Enhanced synthetic scene generation method based on multi-source information fusion
Through multi-source information fusion technology based on space-time registration, the problems of insufficient types and quantity of information sources and insufficient registration in the enhanced synthetic vision method are solved, and the enhanced synthetic vision generation with high reliability, security and real-time performance are achieved, which is suitable for airborne fields such as navigation, landing and search and rescue.
Patent Information
- Application Number
- CN202411810149.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-10
- Publication Date
- 2025-04-18
AI Technical Summary
The current enhanced synthetic vision method has insufficient types and quantity of information sources, and has failed to effectively register time and space, resulting in large fusion errors, long algorithm processing delays, low reliability, poor safety, and inability to adapt to various sensors, and cannot be widely used in airborne fields such as navigation, landing, search and rescue.
Multi-source information fusion technology based on space-time registration is adopted to obtain multi-source sensor information and perform category detection and data preprocessing, perform time and space registration, object detection and identification, enhancement synthetic visual images are generated through coordinate mapping and information fusion algorithms, and video timing reconstruction is carried out to adapt to downstream display devices.
It improves the reliability, robustness, safety and real-timeness of the system, enhances the credibility and accuracy of data, expands the time and space coverage of the system, can adapt to various sensors, improves the situational awareness of the surrounding environment of the aircraft, and thus improves the safety of flight.
Smart Images
Figure CN119850830A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of embedded computer video processing, and in particular to an enhanced synthetic vision generation method based on multi-source information fusion. Background Art
[0002] Limited visibility has always been one of the main threats to aviation safety. In the field of commercial aviation, 30% of fatal accidents are controlled flight into terrain. The reason for this accident is that the external visibility of the aircraft is limited, causing the aircraft to crash into the ground or obstacles while its functions are intact. In traditional cockpit design, in order to allow pilots to obtain more information, the number of instruments is often increased. When performing flight missions, pilots must not only observe the real scene outside the cabin, but also pay attention to the aircraft parameters provided by various instruments, which to a certain extent increases their burden. In complex flight conditions such as aircraft approach and target tracking, this negative impact is more obvious and may even lead to flight accidents.
[0003] The synthetic vision method is an emerging technology born to solve this problem. It is based on terrain databases, vector databases and satellite orthophoto databases, combined with the position, heading, speed, attitude and other information provided by the aircraft, to generate a virtual vision of the external environment in real time, and superimpose the flight instrument information to provide the pilot with a description of the external environment and flight parameters. Compared with other cockpit technologies, synthetic vision technology displays images in two-dimensional / three-dimensional form with high accuracy. It is mainly used to improve the pilot's perception of the external environment to reduce the occurrence of major accidents such as controlled flight collisions and runway incursions. At the same time, due to its rich display capabilities, it can reduce the pilot's workload, ultimately ensuring the maximum safety of the aircraft and the safety of life and property.
[0004] Compared with other application scenarios of virtual scene visualization systems, the embedded synthetic vision method has its own characteristics. First, the flight route covers a wide range of scenes, the terrain elevation data and satellite image data are large in scale, and the viewing distance and resolution of different objects in the scene vary greatly; at the same time, the embedded computing environment has strict requirements on the power consumption and volume of hardware, so its available graphics computing resources are relatively small and its computing power is relatively weak; in addition, the embedded synthetic vision method has high real-time requirements, and the drawn terrain scene must be able to reflect the navigation information of the driver's current position in real time, so the graphics engine that realizes vision synthesis and vision enhancement must have a faster drawing and response speed; finally, the synthetic vision method needs to provide drivers with sufficiently accurate information to help them make correct decisions.
[0005] Enhanced vision is a method that integrates information from multiple sensors, such as forward-looking infrared, radar altimeter, weather radar, millimeter-wave radar, and lidar, to provide pilots with a more accurate and reliable real-time view of the aircraft's exterior. Enhanced synthetic vision fuses two or more images or views with complementary features into a new picture. Aiming at the need for visual-assisted navigation of aircraft in harsh visual environments, it is based on a high-performance 2D and 3D graphics rendering engine, uses internal high-resolution geographic information data for vision synthesis, and uses multi-source sensor information for vision enhancement, thereby improving the visual accuracy, reliability, and safety of virtual scenes and enhancing the realism of scenes.
[0006] The enhanced synthetic vision method combines the advantages of synthetic vision and enhanced vision, and generates terrain, obstacles and related environmental features (including buildings, runways, etc.) in the surrounding field of view for the pilot through an effective information fusion method, providing accurate visualization means to avoid low-altitude surface threats, early detection and avoidance of possible dangers, and improving flight training capabilities in complex terrain and harsh environments. Enhanced synthetic vision has higher credibility, better clarity and recognizability. It can greatly improve the pilot's situational awareness of the environment around the aircraft, thereby further improving flight safety.
[0007] The airborne application of enhanced synthetic vision focuses on improving accuracy and real-time performance, thereby ensuring reliability and safety. Traditional, single sensor sources are difficult to meet actual development needs, and enhanced synthetic vision is developing in the direction of multi-source information fusion. The enhanced synthetic vision method based on multi-source information fusion can adapt to multi-source heterogeneous sensor information and perform multi-level and multi-faceted real-time processing and fusion on them, which can improve the accuracy, reliability and safety of enhanced synthetic vision, while effectively improving the system's temporal and spatial coverage. Summary of the invention
[0008] In view of this, an embodiment of the present application provides an enhanced synthetic vision generation method based on multi-source information fusion, which solves the problems of the current enhanced synthetic vision method, such as insufficient types and quantities of information sources fused, failure to perform effective temporal and spatial registration, large fusion error, long algorithm processing delay time, low reliability, poor security, inability to adapt to various sensors, and inability to be widely used in airborne fields such as navigation, landing, and search and rescue.
[0009] The embodiment of the present application provides an enhanced synthetic view generation method based on multi-source information fusion, including:
[0010] Acquire multi-source sensor information and perform category detection to distinguish image information from discrete target information;
[0011] Performing data preprocessing on the image information and the discrete target information using different processing branches respectively;
[0012] Registering the processed image information and discrete target information;
[0013] Perform target detection and recognition on the registered image information to extract the required target information;
[0014] The required target information is mapped by coordinates to achieve conversion from screen coordinates to geographic coordinates;
[0015] Based on the information fusion algorithm, the target information after coordinate transformation is fused with the discrete target information after registration to obtain fused data;
[0016] Generate a visual enhancement picture according to the fusion data, and generate a visual synthesis picture according to the aircraft status data information, the obstacle database, the high-resolution three-dimensional terrain database, the vector database and the satellite orthophoto database;
[0017] Performing visual fusion according to the visual enhancement picture and the visual synthesis picture to obtain an enhanced synthetic visual picture;
[0018] The characteristic parameters of the downstream display device are detected, and the video timing of the enhanced synthetic vision picture is reconstructed in a targeted manner to generate image information matching the downstream display device for visual display.
[0019] According to a specific implementation of an embodiment of the present application, the data preprocessing includes filtering, format unification, image enhancement and image restoration.
[0020] According to a specific implementation manner of an embodiment of the present application, the registering the processed image information and the discrete target information includes: performing temporal registration and spatial registration on the processed image information and the discrete target information.
[0021] According to a specific implementation of the embodiment of the present application, generating a visual synthesis picture based on the carrier state data information, the high-resolution three-dimensional terrain database, the vector database and the satellite orthophoto database includes:
[0022] Predict the flight trajectory based on the aircraft status data information;
[0023] Based on the flight trajectory, pre-fetching data from the high-resolution three-dimensional terrain database, the vector database, and the satellite orthophoto database;
[0024] The flight route and surrounding environment are visualized and drawn according to the pre-fetched data in combination with the obstacle database through a digital map engine to generate the visual synthesis picture.
[0025] According to a specific implementation of an embodiment of the present application, the carrier aircraft status data information includes the carrier aircraft's attitude, altitude, speed and position information.
[0026] According to a specific implementation of the embodiment of the present application, performing visual fusion according to the visual enhancement picture and the visual synthesis picture includes:
[0027] According to the vision enhancement picture and the vision synthesis picture, vision fusion is performed based on a pixel-level image fusion algorithm, a feature-level fusion algorithm and a decision-level image fusion algorithm.
[0028] According to a specific implementation of the embodiment of the present application, the method further includes:
[0029] According to the enhanced vision picture, risk assessment and marking are performed on the target information to obtain a first mark;
[0030] According to the visual synthesis picture, risk judgment and marking are performed to obtain a second marking;
[0031] Based on the first mark and the second mark, and in combination with the flight situation of the carrier aircraft, risk judgment and marking are performed through a decision-level image fusion algorithm to obtain a final risk mark, and an alarm is displayed according to the final risk mark.
[0032] According to a specific implementation of the embodiment of the present application, the risk judgment and marking are performed based on the first mark and the second mark and in combination with the flight status of the carrier aircraft through a decision-level image fusion algorithm to obtain a final risk mark, including:
[0033] In combination with the flight situation of the carrier aircraft, the information of the first marker and the information of the second marker are compared in position through a decision-level image fusion algorithm. If the positions are different, both the first marker and the second marker are used as the final risk markers. If the positions are the same, a similarity judgment is performed. When the similarity is higher than a preset range value, the marker with a higher flight safety factor between the first marker and the second marker is selected as the final risk marker.
[0034] According to a specific implementation of the embodiment of the present application, the target detection and recognition on the registered image information includes:
[0035] The registered image information is used for target detection and recognition based on neural network and other algorithms.
[0036] Beneficial effects:
[0037] The enhanced synthetic vision generation method based on multi-source information fusion in the embodiment of the present application can solve the problems of insufficient types and quantity of information sources fused by the current enhanced synthetic vision method, failure to perform effective registration in time and space, large fusion error, long algorithm processing delay, low reliability, poor security, inability to adapt to various sensors, and inability to be widely used in airborne fields such as navigation, landing, search and rescue. The multi-source information fusion technology based on time-space registration in the present application can automatically adapt to various sensors, enhance the credibility and accuracy of data, expand the time and space coverage of the system, and improve the reliability, robustness, security and real-time performance of the system.
[0038] This application brings together the advantages of synthetic vision and enhanced vision, and generates terrain, obstacles and related environmental features (including buildings, runways, etc.) in the surrounding field of view for the pilot through an effective multi-source information fusion method, providing accurate visualization means to avoid low-altitude surface threats, early detection and avoidance of possible dangers, and improving flight training capabilities in complex terrain and harsh environments. This application has higher credibility, better clarity and recognizability. It can greatly improve the pilot's situational awareness of the environment around the aircraft, thereby further improving flight safety. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0040] Figure 1 Schematic diagram of an enhanced synthetic view generation method based on multi-source information fusion according to an embodiment of the present invention. DETAILED DESCRIPTION
[0041] The embodiments of the present application are described in detail below with reference to the accompanying drawings.
[0042] The following describes the implementation methods of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.
[0043] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present application, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.
[0044] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The illustrations only show the components related to the present application rather than being drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.
[0045] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.
[0046] The embodiment of the present application provides a method for generating an enhanced synthetic vision based on multi-source information fusion. Taking a certain aircraft auxiliary navigation system as an example, the system is equipped with forward-looking infrared, radar altimeter, millimeter-wave radar, lidar and other sensors. The system is equipped with a large-capacity storage device, which stores geographic information such as high-resolution terrain database, vector database, satellite orthophoto database, etc. The system realizes the real-time fusion of multi-source heterogeneous information through the method provided by the present invention, generates an adaptive and highly secure enhanced synthetic vision picture, and finally outputs it to the display for display. Figure 1 Describe in detail.
[0047] In one embodiment, an enhanced synthetic vision generation method based on multi-source information fusion includes:
[0048] Acquire multi-source sensor information and perform category detection to distinguish image information from discrete target information;
[0049] Performing data preprocessing on the image information and the discrete target information using different processing branches respectively;
[0050] Registering the processed image information and discrete target information;
[0051] Perform target detection and recognition on the registered image information to extract the required target information;
[0052] The required target information is mapped by coordinates to achieve conversion from screen coordinates to geographic coordinates;
[0053] Based on the information fusion algorithm, the target information after coordinate transformation is fused with the discrete target information after registration to obtain fused data;
[0054] Generate a visual enhancement picture according to the fusion data, and generate a visual synthesis picture according to the aircraft status data information, the obstacle database, the high-resolution three-dimensional terrain database, the vector database and the satellite orthophoto database;
[0055] Performing visual fusion according to the visual enhancement picture and the visual synthesis picture to obtain an enhanced synthetic visual picture;
[0056] The characteristic parameters of the downstream display device are detected, and the video timing of the enhanced synthetic vision picture is reconstructed in a targeted manner to generate image information matching the downstream display device for visual display.
[0057] In the embodiment of the present application, the image information, data information and obstacle data of multi-source sensors can be fused and an enhanced visual picture can be generated through information category detection, data preprocessing, registration, target detection and recognition, coordinate mapping, information fusion, vision enhancement and other operations; synthetic visual pictures are generated by calling terrain databases, vector databases and satellite orthophoto databases; finally, enhanced synthetic visual pictures are generated through vision fusion, time series reconstruction and other operations. The enhanced synthetic vision method based on a single sensor source has low accuracy, poor robustness and reliability. The enhanced synthetic vision based on multi-source information fusion can comprehensively process the data information of different information sources in time and space, and derive an estimate or description of the consistency of the measured object or target, which has higher reliability and less ambiguity than a single information source. However, due to factors such as different installation locations and different performance parameters of multi-source sensors, the resolution, frame rate, center point position and other spatiotemporal parameters of the acquired image information and data information are also different. The method in this embodiment can solve the problems of insufficient types and quantity of information sources fused by the current enhanced synthetic vision method, failure to perform effective registration in time and space, large fusion error, long algorithm processing delay, low reliability, poor security, inability to adapt to various sensors, and inability to be widely used in airborne fields such as navigation, landing, search and rescue. The multi-source information fusion technology based on time-space registration of the present invention can automatically adapt to various sensors, enhance the credibility and accuracy of data, expand the time and space coverage of the system, and improve the reliability, robustness, security and real-time performance of the system.
[0058] In one embodiment, the data preprocessing includes filtering, format unification, image enhancement and image restoration.
[0059] In specific implementation, the input multi-source sensor information is detected by category (such as radar, infrared, and visible light), image information (such as infrared and visible light) and discrete target information (such as radar data) are distinguished, and different processing branches are selected for subsequent processing. The processing branches of image information and discrete target information are executed in parallel, and the image information and discrete target information are preprocessed separately, such as filtering, format unification, image enhancement, and image restoration. The purpose of preprocessing is to unify the information format, improve the information quality, and shorten the subsequent processing time.
[0060] When performing category detection, a sensor information database is established based on the information format characteristics of image sensors and data sensors widely used in the market. The more sample formats in the database, the stronger the information category detection capability in this embodiment.
[0061] In one embodiment, registering the processed image information and the discrete target information includes: performing temporal registration and spatial registration on the processed image information and the discrete target information.
[0062] In this embodiment, time registration is to unify the time domain, which means that different performance parameters of different sensors result in information not being aligned in time, and they need to be unified on the same time axis to achieve time adaptation of multi-source sensors; space registration is to unify the space domain, which means that different performance parameters of different sensors result in information not being aligned in space, and it is necessary to unify the parameters such as angle, field of view, resolution, center point, etc. through processing algorithms to achieve space adaptation of multi-source sensors. Unified time domain and space domain means that target detection and recognition are performed on image information obtained by different sensors in the same space at the same time. Different target models can be trained for different application scenarios for on-demand detection. Multi-source information fusion technology based on time-space registration can automatically adapt to various types of sensors.
[0063] In one embodiment, the generating of the visual synthesis picture according to the carrier state data information, the high-resolution three-dimensional terrain database, the vector database and the satellite orthophoto database includes:
[0064] Predict the flight trajectory based on the aircraft status data information;
[0065] Based on the flight trajectory, pre-fetching data from the high-resolution three-dimensional terrain database, the vector database, and the satellite orthophoto database;
[0066] The flight route and surrounding environment are visualized and drawn according to the pre-fetched data in combination with the obstacle database through a digital map engine to generate the visual synthesis picture.
[0067] In one embodiment, the carrier aircraft status data information includes the carrier aircraft's attitude, altitude, speed and position information.
[0068] In specific implementation, the flight trajectory is predicted based on the aircraft's attitude, altitude, speed and position information, and data is pre-fetched from the high-resolution 3D terrain database, vector database, and satellite orthophoto database. The flight route and surrounding environment are visualized through the digital map engine in combination with the obstacle database to generate a visual synthesis picture. Parameters such as the frame rate and resolution of the visual synthesis picture can be configured according to the specific usage scenario.
[0069] In one embodiment, performing visual fusion according to the visual enhancement picture and the visual synthesis picture includes:
[0070] According to the vision enhancement picture and the vision synthesis picture, vision fusion is performed based on a pixel-level image fusion algorithm, a feature-level fusion algorithm and a decision-level image fusion algorithm.
[0071] During specific implementation, the pixel-level image fusion algorithm is used to retain the original information to the greatest extent as the decision-making basis of the pilot; the feature-level fusion algorithm is used to fuse the typical feature information of the vision enhancement picture and the vision synthesis picture to reduce the decision-making burden of the pilot; the decision-level image fusion algorithm is used to assist the pilot to the greatest extent; the pixel-level image fusion algorithm, the feature-level fusion algorithm and the decision-level image fusion algorithm are selected by the pilot as needed, and the function of the fusion algorithm can be switched by using the display buttons.
[0072] In specific implementation, a graphics rendering engine is used to render the fused data into a visual enhancement screen, and the rendering parameters such as frame rate and resolution can be configured according to the specific usage scenario. The core content of the visual synthesis screen is geographic information, including but not limited to terrain information, vector information, satellite orthophoto information, etc. The rendering parameters such as frame rate and resolution can be configured according to the specific usage scenario.
[0073] In one embodiment, the method further comprises:
[0074] According to the enhanced vision picture, risk assessment and marking are performed on the target information to obtain a first mark;
[0075] According to the visual synthesis picture, risk judgment and marking are performed to obtain a second marking;
[0076] Based on the first mark and the second mark, and in combination with the flight situation of the carrier aircraft, risk judgment and marking are performed through a decision-level image fusion algorithm to obtain a final risk mark, and an alarm is displayed according to the final risk mark.
[0077] In one embodiment, the risk judgment and marking are performed based on the first mark and the second mark and in combination with the flight status of the carrier aircraft through a decision-level image fusion algorithm to obtain a final risk mark, including:
[0078] In combination with the flight situation of the carrier aircraft, the information of the first marker and the information of the second marker are compared in position through a decision-level image fusion algorithm. If the positions are different, both the first marker and the second marker are used as the final risk markers. If the positions are the same, a similarity judgment is performed. When the similarity is higher than a preset range value, the marker with a higher flight safety factor between the first marker and the second marker is selected as the final risk marker.
[0079] In one embodiment, the performing target detection and recognition on the registered image information includes:
[0080] The registered image information is used for target detection and recognition based on neural network and other algorithms.
[0081] In the above embodiment, the coordinate mapping is the screen coordinates of the target obtained through target detection and recognition, which need to be uniformly converted to geographic coordinates to facilitate subsequent multi-source information fusion operations. The information fusion algorithm needs to consider high security. When the comprehensive multi-source sensor information still cannot determine the exact location of the target, the danger zone can be circled by using envelope lines and other methods to avoid risks as much as possible. The timing reconstruction is to organize the timing of the output video according to the parameter information of the downstream display (such as video interface, resolution, frame rate, etc.), and finally realize the visualization display of adaptive high-security, strong real-time enhanced synthetic vision based on multi-source information fusion.
[0082] The embodiments provided by the present invention can fuse multi-source sensor image information, data information and obstacle data and generate enhanced visual images through information category detection, image preprocessing, data preprocessing, time alignment, space alignment, target detection and recognition, coordinate mapping, information fusion, vision enhancement and other operations; generate synthetic visual images by calling terrain database, vector database and satellite orthophoto database; finally generate enhanced synthetic visual images through vision fusion, time series reconstruction and other operations. Finally, the adaptive, highly secure and real-time enhanced synthetic vision based on multi-source information fusion is output and displayed through time series reconstruction logic.
[0083] The present invention can solve the problems of insufficient types and quantity of information sources fused by the current enhanced synthetic vision method, failure to effectively align time and space, large fusion error, long algorithm processing delay, low reliability, poor security, inability to adapt to various sensors, and inability to be widely used in airborne fields such as navigation, landing, search and rescue. The present invention is based on multi-source information fusion technology based on time-space alignment, which can automatically adapt to various sensors, enhance the credibility and accuracy of data, expand the time and space coverage of the system, and improve the reliability, robustness, security and real-time performance of the system.
[0084] The present invention combines the advantages of synthetic vision and enhanced vision, and generates terrain, obstacles and related environmental features (including buildings, runways, etc.) in the surrounding field of view for the pilot through an effective multi-source information fusion method, providing accurate visualization means for avoiding low-altitude surface threats, early detection and avoidance of possible dangers, and improving flight training capabilities in complex terrain and harsh environments. The present invention has higher credibility, better clarity and recognizability. It can greatly improve the pilot's situational awareness of the aircraft's surroundings, thereby further improving flight safety.
[0085] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.
Claims
1. An enhanced synthetic view generation method based on multi-source information fusion, characterized in that: include: Acquire multi-source sensor information and perform category detection to distinguish image information from discrete target information; Performing data preprocessing on the image information and the discrete target information using different processing branches respectively; Registering the processed image information and discrete target information; Perform target detection and recognition on the registered image information to extract the required target information; The required target information is mapped by coordinates to achieve conversion from screen coordinates to geographic coordinates; Based on the information fusion algorithm, the target information after coordinate transformation is fused with the discrete target information after registration to obtain fused data; Generate a visual enhancement picture according to the fusion data, and generate a visual synthesis picture according to the aircraft status data information, the obstacle database, the high-resolution three-dimensional terrain database, the vector database and the satellite orthophoto database; Performing visual fusion according to the visual enhancement picture and the visual synthesis picture to obtain an enhanced synthetic visual picture; The characteristic parameters of the downstream display device are detected, and the video timing of the enhanced synthetic vision picture is reconstructed in a targeted manner to generate image information matching the downstream display device for visual display.
2. The enhanced synthetic vision generation method based on multi-source information fusion according to claim 1, characterized in that: The data preprocessing includes filtering, format unification, image enhancement and image restoration.
3. The enhanced synthetic vision generation method based on multi-source information fusion according to claim 1, characterized in that: The registering the processed image information and the discrete target information includes: performing temporal registration and spatial registration on the processed image information and the discrete target information.
4. The enhanced synthetic vision generation method based on multi-source information fusion according to claim 1, characterized in that: The method of generating a visual synthesis image based on the aircraft status data information, the high-resolution three-dimensional terrain database, the vector database and the satellite orthophoto database includes: Predict the flight trajectory based on the aircraft status data information; Based on the flight trajectory, pre-fetching data from the high-resolution three-dimensional terrain database, the vector database, and the satellite orthophoto database; The flight route and surrounding environment are visualized and drawn according to the pre-fetched data in combination with the obstacle database through a digital map engine to generate the visual synthesis picture.
5. The enhanced synthetic vision generation method based on multi-source information fusion according to claim 4, characterized in that: The carrier aircraft status data information includes the carrier aircraft's attitude, altitude, speed and position information.
6. The enhanced synthetic vision generation method based on multi-source information fusion according to claim 1, characterized in that: The performing of visual fusion according to the visual enhancement picture and the visual synthesis picture comprises: According to the vision enhancement picture and the vision synthesis picture, vision fusion is performed based on a pixel-level image fusion algorithm, a feature-level fusion algorithm and a decision-level image fusion algorithm.
7. The enhanced synthetic vision generation method based on multi-source information fusion according to claim 6, characterized in that: The method further comprises: According to the enhanced vision picture, risk assessment and marking are performed on the target information to obtain a first mark; According to the visual synthesis picture, risk judgment and marking are performed to obtain a second marking; Based on the first mark and the second mark, and in combination with the flight situation of the carrier aircraft, risk judgment and marking are performed through a decision-level image fusion algorithm to obtain a final risk mark, and an alarm is displayed according to the final risk mark.
8. The enhanced synthetic vision generation method based on multi-source information fusion according to claim 7, characterized in that: The step of performing risk judgment and marking based on the first mark and the second mark and in combination with the flight status of the carrier aircraft through a decision-level image fusion algorithm to obtain a final risk mark includes: In combination with the flight situation of the carrier aircraft, the information of the first marker and the information of the second marker are compared in position through a decision-level image fusion algorithm. If the positions are different, both the first marker and the second marker are used as the final risk markers. If the positions are the same, a similarity judgment is performed. When the similarity is higher than a preset range value, the marker with a higher flight safety factor between the first marker and the second marker is selected as the final risk marker.
9. The enhanced synthetic vision generation method based on multi-source information fusion according to claim 1, characterized in that: The performing target detection and recognition on the registered image information includes: The registered image information is used for target detection and recognition based on neural network and other algorithms.