Open architecture multi-source fusion navigation system integration system and method

Abstract

The application discloses an open-architecture multi-source fusion navigation system integration system and method, relates to the technical field of multi-source fusion navigation, and can cover multi-source fusion navigation system design and testing on a single digital twin platform, and when different digital twin platforms are deployed on different terminals, better interactivity can be realized. The system comprises a signal generation terminal and an algorithm test terminal. The signal generation terminal is used for building a digital twin platform to complete the building of a test virtual scene, and comprises a carrier library module, a scene library module and a sensor library module. The algorithm test terminal comprises a data processing module, a disturbance library module, an algorithm library module and a visualization module. The open-architecture multi-source fusion navigation system integration system is adopted, and the integration method specifically comprises a signal generation terminal integration method, an algorithm test terminal integration method and a cross-terminal joint simulation test integration method.

Description

Technical Field

[0001] This invention relates to the field of multi-source fusion navigation technology, specifically to an open-architecture multi-source fusion navigation system integration system and method. Background Technology

[0002] Multi-source fusion navigation is a key technological development direction for application terminals under the national integrated PNT (Positioning, Navigation, and Timing) system. Due to the diverse carriers, application scenarios, and navigation information sources, the design, implementation, and testing of multi-source fusion navigation systems under real-world traffic conditions are expensive, time-consuming, complex, and often unreplicable. This makes using models to represent actual carriers and conducting intensive simulation testing within digital twin platforms the most effective method for verifying multi-source fusion navigation systems. However, current digital twin platforms have two limitations: First, the diverse application areas mean that different digital twin platforms have their own advantages in carrier modeling, virtual scene simulation, dynamics simulation, and algorithm development and testing, making it impossible for a single digital twin platform to cover all aspects of multi-source fusion navigation system design and testing. Second, poor interoperability between different digital twin platforms makes it difficult to integrate and collaboratively develop these platforms, as some platforms need to be deployed on different terminals.

[0003] Currently, there is no solution to address the aforementioned limitations of existing digital twin platforms. Summary of the Invention

[0004] In view of this, the present invention provides an open architecture multi-source fusion navigation system integration system and method, which can cover the design and testing of multi-source fusion navigation systems on a single digital twin platform, and can achieve good interactivity when different digital twin platforms are deployed on different terminals.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] An open-architecture multi-source fusion navigation system integration system includes a signal generation terminal and an algorithm testing terminal.

[0007] The signal generating terminal is used to build a digital twin platform to complete the construction of test virtual scenarios, including a carrier library module, a scene library module, and a sensor library module.

[0008] The carrier library module is used to obtain vehicle models through vehicle modeling and pedestrian models through pedestrian modeling.

[0009] The Scene Library module is used for building and rendering virtual scenes.

[0010] The sensor module is used for modeling and deploying various sensors used in multi-source fusion navigation systems.

[0011] The algorithm testing terminal includes a data processing module, a perturbation library module, an algorithm library module, and a visualization module.

[0012] The data processing module is used to receive raw data from various sensors from the signal generating terminal and process the raw sensor data to generate standard attitude, velocity, and position navigation information.

[0013] The perturbation library module is used to simulate various types of perturbations during the simulation process.

[0014] The algorithm library is used to develop and test various multi-source fusion navigation algorithms.

[0015] The visualization module is used to display simulation scenes and real-time data.

[0016] This invention also provides an open-architecture multi-source fusion navigation system integration method. The method employs the aforementioned open-architecture multi-source fusion navigation system integration system and performs the following integration method, specifically including a signal generation terminal integration method, an algorithm testing terminal integration method, and a cross-terminal joint simulation testing integration method:

[0017] The signal generation terminal integration method involves selecting a digital twin platform in the signal generation terminal to complete the construction of the test virtual scene, the establishment of the carrier model, and the deployment of sensors, thereby constructing a digital twin environment that simulates the real world, serving as the signal generation source for testing the multi-source fusion navigation system.

[0018] The algorithm testing terminal integration method involves selecting mathematical processing software within the terminal to complete signal source data acquisition and processing, algorithm development and testing, disturbance injection, and result visualization. This enables the development, debugging, and optimization of various types of multi-source fusion navigation algorithms, ensuring their normal operation and achieving the expected results under different disturbance conditions.

[0019] The cross-terminal joint simulation test integration method packages the signal source data of the digital twin scene simulation process in the signal generating terminal according to the format, and then transmits it to the algorithm test terminal in real time through wireless network communication or wired transmission, thereby realizing the joint debugging of the designed multi-source fusion algorithm by the signal generating terminal and the algorithm test terminal.

[0020] Furthermore, the signal generating terminal integration method includes the following steps:

[0021] Step S1: Build the scenario required by the task, then load the corresponding world map, import the relevant environmental object models, and obtain the simulation scenario.

[0022] Step S2: Pre-build a carrier library. The carrier models in the carrier library include vehicle models obtained through vehicle modeling and pedestrian models obtained through pedestrian modeling. Import the carrier models.

[0023] The system checks if the required vehicle model exists in the carrier library. If it does, the system imports the relevant vehicle model. If it does not exist, the system models the vehicle according to the model file format supported by the carrier library module, sets its physical properties and dynamics model, obtains the relevant vehicle model, and imports it.

[0024] Step S3: Deploy simulated sensors based on the imported vehicle model.

[0025] Step S4: By setting control scripts, the vehicle model can perform actions according to the set tasks in the virtual environment. At the same time, the parameters of the sensor model can be adjusted according to actual needs to achieve unified deployment of the scene library, carrier library and sensor library.

[0026] Furthermore, the algorithm testing terminal integration method includes the following steps:

[0027] Step S1: The data processing module freely selects and receives measurement data from the target sensor, preprocesses the raw data, stores the processed usable information in the form of a data packet, and sends it to the algorithm library module for processing.

[0028] Step S2: After obtaining valid sensor data, call the existing algorithms in the algorithm library module to perform navigation calculations on the data, and test and verify the performance of the algorithms.

[0029] Step S3: Generate random disturbance signals in the disturbance library module and inject them into the sensor's measurement data to test whether the robustness and anti-interference capability of the multi-source fusion navigation system meet the requirements under different disturbance conditions.

[0030] Step S4: Observe the simulation scene and real-time data through the visualization library module, and observe the real-time dynamic curve of the solution result of the navigation algorithm used. Adjust the algorithm parameters through interface interaction.

[0031] Furthermore, the cross-terminal co-simulation test integration method includes the following steps:

[0032] Step S1: Connect the signal generating terminal and the algorithm testing terminal for communication. The communication connection method is either wireless connection via the same local area network or wired connection via serial communication.

[0033] Step S2: Create communication nodes. In the multi-source fusion navigation integration system, each execution unit is a data communication node. The execution units in the carrier library module are carrier models, the execution units in the sensor module are sensors, and the execution units in the algorithm library module are various navigation algorithms. The communication nodes created in this way include carrier nodes, sensor nodes, and navigation algorithm nodes.

[0034] Step S3: The signal generating terminal and the algorithm testing terminal communicate through a publisher / subscriber pattern. Each node of the signal generating terminal publishes messages on a set topic, and each node of the algorithm testing terminal can subscribe to and receive these messages.

[0035] Step S4: Define message type. Each type of signal source data that needs to be transmitted is a message. Define the message format according to the type of data.

[0036] Step S5: Set up a communication network controller to track communication nodes and messages in the multi-source fusion navigation system. All communication nodes are connected to the communication network controller so that all communication nodes can find each other.

[0037] Step S6: Start the communication nodes of the signal generating terminal and the algorithm testing terminal. The communication node of the signal generating terminal packages and publishes the signal source data to be transmitted in an agreed format, while the communication node of the algorithm testing terminal receives the data in the agreed format, so as to realize the real-time transmission of signal source data between the two terminals.

[0038] Beneficial effects:

[0039] 1. This invention provides an open-architecture multi-source fusion navigation system integration system that can simulate various stages in real-world testing of multi-source fusion navigation systems. This enables more modular, streamlined, and standardized simulation testing of multi-source fusion navigation systems, resulting in more realistic and reliable test results with significant reference value for actual system testing. Furthermore, this device supports third-party developers integrating their own navigation data and services through interfaces, thereby further enriching the system's navigation functions, promoting the integration of different navigation data and services, and improving the adaptability and flexibility of the multi-source fusion navigation system.

[0040] 2. This invention provides an integration method for a multi-source fusion navigation system, in which different digital twin platforms can be deployed on different signal generating terminals. This allows for the comprehensive utilization of the advantages of existing digital twin platforms to complete carrier modeling and dynamic simulation, large-scale sensor deployment, switching between multiple complex scenarios, and autonomous design and testing of multi-source fusion navigation algorithms. Furthermore, through the designed cross-terminal joint simulation and testing integration method, the collaborative development of digital twin environment construction and algorithm design and testing is achieved, greatly shortening the development cycle of multi-source autonomous navigation technology. This is of great significance for achieving accurate and robust positioning in multi-source autonomous navigation. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the structure of the multi-source fusion navigation system integration device based on Carla / Matlab provided in Embodiment 1 of the present invention.

[0042] Figure 2 This is a flowchart of the multi-source fusion navigation system integration method based on Carla / Matlab provided in Embodiment 2 of the present invention.

[0043] Figure 3 This is a flowchart of the steps in the carrier model import step S1 according to the second embodiment of the present invention.

[0044] Figure 4 This is a flowchart of the steps of data processing step S4 according to the second embodiment of the present invention.

[0045] Figure 5 This is a flowchart of the steps in the system robustness test step S6 according to the second embodiment of the present invention. Detailed Implementation

[0046] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0047] This invention provides an open-architecture multi-source fusion navigation system integration system, including a signal generation terminal and an algorithm testing terminal.

[0048] There can be multiple signal generating terminals, and different digital twin platforms can be deployed on different signal generating terminals. The signal generating terminal is used to build the test virtual scene on the digital twin platform, including a carrier library module, a scene library module, and a sensor library module.

[0049] The carrier library module is used to obtain vehicle models through vehicle modeling and pedestrian models through pedestrian modeling.

[0050] The Scene Library module is used for building and rendering virtual scenes.

[0051] The sensor module is used for modeling and deploying various sensors used in multi-source fusion navigation systems;

[0052] The algorithm testing terminal includes a data processing module, a perturbation library module, an algorithm library module, and a visualization module.

[0053] The data processing module is used to receive raw data from various sensors from the signal generating terminal and process the raw sensor data to generate standard attitude, velocity, and position navigation information.

[0054] The perturbation library module is used to simulate various types of perturbations during the simulation process. It can form a perturbation library by measuring the noise of actual sensors and taking noise segments from them. When using it, it can be directly added to the original data. Alternatively, random perturbation signals such as Gaussian white noise or abrupt signals can be generated mathematically.

[0055] The algorithm library is used for developing and testing various multi-source fusion navigation algorithms. In this embodiment of the invention, the algorithms in the library include VINS, FAST-LIO, inertial navigation calculations, and multi-source fusion navigation algorithms.

[0056] The visualization module is used to display simulation scenes and real-time data.

[0057] This invention also provides an open-architecture multi-source fusion navigation system integration method. Using the aforementioned open-architecture multi-source fusion navigation system integration device, the following integration method is executed, specifically including a signal generation terminal integration method, an algorithm testing terminal integration method, and a cross-terminal joint simulation testing integration method:

[0058] The signal generating terminal integration method involves selecting a digital twin platform within the signal generating terminal to complete the construction of a virtual test scenario, the establishment of a carrier model, and the deployment of sensors, thereby constructing a digital twin environment that simulates the real world, serving as the signal generation source for testing a multi-source fusion navigation system. In this embodiment of the invention, the signal generating terminal integration method includes the following steps:

[0059] Step S1: Build the scenario required for the task, then load the corresponding world map, import the relevant environmental object models, and obtain the simulation scene. The imported environmental object models include building models, tree models, grass models, and obstacle models.

[0060] Step S2: Pre-build a carrier library. The carrier models in the carrier library include vehicle models obtained through vehicle modeling and pedestrian models obtained through pedestrian modeling. Import the carrier models.

[0061] The system checks if the required vehicle model exists in the carrier library. If it does, the system imports the relevant vehicle model. If it does not exist, the system models the vehicle according to the model file format supported by the carrier library module, sets its physical characteristics and dynamics model, obtains the relevant vehicle model, and then imports it.

[0062] Step S3: Based on the imported vehicle model, deploy simulated sensors. Place the sensors in appropriate locations on the vehicle. For example, the lidar should be placed at the bottom of the vehicle, and the vision camera is generally installed at the front of the vehicle.

[0063] Step S4: By setting control scripts, the vehicle model can perform actions according to the set tasks in the virtual environment. At the same time, the parameters of the sensor model can be adjusted according to actual needs to achieve unified deployment of the scene library, carrier library and sensor library.

[0064] The algorithm testing terminal integration method involves selecting mathematical processing software within the algorithm testing terminal to complete signal source data acquisition and processing, algorithm development and testing, disturbance injection, and result visualization. This enables the development, debugging, and optimization of various types of multi-source fusion navigation algorithms, ensuring their normal operation and achievement of expected results under different disturbance conditions. In this embodiment of the invention, the algorithm testing terminal integration method includes the following steps:

[0065] Step S1: The data processing module freely selects and receives measurement data from the target sensor, preprocesses the raw data, stores the processed usable information in the form of a data packet, and sends it to the algorithm library module for processing.

[0066] Step S2: After obtaining valid sensor data, call the existing algorithms in the algorithm library module to perform navigation calculations on the data, and test and verify the performance of the algorithms.

[0067] Step S3: Generate random disturbance signals in the disturbance library module and inject them into the sensor's measurement data to test whether the robustness and anti-interference capability of the multi-source fusion navigation system meet the requirements under different disturbance conditions.

[0068] Step S4: Observe the simulation scene and real-time data through the visualization library module, and observe the real-time dynamic curve of the solution result of the navigation algorithm used. Adjust the algorithm parameters through interface interaction.

[0069] The cross-terminal joint simulation test integration method packages the signal source data of the digital twin scene simulation process in the signal generating terminal according to a specified format, and then transmits it to the algorithm testing terminal in real time via wireless network communication or wired transmission. This enables the joint debugging of the designed multi-source fusion algorithm by the signal generating terminal and the algorithm testing terminal. In this embodiment of the invention, the cross-terminal joint simulation test integration method includes the following steps:

[0070] Step S1: Connect the signal generating terminal and the algorithm testing terminal for communication. The communication connection method is either wireless connection via the same local area network or wired connection via serial communication.

[0071] Step S2: Create communication nodes. In the multi-source fusion navigation integration system, each execution unit is a data communication node. The execution units in the carrier library module are carrier models, the execution units in the sensor module are sensors, and the execution units in the algorithm library module are various navigation algorithms. The communication nodes created in this way include carrier nodes, sensor nodes, and navigation algorithm nodes.

[0072] Step S3: The signal generating terminal and the algorithm testing terminal communicate through a publisher / subscriber pattern. Each node of the signal generating terminal publishes messages on a set topic, and each node of the algorithm testing terminal can subscribe to and receive these messages.

[0073] Step S4: Define message type. Each type of signal source data that needs to be transmitted is a message. Define the message format according to the type of data.

[0074] Step S5: Set up a communication network controller to track communication nodes and messages in the multi-source fusion navigation system. All communication nodes are connected to the communication network controller so that all communication nodes can find each other.

[0075] Step S6: Start the communication nodes of the signal generating terminal and the algorithm testing terminal. The communication node of the signal generating terminal packages and publishes the signal source data to be transmitted in an agreed format, while the communication node of the algorithm testing terminal receives the data in the agreed format, so as to realize the real-time transmission of signal source data between the two terminals.

[0076] Specific example 1:

[0077] This invention provides an integrated device for a multi-source fusion navigation system based on Carla / Matlab, such as... Figure 1 As shown, it includes the following modules:

[0078] Step S1: Carrier Library Module. The Carla autonomous driving simulation platform can call various physical models of autonomous vehicles, pedestrians, etc. stored in the carrier library, and provide information such as the geometry, physical characteristics, and dynamics of various carriers.

[0079] Step S2: Scene Library Module. The Carla autonomous driving simulation platform can load different scene maps stored in the scene library, mainly including common environments such as cities, rural areas, and grasslands. At the same time, based on the Unity Unreal Engine, advanced rendering functions such as image enhancement and ray tracing make the scenes more realistic and more interactive.

[0080] Step S3: Sensor Library Module. The Carla autonomous driving simulation platform can be configured with various common types of sensors from the sensor library, such as inertial measurement units, GNSS, RGB cameras, and LiDAR.

[0081] Step S4: Data Processing Module. The mathematical computing platform Matlab can receive raw data from various sensors in the simulation and process this data to generate useful information.

[0082] Step S5: Algorithm Library Module. The system contains various directly callable algorithms, such as VINS, FAST-LIO, and Kalman filtering.

[0083] Step S6: Disturbance Library Module. The mathematical computing platform Matlab can simulate various types of disturbances during the simulation process, such as noise, interference, and sudden events. It can also store noise extracted from actual sensor measurements.

[0084] Step S7: Visualization Library Module. The autonomous driving simulation platform Carla can display simulation scenes and real-time data, while the mathematical calculation platform Matlab can display navigation algorithm simulation test results. It also has human-computer interaction functions for adjusting simulation settings and adjusting algorithm parameters, etc.

[0085] Specific example two:

[0086] The present invention also provides an integration method for a multi-source fusion navigation system based on Carla / Matlab, such as... Figure 2 As shown, it includes the following steps:

[0087] Step S1: Import the carrier model. The user checks if the physical model of the required carrier exists in the Carla carrier library. If it does, the relevant model is imported. If not, 3D modeling can be performed using Blender, and then the FBX model file can be imported. The physical properties and motion laws can then be set in Carla.

[0088] In an exemplary implementation instance, such as Figure 3 As shown, step S1 may include the following sub-steps:

[0089] Step S11: Create a three-dimensional geometric model of the carrier in the 3D modeling software Blender to facilitate visualization in the simulation.

[0090] Step S12: Set various physical properties of the carrier in Carla, such as inertia, friction, and aerodynamic coefficients, to calculate the carrier's motion and response in the simulation.

[0091] Step S13: Set the motion laws of the vehicle in Carla, such as Newton's laws of motion and rigid body motion equations. These laws can be used to calculate the motion trajectory and state of the vehicle in the simulation.

[0092] Step S2: Load the scene map. The user loads the corresponding world map from the Carla scene library and imports relevant building, vegetation, and other models.

[0093] Step S3: Deploy large-scale sensors. In Carla, users install simulated sensors, such as inertial measurement units, GNSS, RGB cameras, and LiDAR, in appropriate locations based on their imported carrier model, and set suitable sensor parameters to complete the deployment of the multi-source fusion navigation hardware system.

[0094] Step S4: Data Communication and Processing. In Matlab, the user reads the target sensor data packet published by Carla via ROS node communication, preprocesses the raw data, and obtains usable information from the target sensor measurements.

[0095] In an exemplary implementation instance, such as Figure 4 As shown, step S4 may include the following sub-steps:

[0096] Step S41: ROS networking connection. Connect the Carla terminal and Matlab terminal to the same local area network and achieve ROS communication by subscribing to each other's IP addresses.

[0097] Step S42: Create ROS nodes. In the Carla / Matlab multi-source fusion navigation co-simulation system, each execution unit is a data ROS node, such as VINS node, IMU node, Matlab node, etc.

[0098] Step S43: Publish / Subscribe Communication Mode. The Carla terminal and the Matlab terminal communicate through a publisher / subscriber mode. Each sensor node of the Carla terminal publishes messages on specific topics, and the Matlab terminal nodes can subscribe to and receive these messages.

[0099] Step S44: Define the message type. Each type of signal source data that needs to be transmitted is a message. The message format needs to be defined according to the type of data, that is, what measurement information from the sensor needs to be included in the message, such as timestamps, attitude, velocity, position and other navigation information.

[0100] Step S45: Set up the ROS Master controller to track nodes and messages in the multi-source fusion navigation system. All nodes need to be connected to the ROS Master so that they can find each other.

[0101] Step S46: Data transmission and processing. Start each node of the Carla terminal and Matlab terminal. The Carla terminal node packages and publishes the sensor data to be transmitted in a specific format, while the Matlab terminal node receives the data in the agreed format, realizing real-time transmission of sensor data in the digital twin environment between the two terminals.

[0102] Step S5: Algorithm Simulation Test. Users call the processed sensor data in Matlab to develop new multi-source fusion navigation algorithms, test and verify the algorithm's performance, and continuously improve and optimize it. Through continuous iteration, the algorithm's functionality and performance are gradually improved to meet the needs of multi-source fusion autonomous navigation.

[0103] Step S6: System robustness test. The user generates a suitable random disturbance signal in Matlab and injects it into the sensor's measurement data to test whether the robustness and anti-interference capability of the multi-source fusion navigation system meet the requirements under different disturbance conditions.

[0104] In an exemplary implementation instance, such as Figure 5 As shown, step S6 may include the following sub-steps:

[0105] Step S61: Generate various types of random disturbance signals, such as Gaussian noise, impulse interference, and sinusoidal signals. Users can set the desired disturbance type and parameters, or use noise extracted through actual sensor measurements.

[0106] Step S62: Inject the generated disturbance signal into the sensor measurement data to simulate interference in the actual system. There are many injection methods, such as adding it to the input signal or directly applying it to the output signal.

[0107] Step S63: Evaluate the system's performance metrics after the disturbance is introduced, such as navigation error. These metrics can be used to determine whether the system's robustness and anti-interference capability meet the requirements.

[0108] Step S7: Visualization. Users observe the simulation scene and real-time data of the entire simulation system through the RVIZ visualization interface. They also develop a host computer interface for the multi-source fusion navigation simulation platform using Matlab. Through interface interaction, users can observe the real-time dynamic curves of the solution results of the navigation algorithm and adjust simulation settings and algorithm parameters to better control and optimize the navigation algorithm simulation process.

[0109] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An open-architecture multi-source fusion navigation system integration system, characterized in that, This includes signal generation terminals and algorithm testing terminals; The signal generating terminal is used to build a digital twin platform to complete the construction of a test virtual scene, including a carrier library module, a scene library module, and a sensor library module; The carrier library module is used to obtain vehicle models through vehicle modeling and pedestrian models through pedestrian modeling. The scene library module is used for the construction and rendering of virtual scenes; The sensor library module is used for modeling and deploying various sensors used in multi-source fusion navigation systems; The algorithm testing terminal includes a data processing module, a perturbation library module, an algorithm library module, and a visualization module; The data processing module is used to receive various raw sensor data from the signal generating terminal and process the raw sensor data to generate standard attitude, velocity, and position navigation information. The disturbance library module is used to simulate various types of disturbances during the simulation process; The algorithm library module is used to develop and test various multi-source fusion navigation algorithms; The visualization module is used to display simulation scenes and real-time data.

2. An open-architecture multi-source fusion navigation system integration method, characterized in that, The multi-source fusion navigation system integration system using the open architecture described in claim 1 executes the following integration method, specifically including a signal generation terminal integration method, an algorithm testing terminal integration method, and a cross-terminal joint simulation testing integration method: The signal generating terminal integration method involves selecting a digital twin platform in the signal generating terminal to complete the construction of the test virtual scene, the establishment of the carrier model, and the deployment of sensors, thereby constructing a digital twin environment that simulates the real world, serving as the signal generation source for testing the multi-source fusion navigation system. The algorithm testing terminal integration method selects mathematical processing software in the algorithm testing terminal to complete signal source data acquisition and processing, algorithm development and testing, disturbance injection and result visualization, so as to realize the development, debugging and optimization of various types of multi-source fusion navigation algorithms and ensure that they operate normally and achieve the expected results under different disturbance conditions. The cross-terminal joint simulation test integration method packages the signal source data of the digital twin scene simulation process in the signal generating terminal according to the format, and then transmits it to the algorithm test terminal in real time through wireless network communication or wired transmission, thereby realizing the joint debugging of the designed multi-source fusion algorithm by the signal generating terminal and the algorithm test terminal.

3. The method for integrating a multi-source fusion navigation system with an open architecture as described in claim 2, characterized in that, The signal generating terminal integration method includes the following steps: Step S1: Build the scenario required by the task, then load the corresponding world map, import the relevant environmental object models, and obtain the simulation scenario; Step S2: Pre-build a carrier library. The carrier models in the carrier library include vehicle models obtained through vehicle modeling and pedestrian models obtained through pedestrian modeling. Import the carrier models. The system checks if the required vehicle model exists in the carrier library. If it does, it imports the relevant vehicle model. If it does not, it models the vehicle according to the model file format supported by the carrier library module, sets its physical properties and dynamics model, obtains the relevant vehicle model, and imports it. Step S3: Deploy simulated sensors based on the imported vehicle model; Step S4: By setting control scripts, the vehicle model can perform actions according to the set tasks in the virtual environment. At the same time, the parameters of the sensor model can be adjusted according to actual needs to achieve unified deployment of the scene library, carrier library and sensor library.

4. The open-architecture multi-source fusion navigation system integration method as described in claim 3, characterized in that, The algorithm testing terminal integration method includes the following steps: Step S1: The data processing module freely selects and receives measurement data from the target sensor, preprocesses the measurement data, stores the processed usable information in the form of a data packet, and transmits it to the algorithm library module for processing. Step S2: Call the existing algorithms in the algorithm library module to perform navigation calculations on the data packets, and test and verify the performance of the algorithms; Step S3: Generate random disturbance signals in the disturbance library module and inject them into the sensor measurement data to test whether the robustness and anti-interference capability of the multi-source fusion navigation system meet the requirements under different disturbance conditions. Step S4: Observe the simulation scene and real-time data through the visualization library module, and observe the real-time dynamic curve of the solution result of the navigation algorithm used. Adjust the algorithm parameters through interface interaction.

5. The method for integrating a multi-source fusion navigation system with an open architecture as described in claim 4, characterized in that, The cross-terminal joint simulation test integration method includes the following steps: Step S1: Connect the signal generating terminal and the algorithm testing terminal for communication; the communication connection method is either wireless connection via the same local area network or wired connection via serial communication. Step S2: Create a communication node. In the multi-source fusion navigation integration system, each execution unit is a data communication node. The execution unit in the carrier library module is the carrier model, the execution unit in the sensor library module is the sensor, and the execution unit in the algorithm library module is each navigation algorithm. The communication nodes created in this way include carrier nodes, sensor nodes, and navigation algorithm nodes. Step S3: The signal generating terminal and the algorithm testing terminal communicate through a publisher / subscriber pattern. Each node of the signal generating terminal publishes messages with a set topic, and each node of the algorithm testing terminal can subscribe to and receive these messages. Step S4: Define message type. Each type of signal source data that needs to be transmitted is a message. Define the message format according to the type of data. Step S5: Set up a communication network controller to track communication nodes and messages in the multi-source fusion navigation system. All communication nodes are connected to the communication network controller so that all communication nodes can find each other. Step S6: Start the communication nodes of the signal generating terminal and the algorithm testing terminal. The communication node of the signal generating terminal packages and publishes the signal source data to be transmitted in an agreed format, while the communication node of the algorithm testing terminal receives the data in the agreed format, so as to realize the real-time transmission of signal source data between the two terminals.

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