. This should be understood by those skilled in the technical field to which the embodiments of the present invention belong.
[0061] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processing module, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices.
[0062] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A satellite positioning acquisition architecture, characterized in that: include: The communication module, which includes the application service support layer and the basic software layer; A microprocessor unit that contains the inertial navigation algorithm and the real-time dynamic difference algorithm; at least two positioning modules, each positioning module comprising a global navigation satellite system engine; The architecture is configured to receive real-time dynamic differential network signals, map matching algorithm signals and satellite positioning signals, and process the above signals to output preset standardized message signals.
2. The satellite positioning acquisition architecture according to claim 1, characterized in that: The at least two positioning modules include a first positioning module built into the communication module and at least one external second positioning module.
3. The satellite positioning acquisition architecture according to claim 2, characterized in that: At least one of the second positioning modules is configured to provide a static heading angle.
4. The satellite positioning acquisition architecture according to claim 1, characterized in that: The real-time dynamic differentiation algorithm is preferably arranged in the microprocessor unit.
5. The satellite positioning acquisition architecture according to claim 1, characterized in that: The inertial navigation algorithm is configured to receive signals from an inertial measurement unit, an inertial sensor, an accelerometer, or a gyroscope.
6. The satellite positioning acquisition architecture according to claim 1, characterized in that: The preset standardized message signal includes latitude and longitude, number of valid satellites and signal ratio information.
7. The satellite positioning acquisition architecture according to claim 2, characterized in that: The satellite positioning signal is transmitted to the first positioning module and the second positioning module simultaneously.
8. The satellite positioning acquisition architecture according to claim 7, characterized in that: The architecture processes the satellite positioning signal by combining the real-time dynamic difference algorithm, the map matching algorithm and the inertial navigation algorithm to obtain a multi-source global navigation satellite system signal.
9. A satellite positioning acquisition method, applied to the satellite positioning acquisition architecture as claimed in any one of claims 1 to 7, characterized in that: The method comprises: Receive real-time dynamic differential network signals, map matching algorithm signals and satellite positioning signals; In the communication module, the satellite positioning signal and the real-time dynamic differential network signal are preliminarily processed to obtain a first positioning signal; In the positioning module, the satellite positioning signal is processed to obtain a second positioning signal; In the microprocessor unit, the first positioning signal and the second positioning signal are integrated to obtain a multi-source positioning signal; The communication module is used to match and calibrate the map matching algorithm signal and output the preset standardized message signal.
10. The satellite positioning acquisition method according to claim 9, characterized in that: Also includes: In the microprocessor unit, an inertial navigation algorithm is used to perform positioning compensation on the multi-source positioning signals.
11. A readable storage medium, characterized in that: The readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the satellite positioning acquisition method as claimed in claim 9 or 10 is implemented.