Air target formation identification method and device

Abstract

The application relates to an air target formation identification method and device, wherein the method comprises the following steps: a multi-modal air target detection hardware system is built, the system comprises multiple types of visual and dynamic visual sensors; multi-modal input data is preprocessed, an air target is identified through a target detection algorithm; an air target formation knowledge base is constructed, the dynamic correlation between air targets is modeled through a hypergraph, and the possible formation of a target group is compared and identified; a target formation dynamic perception model is established, the air target formation knowledge base is updated in real time according to the change of the formation, and the position, speed and other information of the target group are monitored in real time, and the ground target situation perception real-time analysis is used. Therefore, the problems that the prior art cannot efficiently fuse multi-source data, cannot realize high-precision formation identification in a complex dynamic environment, and cannot adapt to the changing flight mode demand in a tactical environment are solved.

Description

Technical Field

[0001] This application relates to the field of aerial target recognition technology, and in particular to an aerial target formation recognition method and apparatus. Background Technology

[0002] Traditional aerial target identification systems primarily rely on single-type sensors, such as radar or optical cameras, to detect and track aerial targets. However, these methods often struggle to guarantee accuracy and real-time performance in complex flight environments, dense formations of multiple targets, rapid dynamic changes, and obstruction. Furthermore, the limited data processing capabilities of single sensors make it difficult to comprehensively capture the multidimensional features of aerial targets, resulting in insufficient robustness of the identification results.

[0003] In recent years, with the development of multi-sensor fusion technology and artificial intelligence algorithms, aerial target formation recognition technology has made some progress. Multimodal sensor systems can simultaneously acquire multiple types of data, such as radar, infrared, and optical data, and improve target detection performance through data fusion. However, how to efficiently fuse multi-source data, extract meaningful features, and achieve high-precision formation recognition in complex and dynamic environments remains a pressing problem. Furthermore, most existing formation recognition methods rely on predefined templates or rules, limiting their ability to recognize new or unconventional formations. They struggle to adapt to the changing flight patterns required in tactical environments, and the ability to update in real-time and adapt to dynamic changes is also lacking in existing systems, significantly impacting the timeliness and accuracy of command and decision-making in rapidly changing battlefield environments.

[0004] In summary, existing technologies struggle to efficiently integrate multi-source data, cannot achieve high-precision formation identification in complex dynamic environments, and are ill-suited to the changing flight modes required in tactical environments, thus requiring urgent solutions. Summary of the Invention

[0005] This application provides a method and apparatus for identifying aerial target formations, in order to solve the problems of existing technologies, such as difficulty in efficiently fusing multi-source data, inability to achieve high-precision formation identification in complex dynamic environments, and difficulty in adapting to the changing flight mode requirements in tactical environments.

[0006] The first aspect of this application provides a method for identifying aerial target formations, comprising the following steps: collecting multimodal data corresponding to a target group, wherein the multimodal data includes visible light image data, infrared data, and radar data; preprocessing the multimodal data to obtain corresponding standard data, and identifying each aerial target in the target group based on the standard data and a pre-built target detection algorithm to obtain dynamic identification data corresponding to each aerial target; constructing a current hypergraph structure corresponding to the target group based on the dynamic identification data and a preset hypergraph modeling strategy, and using the current hypergraph structure to perform a comparison and identification operation on the current formation corresponding to the target group in a pre-built aerial target formation knowledge base to determine the current formation type corresponding to the target group.

[0007] Optionally, in one embodiment of this application, after determining the current formation type corresponding to the target group, the method further includes: monitoring the changing trend information corresponding to the target group based on the current formation type and a pre-built target formation dynamic perception model, so as to update the air target formation knowledge base according to the changing trend information; monitoring multiple dynamic parameters corresponding to the target group in real time, and determining multiple key performance indicators based on the multiple dynamic parameters, and determining whether there are any abnormal air targets in the target group that meet preset abnormal conditions based on the multiple key performance indicators and a preset anomaly detection algorithm, wherein when there are abnormal air targets in the target group that meet the preset abnormal conditions, corresponding early warning and response measures are executed; generating corresponding real-time analysis reports and visualization data based on the multiple dynamic parameters, and sending the real-time analysis reports and visualization data to a preset ground control center.

[0008] Optionally, in one embodiment of this application, the acquisition of multimodal data corresponding to the target group, wherein the multimodal data includes visible light image data, infrared data, and radar data, includes: constructing a multimodal aerial target detection hardware system based on preset multi-type visual sensors and dynamic visual sensors; and synchronously acquiring the visible light image data, the infrared data, and the radar data through the multimodal aerial target detection hardware system.

[0009] Optionally, in one embodiment of this application, the step of preprocessing the multimodal data to obtain corresponding standard data, and identifying each aerial target in the target group based on the standard data and a pre-built target detection algorithm to obtain dynamic identification data corresponding to each aerial target, includes: denoising the multimodal data, and performing data format conversion and time synchronization operations on the denoised multimodal data to obtain corresponding standard data; extracting features from the standard data based on the target detection algorithm, and determining the dynamic identification data corresponding to each aerial target according to a preset multi-task learning strategy and the features, wherein the dynamic identification data includes the position, velocity, and direction corresponding to each aerial target.

[0010] Optionally, in one embodiment of this application, the step of constructing a current hypergraph structure corresponding to the target group based on the dynamic recognition data and a preset hypergraph modeling strategy, and using the current hypergraph structure to compare and identify the current formation corresponding to the target group in a pre-built aerial target formation knowledge base to determine the current formation type corresponding to the target group, includes: taking each aerial target as a hypergraph node, obtaining the correlation index between each aerial target, and determining the corresponding hyperedge based on the correlation index, so as to construct a corresponding current hypergraph structure based on the hypergraph node and the hyperedge; comparing the similarity of the current hypergraph structure with each formation template in a preset plurality of formation templates to obtain the similarity corresponding to each formation template; determining the target formation template with the highest similarity among the plurality of formation templates, and training a pre-built hypergraph neural network model based on a preset formation sample training dataset, and inputting the target formation template into the trained hypergraph neural network model to output the current formation type corresponding to the target group.

[0011] A second aspect of this application provides an aerial target formation identification device, comprising: a data acquisition module for acquiring multimodal data corresponding to a target group, wherein the multimodal data includes visible light image data, infrared data, and radar data; a preprocessing module for preprocessing the multimodal data to obtain corresponding standard data, and identifying each aerial target in the target group based on the standard data and a pre-built target detection algorithm to obtain dynamic identification data corresponding to each aerial target; and a formation identification module for constructing a current hypergraph structure corresponding to the target group based on the dynamic identification data and a preset hypergraph modeling strategy, and using the current hypergraph structure to perform a comparison and identification operation on the current formation corresponding to the target group in a pre-built aerial target formation knowledge base to determine the current formation type corresponding to the target group.

[0012] Optionally, in one embodiment of this application, it further includes: a monitoring module, configured to monitor the changing trend information corresponding to the target group based on the current formation type and a pre-built target formation dynamic perception model after determining the current formation type, so as to update the air target formation knowledge base according to the changing trend information; a judgment module, configured to monitor multiple dynamic parameters corresponding to the target group in real time, determine multiple key performance indicators based on the multiple dynamic parameters, and determine whether there are any abnormal air targets in the target group that meet preset abnormal conditions based on the multiple key performance indicators and a preset anomaly detection algorithm, wherein when there are abnormal air targets in the target group that meet the preset abnormal conditions, corresponding early warning and response measures are executed; and a generation module, configured to generate corresponding real-time analysis reports and visualization data based on the multiple dynamic parameters, and send the real-time analysis reports and visualization data to a preset ground control center.

[0013] Optionally, in one embodiment of this application, the acquisition module includes: a modeling module for constructing a multimodal aerial target detection hardware system based on preset multi-type visual sensors and dynamic visual sensors; and an acquisition module for synchronously acquiring the visible light image data, the infrared data, and the radar data through the multimodal aerial target detection hardware system.

[0014] Optionally, in one embodiment of this application, the preprocessing module includes: a denoising unit, used to denoise the multimodal data and perform data format conversion and time synchronization operations on the denoised multimodal data to obtain corresponding standard data; and an extraction unit, used to extract features from the standard data based on the target detection algorithm, and determine the dynamic identification data corresponding to each aerial target according to a preset multi-task learning strategy and the features, wherein the dynamic identification data includes the position, speed, and direction corresponding to each aerial target.

[0015] Optionally, in one embodiment of this application, the formation recognition module includes: a construction unit, configured to use each aerial target as a hypergraph node, obtain the correlation index between each aerial target, and determine the corresponding hyperedge based on the correlation index, so as to construct a corresponding current hypergraph structure based on the hypergraph node and the hyperedge; a comparison unit, configured to compare the current hypergraph structure with each of the preset multiple formation templates to obtain the similarity corresponding to each formation template; and a training unit, configured to determine the target formation template with the highest similarity among the multiple formation templates, train a pre-constructed hypergraph neural network model based on a preset formation sample training dataset, and input the target formation template into the trained hypergraph neural network model to output the current formation type corresponding to the target group.

[0016] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the airborne target formation identification method as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for identifying airborne target formations.

[0018] A fifth aspect of this application provides a computer program product, including a computer program that is executed to implement the above-described method for identifying aerial target formations.

[0019] Therefore, the embodiments of this application have the following beneficial effects:

[0020] The embodiments of this application can collect multimodal data corresponding to a target group, including visible light image data, infrared data, and radar data; preprocess the multimodal data to obtain corresponding standard data; and identify each aerial target in the target group based on the standard data and a pre-built target detection algorithm to obtain dynamic identification data corresponding to each aerial target; based on the dynamic identification data and a preset hypergraph modeling strategy, construct the current hypergraph structure corresponding to the target group; and use the current hypergraph structure to compare and identify the current formation of the target group in a pre-built aerial target formation knowledge base to determine the current formation type of the target group. This application, by introducing a dynamic knowledge base of aerial target formations and combining it with hypergraph modeling and analysis technology, achieves situational awareness of aerial target groups and high-precision, real-time aerial target formation identification, significantly improving the efficiency and accuracy of airborne surveillance and command decision-making. Therefore, it solves the problems of existing technologies, such as difficulty in efficiently integrating multi-source data, inability to achieve high-precision formation identification in complex dynamic environments, and difficulty in adapting to the changing flight mode requirements in tactical environments.

[0021] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0023] Figure 1 This is a flowchart of an aerial target formation identification method provided according to an embodiment of this application;

[0024] Figure 2 A schematic diagram of the logical architecture of an aerial target formation identification method provided in one embodiment of this application;

[0025] Figure 3 This is an example diagram of an aerial target formation identification device according to an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0027] Among them, 10-Airborne target formation identification device; 100-Acquisition module, 200-Preprocessing module, 300-Formation identification module; 401-Memory, 402-Processor, 403-Communication interface. Detailed Implementation

[0028] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0029] The following describes an aerial target formation identification method and apparatus according to embodiments of this application with reference to the accompanying drawings. Addressing the problems mentioned in the background art, this application provides an aerial target formation identification method. In this method, multimodal data corresponding to a target group is collected, including visible light image data, infrared data, and radar data. The multimodal data is preprocessed to obtain corresponding standard data. Based on the standard data and a pre-built target detection algorithm, each aerial target in the target group is identified to obtain dynamic identification data corresponding to each aerial target. Based on the dynamic identification data and a preset hypergraph modeling strategy, a current hypergraph structure corresponding to the target group is constructed. This current hypergraph structure is then used to compare and identify the current formation of the target group in a pre-built aerial target formation knowledge base to determine the current formation type of the target group. This application, by introducing a dynamic aerial target formation knowledge base and combining hypergraph modeling and analysis technology, achieves situational awareness of aerial target groups and high-precision, real-time aerial target formation identification, significantly improving the efficiency and accuracy of aerial monitoring and command decision-making. This solves the problems of existing technologies, such as the inability to efficiently integrate multi-source data, the inability to achieve high-precision formation recognition in complex dynamic environments, and the difficulty in adapting to the changing flight mode requirements in tactical environments.

[0030] Specifically, Figure 1 This is a flowchart of an aerial target formation identification method provided in an embodiment of this application.

[0031] like Figure 1 As shown, the method for identifying aerial target formations includes the following steps:

[0032] In step S101, multimodal data corresponding to the target group is collected, including visible light image data, infrared data, and radar data.

[0033] This application embodiment first utilizes multiple types of visual and dynamic visual sensors to build a multimodal aerial target detection hardware system, such as... Figure 2 As shown, multimodal data such as visible light image data, infrared data, and radar data corresponding to the target group are collected simultaneously.

[0034] Optionally, in one embodiment of this application, multimodal data corresponding to the target group is collected, wherein the multimodal data includes visible light image data, infrared data and radar data, including: constructing a multimodal aerial target detection hardware system based on preset multi-type visual sensors and dynamic visual sensors; and synchronously collecting visible light image data, infrared data and radar data through the multimodal aerial target detection hardware system.

[0035] It should be noted that the embodiments of this application can first select and install various types of visual sensors and dynamic visual sensors suitable for aerial target detection, including high-resolution cameras, infrared sensors and radar systems, etc.

[0036] To ensure comprehensive coverage of the target monitoring area, sensors should be rationally arranged in key locations and accurately calibrated and synchronized to improve the accuracy and consistency of data acquisition. In addition, the embodiments of this application should consider the environmental adaptability and anti-interference ability of the sensors to ensure that they can operate stably under various climate and lighting conditions.

[0037] Secondly, this embodiment also requires a sensor data acquisition module to ensure that various sensors can synchronously acquire and transmit multimodal data to the central processing unit. The data acquisition module should possess high bandwidth and low latency communication capabilities, supporting compatibility with multiple data formats to accommodate data output from different sensors. Simultaneously, this embodiment should implement real-time data synchronization and time stamping functions to ensure temporal consistency of data from different sensors, thereby providing a reliable foundation for subsequent data fusion and analysis. Furthermore, to ensure the security and stability of data transmission, this embodiment also requires a redundant backup system and protection mechanisms to prevent data loss or transmission interruption.

[0038] In step S102, the multimodal data is preprocessed to obtain the corresponding standard data, and based on the standard data and the pre-built target detection algorithm, each aerial target in the target group is identified to obtain the dynamic identification data corresponding to each aerial target.

[0039] Furthermore, embodiments of this application also require preprocessing of the multimodal input data and combining it with a target detection algorithm to identify aerial targets in the target group, thereby obtaining dynamic identification data corresponding to each aerial target.

[0040] Optionally, in one embodiment of this application, the multimodal data is preprocessed to obtain corresponding standard data, and each aerial target in the target group is identified based on the standard data and a pre-built target detection algorithm to obtain dynamic identification data corresponding to each aerial target. This includes: denoising the multimodal data, and performing data format conversion and time synchronization operations on the denoised multimodal data to obtain corresponding standard data; extracting features from the standard data based on the target detection algorithm, and determining the dynamic identification data corresponding to each aerial target according to a preset multi-task learning strategy and features, wherein the dynamic identification data includes the position, speed, and direction corresponding to each aerial target.

[0041] In actual implementation, the embodiments of this application can first perform preprocessing operations such as noise reduction, data format conversion and time synchronization on the received multimodal data to improve data quality and consistency.

[0042] Specifically, in the denoising process, this embodiment employs advanced filtering techniques such as Kalman filtering and median filtering to remove random noise and interference signals from the sensor data; data format conversion ensures that data output from different sensors can be unified into a standardized format for easier subsequent processing; time synchronization, through a precise timestamp mechanism, ensures that data from each sensor is perfectly aligned in time to obtain corresponding standard data, avoiding recognition errors caused by time deviations. Furthermore, this embodiment also includes data integrity checks and anomaly detection during preprocessing to ensure the reliability and stability of the input data.

[0043] Subsequently, embodiments of this application can apply target detection algorithms (such as convolutional neural networks, support vector machines, etc.) to analyze the preprocessed data (i.e., standard data) and identify the basic attributes of aerial targets, such as position, velocity, and direction (i.e., dynamic identification data). In the application of the target detection algorithm, embodiments of this application first use convolutional neural networks for feature extraction, extracting high-dimensional feature representations of aerial targets through multi-layer convolution and pooling operations. Secondly, embodiments of this application can use support vector machines or other classification algorithms to classify the extracted features, accurately distinguishing different types of aerial targets. In addition, embodiments of this application can employ a multi-task learning method to simultaneously estimate the position, velocity, and direction of the target, ensuring the consistency of various attributes.

[0044] It is understood that, in order to improve the performance and adaptability of the algorithm, the embodiments of this application may also combine transfer learning and data augmentation techniques, and utilize pre-trained models and expanded training data to further improve the accuracy and robustness of target recognition.

[0045] Therefore, the embodiments of this application, by employing high-performance computing units and optimized algorithm design, achieve rapid processing and real-time identification of large amounts of aerial target data, thus meeting the real-time situational awareness requirements in highly dynamic environments.

[0046] In step S103, based on dynamic identification data and a preset hypergraph modeling strategy, the current hypergraph structure corresponding to the target group is constructed. The current hypergraph structure is then used to compare and identify the current formation corresponding to the target group in the pre-built aerial target formation knowledge base to determine the current formation type corresponding to the target group.

[0047] Subsequently, the embodiments of this application can construct an aerial target formation knowledge base and use hypergraph modeling to model the dynamic relationships between aerial targets in order to compare and identify possible formations of target groups.

[0048] Therefore, the embodiments of this application, through the fusion of multimodal data and hypergraph modeling technology, are able to comprehensively capture the multidimensional features of aerial targets, significantly improving the accuracy and reliability of formation identification.

[0049] Optionally, in one embodiment of this application, based on dynamic identification data and a preset hypergraph modeling strategy, a current hypergraph structure corresponding to a target group is constructed. This is used to compare and identify the current formation corresponding to the target group in a pre-built aerial target formation knowledge base, thereby determining the current formation type corresponding to the target group. This includes: treating each aerial target as a hypergraph node, obtaining correlation indicators between each aerial target, and determining the corresponding hyperedges based on the correlation indicators, thereby constructing the corresponding current hypergraph structure based on the hypergraph nodes and hyperedges; comparing the similarity of the current hypergraph structure with each of a preset plurality of formation templates to obtain the similarity corresponding to each formation template; determining the target formation template with the highest similarity among the plurality of formation templates, and training a pre-built hypergraph neural network model based on a preset formation sample training dataset, and inputting the target formation template into the trained hypergraph neural network model to output the current formation type corresponding to the target group.

[0050] It should be noted that the specific steps for obtaining the current formation type corresponding to the target group in the embodiments of this application are as follows:

[0051] Step 1: Collect and store dynamic data such as the position coordinates, flight speed, heading angle, and altitude of the identified aerial targets.

[0052] After target detection is completed, this embodiment of the application needs to systematically collect dynamic data of each aerial target. This data includes the target's two-dimensional or three-dimensional position coordinates (x, y, z), flight speed v = (v... x ,v y ,vz The data includes the heading angle θ and altitude information h. To ensure the real-time performance and accuracy of the data, this application embodiment requires the establishment of an efficient data storage mechanism, such as using a time-series database to manage dynamic data. In addition, this application embodiment may employ data compression and indexing techniques to optimize storage space and query efficiency, and the timestamps and unique identifiers of the data should be accurately recorded so as to enable precise association and analysis in subsequent steps.

[0053] Step 2: Using hypergraph modeling technology, model the spatial and dynamic relationships between aerial targets to form a high-order relationship model that reflects the structure of the target group:

[0054] Those skilled in the art should understand that a hypergraph is a generalized graph structure capable of representing high-order relationships between multiple nodes. In this embodiment, each aerial target can first be considered a node in the hypergraph; secondly, based on indicators such as spatial distance, velocity similarity, and heading consistency between targets, hyperedges can be constructed to represent the relationships between multiple targets, and the weight w of the hyperedge can be defined using the following formula. e :

[0055]

[0056] Where, d ij δ(v) represents the distance between target i and target j; i ,v j ) represents the similarity measure of velocity vectors; cos(θ) i -θ j ) indicates the consistency of the heading angle; α, β, and γ are weighting coefficients used to balance the importance of various indicators.

[0057] Therefore, in the embodiments of this application, the supergraph can effectively capture the complex dynamic relationships within the target group, form a high-order relationship model, and reflect the overall structure and behavior pattern of the target group.

[0058] Step 3: Based on the constructed hypergraph model, compare the possible formations of the target group, and identify the specific formation type of the current target group through pattern matching or machine learning algorithms:

[0059] After constructing the hypergraph model, the embodiments of this application can compare the hypergraph of the current target group with the predefined array template, and can use pattern matching algorithms or machine learning methods, such as Hypergraph Convolutional Network (HGNN), for recognition. The specific implementation steps include: array pattern matching and array pattern recognition.

[0060] In terms of formation pattern matching, this application embodiment can define multiple pre-defined formation templates, each template corresponding to a specific hypergraph structure, and calculate the similarity S(G,G) between the current hypergraph and each template hypergraph. t The template with the highest similarity is selected as the recognition result of the current formation.

[0061] As one possible approach, the similarity calculation in this application embodiment can employ graph isomorphism detection, subgraph matching, or a similarity index based on node and edge features, as shown in the following formula:

[0062]

[0063] Among them, E and E t These are the current hypergraph G and the template hypergraph G, respectively. t The set of superedges; w e and The weights of their respective superedges.

[0064] Secondly, regarding formation pattern recognition, this embodiment of the application can utilize the constructed hypergraph data to train a hypergraph neural network model, enabling it to automatically learn and recognize different formation types. During training, this embodiment of the application can employ a supervised learning method and use labeled formation sample data (i.e., formation sample training dataset) for training. The hypergraph neural network model outputs the formation category to which the current target group belongs. Its prediction process can be represented as:

[0065]

[0066] Among them, f HGNN This represents a hypergraph neural network; G is the input hypergraph; and Θ is the model parameters.

[0067] Once trained, the hypergraph neural network model can efficiently classify real-time input hypergraphs, thereby accurately identifying the current specific formation type of the target group.

[0068] Therefore, the embodiments of this application can effectively cope with interference and occlusion in complex flight environments through multi-source data fusion, thereby maintaining stable recognition performance in various complex scenarios.

[0069] Optionally, in one embodiment of this application, after determining the current formation type corresponding to the target group, the method further includes: monitoring the changing trend information corresponding to the target group based on the current formation type and a pre-built target formation dynamic perception model, so as to update the air target formation knowledge base according to the changing trend information; monitoring multiple dynamic parameters corresponding to the target group in real time, determining multiple key performance indicators based on the multiple dynamic parameters, and judging whether there are abnormal air targets in the target group that meet the preset abnormal conditions based on the multiple key performance indicators and a preset anomaly detection algorithm, wherein when there are abnormal air targets in the target group that meet the preset abnormal conditions, corresponding early warning and response measures are executed; generating corresponding real-time analysis reports and visualization data based on multiple dynamic parameters, and sending the real-time analysis reports and visualization data to a preset ground control center.

[0070] It should be noted that after determining the current formation type corresponding to the target group, this embodiment of the application also needs to establish a target formation dynamic perception model to update the aerial target formation knowledge base in real time according to the changes in formation, and to monitor the position and speed of the target group in real time for real-time analysis of ground target situational awareness. The specific steps are shown in the following formula:

[0071] Step 1: Establish a dynamic perception model of the target formation and continuously monitor the changing trends of the formation (such as dynamic behaviors like formation expansion, contraction, and rotation).

[0072] Understandably, in order to accurately perceive the dynamic changes in the target formation, this embodiment of the application needs to construct a comprehensive dynamic perception model. Firstly, this embodiment of the application can promptly capture the dynamic behavior of the formation by monitoring real-time changes in the target group's formation, such as expansion, contraction, and rotation. In actual implementation, this embodiment of the application can employ advanced filtering techniques and predictive algorithms to track the evolution of the formation, ensuring a timely response to every changing trend.

[0073] Secondly, embodiments of this application can utilize machine learning algorithms such as Hidden Markov Models or Long Short-Term Memory Networks to identify specific dynamic behavioral patterns of target groups. For example, when formation expansion is detected, the model should be able to automatically identify and classify it as a specific tactical action, thereby providing more accurate situational awareness.

[0074] Furthermore, the embodiments of this application can comprehensively analyze formation changes by combining multi-dimensional data such as position, velocity, and acceleration, thereby improving the ability to understand and predict dynamic changes in formations.

[0075] Finally, the embodiments of this application can establish a real-time feedback mechanism to ensure that the detected formation changes can be promptly transmitted to other functional modules, so as to maintain the coordination and efficient operation of the entire sensing system, thereby effectively capturing and analyzing the real-time changes of the target formation, and improving the adaptability and response speed to complex dynamic environments.

[0076] Step 2: Based on real-time monitored formation changes, dynamically update the aerial target formation knowledge base to ensure that the formation models in the knowledge base remain consistent with the actual situation.

[0077] Understandably, to ensure the knowledge base always reflects the current actual formation situation, this application embodiment needs to dynamically update it. First, this application embodiment can synchronize the formation change data monitored by the dynamic perception model with the existing knowledge base, integrating old and new data through data fusion technology to maintain the real-time performance and accuracy of the knowledge base. Second, this application embodiment can utilize pattern recognition algorithms to match the detected new formation patterns with the formation models in the knowledge base. If a new formation type is detected, this application embodiment can automatically generate a new model and incorporate it into the knowledge base, thereby continuously expanding and improving the content of the knowledge base. Furthermore, version control is implemented for the formation models in the knowledge base, recording the content and reasons for each update to ensure that it can be retrospectively reviewed when needed, thus maintaining the reliability and traceability of the knowledge base. Finally, this application embodiment can also introduce automated management tools to regularly scan and update the knowledge base, reducing manual intervention and improving update efficiency and accuracy. This automated management not only improves the update speed but also ensures the continuous optimization and consistency of the knowledge base content.

[0078] Therefore, the knowledge base in this application embodiment can reflect the latest dynamics of the target formation in a timely manner, so that data identification and analysis can always be based on the most accurate and up-to-date data.

[0079] Step 3: Monitor the target group's position, speed, and other key information in real time, and feed the monitoring results back to the ground control center to support ground target situational awareness and real-time analysis, and assist command and decision-making.

[0080] To achieve comprehensive situational awareness and efficient command and decision support, real-time monitoring and feedback of key information about target groups are necessary. First, embodiments of this application can continuously collect and analyze the target group's location information, velocity, and other dynamic parameters to reflect the current situation promptly. Second, embodiments of this application can set key performance indicators such as the target's rate of change of velocity, acceleration, and heading angle changes, and monitor in real time whether these key performance indicators exceed preset thresholds. This helps to promptly detect anomalies or potential threats, ensuring a clear understanding of the dynamic changes of the target group.

[0081] Furthermore, embodiments of this application can utilize anomaly detection algorithms to identify potential abnormal behaviors or emergencies within a group. Once an anomaly is detected, embodiments of this application can immediately issue an alarm and take corresponding countermeasures to ensure rapid response and handling of emergencies. Simultaneously, embodiments of this application can display real-time monitoring data to the ground control center through visualization tools, including electronic maps and dynamic trajectory maps, helping command personnel to intuitively understand the current situation. Additionally, embodiments of this application can generate real-time reports and analysis results to assist command decision-making, making the decision-making process more scientific and efficient.

[0082] Finally, the embodiments of this application can also establish an efficient two-way communication mechanism to ensure that the ground control center can receive monitoring data in a timely manner and issue instructions or request detailed information to the air system as needed. This efficient communication mechanism effectively ensures the timely transmission of information and the rapid execution of instructions, improves the overall response speed and accuracy of combat or monitoring, thereby enabling comprehensive and real-time monitoring of target groups and effectively supporting the situational awareness and decision-making of the ground control center, thus improving the overall response speed and accuracy of combat or monitoring.

[0083] Therefore, the embodiments of this application can update the knowledge base in real time through the target formation dynamic perception model, thereby adapting to changes in new or unconventional formations and improving the flexibility and adaptability of aerial target formation identification. In addition, the embodiments of this application can also provide reliable decision support for the ground command center by outputting high-precision formation identification results in real time, thereby improving the efficiency and accuracy of overall aerial monitoring and command decision-making.

[0084] The aerial target formation identification method proposed in this application involves collecting multimodal data corresponding to a target group, including visible light image data, infrared data, and radar data. The multimodal data is preprocessed to obtain corresponding standard data. Based on the standard data and a pre-built target detection algorithm, each aerial target in the target group is identified to obtain dynamic identification data for each aerial target. Based on the dynamic identification data and a preset hypergraph modeling strategy, a current hypergraph structure corresponding to the target group is constructed. This current hypergraph structure is then used to compare and identify the current formation of the target group in a pre-built aerial target formation knowledge base to determine the current formation type of the target group. This application, by introducing a dynamic aerial target formation knowledge base and combining hypergraph modeling and analysis techniques, achieves situational awareness of aerial target groups and high-precision, real-time aerial target formation identification, significantly improving the efficiency and accuracy of aerial monitoring and command decision-making.

[0085] Secondly, the aerial target formation identification device according to the embodiments of this application is described with reference to the accompanying drawings.

[0086] Figure 3 This is a block diagram of an aerial target formation identification device according to an embodiment of this application.

[0087] like Figure 3 As shown, the aerial target formation identification device 10 includes: a data acquisition module 100, a preprocessing module 200, and a formation identification module 300.

[0088] The acquisition module 100 is used to acquire multimodal data corresponding to the target group, including visible light image data, infrared data and radar data.

[0089] The preprocessing module 200 is used to preprocess the multimodal data to obtain the corresponding standard data, and based on the standard data and the pre-built target detection algorithm, to identify each aerial target in the target group to obtain the dynamic identification data corresponding to each aerial target.

[0090] The formation recognition module 300 is used to construct the current hypergraph structure corresponding to the target group based on dynamic recognition data and preset hypergraph modeling strategies. The current hypergraph structure is then used to compare and recognize the current formation corresponding to the target group in a pre-built aerial target formation knowledge base to determine the current formation type of the target group.

[0091] Optionally, in one embodiment of this application, the air target formation identification device 10 of this application embodiment further includes: a monitoring module, a judgment module, and a generation module.

[0092] The monitoring module is used to monitor the changing trend information of the target group after determining the current formation type corresponding to the target group, based on the current formation type and the pre-built target formation dynamic perception model, so as to update the air target formation knowledge base according to the changing trend information.

[0093] The judgment module is used to monitor multiple dynamic parameters corresponding to the target group in real time, determine multiple key performance indicators based on the multiple dynamic parameters, and determine whether there are abnormal aerial targets in the target group that meet the preset abnormal conditions based on the multiple key performance indicators and the preset anomaly detection algorithm. When there are abnormal aerial targets in the target group that meet the preset abnormal conditions, corresponding early warning and response measures are executed.

[0094] The generation module is used to generate corresponding real-time analysis reports and visualization data based on multiple dynamic parameters, and send the real-time analysis reports and visualization data to the preset ground control center.

[0095] Optionally, in one embodiment of this application, the acquisition module 100 includes a modeling module and an acquisition module.

[0096] The modeling module is used to construct a multimodal aerial target detection hardware system based on preset multi-type visual sensors and dynamic visual sensors.

[0097] The acquisition module is used to simultaneously acquire visible light image data, infrared data, and radar data through a multimodal aerial target detection hardware system.

[0098] Optionally, in one embodiment of this application, the preprocessing module 200 includes a noise reduction unit and an extraction unit.

[0099] The denoising unit is used to denoise the multimodal data and perform data format conversion and time synchronization operations on the denoised multimodal data to obtain the corresponding standard data.

[0100] The extraction unit is used to extract features from standard data based on the target detection algorithm, and determine the dynamic identification data corresponding to each aerial target according to the preset multi-task learning strategy and features. The dynamic identification data includes the position, speed and direction of each aerial target.

[0101] Optionally, in one embodiment of this application, the formation recognition module 300 includes: a construction unit, a comparison unit, and a training unit.

[0102] The construction unit is used to treat each aerial target as a hypergraph node, obtain the correlation index between each aerial target, and determine the corresponding hyperedge based on the correlation index, so as to construct the corresponding current hypergraph structure based on the hypergraph node and hyperedge.

[0103] The comparison unit is used to compare the similarity of the current hypergraph structure with each of the preset multiple array templates to obtain the similarity of each array template.

[0104] The training unit is used to determine the target formation template with the highest similarity among multiple formation templates, and to train a pre-built hypergraph neural network model based on a preset formation sample training dataset. The target formation template is then input into the trained hypergraph neural network model to output the current formation type corresponding to the target group.

[0105] It should be noted that the foregoing explanation of the aerial target formation identification method embodiment also applies to the aerial target formation identification device of this embodiment, and will not be repeated here.

[0106] The aerial target formation identification device proposed in this application includes a data acquisition module 100 for acquiring multimodal data corresponding to a target group, wherein the multimodal data includes visible light image data, infrared data, and radar data; a preprocessing module 200 for preprocessing the multimodal data to obtain corresponding standard data, and identifying each aerial target in the target group based on the standard data and a pre-built target detection algorithm to obtain dynamic identification data corresponding to each aerial target; and a formation identification module 300 for constructing the current hypergraph structure corresponding to the target group based on the dynamic identification data and a preset hypergraph modeling strategy, so as to use the current hypergraph structure to compare and identify the current formation of the target group in a pre-built aerial target formation knowledge base to determine the current formation type of the target group. This application, by introducing an aerial target formation dynamic knowledge base and combining hypergraph modeling and analysis technology, achieves situational awareness of aerial target groups and high-precision, real-time aerial target formation identification, significantly improving the efficiency and accuracy of aerial monitoring and command decision-making.

[0107] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0108] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0109] When the processor 402 executes the program, it implements the air target formation identification method provided in the above embodiments.

[0110] Furthermore, electronic devices also include:

[0111] Communication interface 403 is used for communication between memory 401 and processor 402.

[0112] The memory 401 is used to store computer programs that can run on the processor 402.

[0113] The memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0114] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0115] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0116] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0117] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for identifying aerial target formations.

[0118] This application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-described method for identifying aerial target formations.

[0119] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0120] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0121] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0122] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0123] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0124] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0125] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0126] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for identifying aerial target formations, characterized in that, Includes the following steps: Collect multimodal data corresponding to the target group, wherein the multimodal data includes visible light image data, infrared data and radar data; The multimodal data is preprocessed to obtain corresponding standard data, and based on the standard data and a pre-built target detection algorithm, each aerial target in the target group is identified to obtain dynamic identification data corresponding to each aerial target. Based on the dynamic identification data and the preset hypergraph modeling strategy, the current hypergraph structure corresponding to the target group is constructed. The current hypergraph structure is then used to compare and identify the current formation corresponding to the target group in the pre-built aerial target formation knowledge base to determine the current formation type corresponding to the target group.

2. The method according to claim 1, characterized in that, After determining the current formation type corresponding to the target group, the method further includes: Based on the current formation type and the pre-built target formation dynamic perception model, monitor the change trend information corresponding to the target group, and update the air target formation knowledge base according to the change trend information; The system monitors multiple dynamic parameters corresponding to the target group in real time, determines multiple key performance indicators based on the multiple dynamic parameters, and determines whether there are any abnormal aerial targets in the target group that meet the preset abnormal conditions based on the multiple key performance indicators and the preset anomaly detection algorithm. When there are any abnormal aerial targets in the target group that meet the preset abnormal conditions, the system executes corresponding early warning and response measures. Based on the multiple dynamic parameters, corresponding real-time analysis reports and visualization data are generated, and the real-time analysis reports and visualization data are sent to a preset ground control center.

3. The method according to claim 1, characterized in that, The multimodal data corresponding to the target group being collected includes visible light image data, infrared data, and radar data, comprising: A multimodal aerial target detection hardware system is constructed based on preset multi-type visual sensors; The multimodal aerial target detection hardware system simultaneously acquires visible light image data, infrared data, and radar data.

4. The method according to claim 3, characterized in that, The process of preprocessing the multimodal data to obtain corresponding standard data, and then identifying each aerial target in the target group based on the standard data and a pre-built target detection algorithm to obtain dynamic identification data for each aerial target, includes: The multimodal data is denoised, and the denoised multimodal data is then converted in data format and synchronized in time to obtain the corresponding standard data. Based on the target detection algorithm, features of the standard data are extracted, and dynamic identification data corresponding to each aerial target is determined according to a preset multi-task learning strategy and the features. The dynamic identification data includes the position, speed and direction of each aerial target.

5. The method according to claim 4, characterized in that, Based on the dynamic identification data and the preset hypergraph modeling strategy, the current hypergraph structure corresponding to the target group is constructed. This current hypergraph structure is then used to compare and identify the current formation corresponding to the target group in a pre-built aerial target formation knowledge base to determine the current formation type of the target group. This includes: Each aerial target is used as a hypergraph node, and the correlation index between each aerial target is obtained. The corresponding hyperedge is determined according to the correlation index, so as to construct the corresponding current hypergraph structure based on the hypergraph node and the hyperedge. The current hypergraph structure is compared with each of the preset array templates to obtain the similarity score for each array template. The target formation template with the highest similarity among the multiple formation templates is determined, and a pre-constructed hypergraph neural network model is trained based on a preset formation sample training dataset. The target formation template is then input into the trained hypergraph neural network model to output the current formation type corresponding to the target group.

6. An aerial target formation identification device, characterized in that, include: The acquisition module is used to acquire multimodal data corresponding to the target group, wherein the multimodal data includes visible light image data, infrared data and radar data; The preprocessing module is used to preprocess the multimodal data to obtain corresponding standard data, and based on the standard data and a pre-built target detection algorithm, to identify each aerial target in the target group to obtain dynamic identification data corresponding to each aerial target. The formation identification module is used to construct the current hypergraph structure corresponding to the target group based on the dynamic identification data and the preset hypergraph modeling strategy, so as to use the current hypergraph structure to compare and identify the current formation corresponding to the target group in the pre-built air target formation knowledge base, so as to determine the current formation type corresponding to the target group.

7. The apparatus according to claim 6, characterized in that, Also includes: The monitoring module is used to monitor the changing trend information of the target group after determining the current formation type corresponding to the target group, based on the current formation type and the pre-built target formation dynamic perception model, so as to update the air target formation knowledge base according to the changing trend information. The judgment module is used to monitor multiple dynamic parameters corresponding to the target group in real time, determine multiple key performance indicators based on the multiple dynamic parameters, and determine whether there are any abnormal aerial targets in the target group that meet the preset abnormal conditions based on the multiple key performance indicators and the preset anomaly detection algorithm. When there are abnormal aerial targets in the target group that meet the preset abnormal conditions, corresponding early warning and response measures are executed. The generation module is used to generate corresponding real-time analysis reports and visualization data based on the multiple dynamic parameters, and send the real-time analysis reports and visualization data to a preset ground control center.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the air target formation identification method as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the air target formation identification method as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the air target formation identification method as described in any one of claims 1-5.

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