A dynamic formation heterogeneous cluster electromagnetic interference suppression system based on adjacency clustering and parallel optimization

Abstract

The present application relates to the field of aircraft formation heterogeneous cluster, and particularly relates to a dynamic formation heterogeneous cluster electromagnetic interference suppression system based on adjacent clustering parallel optimization. The scheme comprises: an initialization module, which initializes system parameters and task scenarios, sets a task time period set of the aircraft formation, obtains a set of aircrafts in the formation and a corresponding set of airborne equipment, and initializes inherent parameters of all equipment; a time-varying task weight calculation module, which calculates time-varying task weights of the equipment according to whether the aircraft participates in the formation at the current moment and the importance of the equipment at the current task stage; an effective interference neighbor set construction module, which calculates path loss based on the spatial positions between the equipment, and identifies an effective neighbor set which simultaneously constitutes interference to the equipment in the spatial domain and the frequency domain; an adjacent clustering module, which randomly selects the equipment and preselects a new strategy; a strategy updating module, which updates the strategy; and an iteration module, which iterates until convergence. The present application is suitable for aircraft formation heterogeneous cluster.

Description

Technical Field

[0001] This invention relates to the field of heterogeneous aircraft formation clusters, specifically to a dynamic heterogeneous formation electromagnetic interference suppression system based on adjacency clustering parallel optimization. Background Technology

[0002] Multi-aircraft formations and cluster platforms are core features of future intelligent aviation systems. However, with the exponential growth in the number of airborne radio frequency devices (communication, navigation, radar, etc.) within the formation, competition for limited airspace spectrum intensifies, and electromagnetic interference seriously threatens system stability and electromagnetic compatibility.

[0003] The existing technology has the following problems:

[0004] Static spectrum allocation cannot adapt to the dynamic changes of electromagnetic signals under task-driven conditions, which can easily lead to resource consumption or interference and reduce compatibility.

[0005] The computational demands of centralized methods such as genetic algorithms increase exponentially with the number of devices, making it difficult to meet the real-time requirements of large-scale network global optimization and strong coupling and high dynamics of formations.

[0006] Existing distributed methods (such as deep learning and multi-agent reinforcement learning) are mostly adapted to homogeneous devices and rely on hard-to-obtain historical training data, which is not adaptable to heterogeneous device clusters.

[0007] The sole pursuit of maximizing the signal-to-interference-plus-noise ratio ignores the differences in equipment priorities during mission phases, system instability caused by frequency switching, and power transmission efficiency issues. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide a dynamic formation heterogeneous cluster electromagnetic interference suppression system based on adjacency clustering parallel optimization. It combines distributed game theory and parallel computing to improve solution efficiency, ensuring communication quality while taking into account system stability and low power consumption requirements.

[0009] The present invention achieves the above objectives by adopting the following technical solution: The present invention provides a dynamic formation heterogeneous cluster electromagnetic interference suppression system based on adjacency clustering parallel optimization, comprising:

[0010] The initialization module initializes system parameters and mission scenarios, sets the mission time period set for the aircraft formation, obtains the set of aircraft in the formation and their corresponding airborne equipment set, and initializes the inherent parameters of all equipment, including the set of selectable center frequencies, the set of selectable transmit power, bandwidth, and system noise floor.

[0011] The time-varying task weight calculation module calculates the weight based on the aircraft's current position. Calculate the status of each device in the formation and its importance in the current mission phase. The normalized task weights are used to determine the set of active devices participating in the game;

[0012] The effective interference neighbor set construction module calculates path loss based on the spatial location between devices and, combined with the frequency overlap under the current strategy, identifies the construction of effective interference neighbor sets for devices in both the spatial and frequency domains.

[0013] The adjacency clustering module randomly selects devices and pre-selects new strategies, and constructs independent clusters based on all neighbor nodes involved by the device under the original and new strategies;

[0014] The strategy update module performs parallel computation within each cluster, calculating the utility function based on three indicators: spectral security margin, frequency switching degree, and power emission factor. If the new strategy can improve the utility value, i.e., meet the better response conditions, the device strategy is updated to the new strategy; otherwise, it remains unchanged.

[0015] The iteration module repeatedly executes the functions of the adjacency clustering module and the policy update module until the maximum number of iterations is reached or the system's total potential function converges, and outputs the final spectrum and power allocation scheme.

[0016] Furthermore, the time-varying task weight calculation module is specifically used to calculate the weight of the aircraft. Deployment of device collection The total number of devices is Then define the device. At any moment Unified task weights for:

[0017] ;

[0018] in, It is a binary variable representing an aircraft. At any moment Does it exist in the formation? Indicates equipment The task importance coefficient at that moment must satisfy the normalization condition. .

[0019] Furthermore, the effective interference neighbor set construction module is specifically used for:

[0020] Calculate path loss:

[0021] equipment Transmit port to device Free space path loss at the receiver port for:

[0022] ;

[0023] in, for and The Euclidean distance between the two devices. The speed of light;

[0024] Identify spatial neighbors :

[0025] Filter out signals whose arrival strength exceeds the system noise floor. The equipment is used in the following manner:

[0026] ;

[0027] in, For equipment At any moment The transmission power;

[0028] Identify frequency domain neighbors :

[0029] Filter out devices with a center frequency difference smaller than that of the equipment Frequency domain neighbor threshold The equipment is used in the following manner:

[0030] ;

[0031] in, and Representing the equipment and For the working center frequency;

[0032] Determine valid neighbors :

[0033] Take space neighbors and frequency domain neighbors The intersection of the devices Effective Neighbors : .

[0034] Furthermore, the core logic of clustering is as follows:

[0035] In the In this iteration, for the randomly selected device Randomly generate a trial strategy Construct clusters ,in The device under the current policy Neighborhood set, It is a device under a probing strategy The potential neighbor set will Treat them as a whole and remove them from the remaining devices until all devices have been grouped.

[0036] Furthermore, the specific logic for policy updates in the policy update module is as follows:

[0037] In each cluster Within this framework, only local information needs to be used to calculate utility; if a trial-and-error strategy is employed... Make the equipment If the utility increases, update the policy:

[0038] ;

[0039] in, .

[0040] Furthermore, spectrum security margin For quantification equipment Its anti-interference capability is expressed as:

[0041] , This represents the theoretical maximum margin under no-interference conditions. and These are the cumulative spectral interference integrals from the concentrated transmitters and receivers of the interfering neighbors, respectively;

[0042] Frequency switching The expression is used to punish drastic changes in frequency:

[0043] ,in, For the previous moment Frequency strategy;

[0044] Power emission factor The expression used to control energy consumption and radiated interference is:

[0045] ;

[0046] Then, equipment Local utility function for:

[0047] ;

[0048] in, , Frequency switching degree and weighting coefficients; For equipment The type identifier is set to 1 for transmitting devices and 0 for receiving devices.

[0049] The beneficial effects of this invention are as follows:

[0050] This invention uses a clustering algorithm based on device adjacency relationships to decouple large-scale networks into multiple independent sub-problems for parallel processing.

[0051] The model constructed in this invention guarantees that the algorithm can converge to a pure policy Nash equilibrium within a finite number of iterations, and avoids getting trapped in local optima.

[0052] This invention improves spectrum security margin (interference suppression) while effectively reducing frequency switching frequency (improving stability) and transmission power (reducing energy consumption).

[0053] This invention introduces task weights and multi-stage game theory, which can effectively cope with the high dynamic interference environment caused by task phase switching, equipment power-on / off and spatial position changes in aircraft formation. Attached Figure Description

[0054] Figure 1 This is a block diagram of an electromagnetic interference suppression system for dynamic formation heterogeneous clusters based on adjacency clustering parallel optimization provided by an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0056] This invention provides a dynamic formation heterogeneous cluster electromagnetic interference suppression system based on adjacency clustering parallel optimization, such as... Figure 1 As shown, it specifically includes:

[0057] The initialization module initializes system parameters and mission scenarios, and sets the mission time period set for the aircraft formation. Obtain the aircraft set within the formation and its corresponding airborne equipment set Initialize the inherent parameters of all devices, including the optional set of center frequencies. Optional transmit power set ,bandwidth and system noise floor ;

[0058] The time-varying task weight calculation module calculates the weight based on the aircraft's current position. Calculate the status of each device in the formation and its importance in the current mission phase. Normalized task weights This determines the set of active devices participating in the game.

[0059] If the plane Deployment of device collection The total number of devices is Then define the device. At any moment Unified task weights for:

[0060] ;

[0061] in, It is a binary variable representing an aircraft. At any moment Does it exist in the formation? Indicates equipment The task importance coefficient at that moment must satisfy the normalization condition. .

[0062] The effective interference neighbor set construction module calculates path loss based on the spatial location between devices. Based on the frequency overlap under the current strategy, the system identifies instances of simultaneous device interference in both the spatial and frequency domains. Constitute an effective set of interfering neighbors .

[0063] Specifically as follows:

[0064] Calculate path loss:

[0065] equipment Transmit port to device Free space path loss at the receiver port for:

[0066] ;

[0067] in, for and The Euclidean distance between the two devices. The speed of light;

[0068] Identify spatial neighbors :

[0069] Filter out signals whose arrival strength exceeds the system noise floor. Equipment:

[0070] ;

[0071] in, For equipment At any moment The transmission power;

[0072] Identify frequency domain neighbors :

[0073] Filter out devices with a center frequency difference smaller than that of the equipment Frequency domain neighbor threshold Equipment:

[0074] ;

[0075] in, and Representing the equipment and For the working center frequency;

[0076] Determine valid neighbors :

[0077] Take space neighbors and frequency domain neighbors The intersection of the devices Effective Neighbors : .

[0078] Adjacency clustering module, randomly selects devices And pre-select new strategies According to the device in the original strategy and new strategies All neighboring nodes involved (i.e. ), construct independent clusters .

[0079] The core logic of clustering is as follows:

[0080] In the In this iteration, for the randomly selected device Randomly generate a trial strategy Construct clusters ,in The device under the current policy Neighborhood set, It is a device under a probing strategy The potential neighbor set will Treat them as a whole and remove them from the remaining devices until all devices have been grouped.

[0081] The policy update module performs parallel computation within each cluster, based on spectral security margin. Frequency switching degree and power emission factor The utility function is calculated using three metrics. If the new strategy improves the utility value, i.e., meets the conditions for a better response, then the device strategy is updated. Otherwise, it remains unchanged.

[0082] The logic for policy updates is as follows:

[0083] In each cluster Within this framework, only local information needs to be used to calculate utility; if a trial-and-error strategy is employed... Make the equipment If the utility increases, update the policy:

[0084] ;

[0085] in, .

[0086] Spectrum security margin For quantification equipment Its anti-interference capability is expressed as:

[0087] , This represents the theoretical maximum margin under no-interference conditions. and These are the cumulative spectral interference integrals from the concentrated transmitters and receivers of the interfering neighbors, respectively;

[0088] Frequency switching The expression is used to punish drastic changes in frequency:

[0089] ,in, For the previous moment Frequency strategy;

[0090] Power emission factor The expression used to control energy consumption and radiated interference is:

[0091] ;

[0092] Then, equipment Local utility function for:

[0093] ;

[0094] in, , Frequency switching degree and weighting coefficients; For equipment The type identifier is set to 1 for transmitting devices and 0 for receiving devices.

[0095] The iteration module repeatedly executes the functions of the adjacency clustering module and the policy update module until the maximum number of iterations is reached. or the system's total potential function Converges and outputs the final spectrum and power allocation scheme.

[0096] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A dynamic formation heterogeneous cluster electromagnetic interference suppression system based on adjacency clustering parallel optimization, characterized in that, include: The initialization module initializes system parameters and mission scenarios, sets the mission time period set for the aircraft formation, obtains the set of aircraft in the formation and their corresponding airborne equipment set, and initializes the inherent parameters of all equipment, including the set of selectable center frequencies, the set of selectable transmit power, bandwidth, and system noise floor. The time-varying task weight calculation module calculates the weight based on the aircraft's current position. Calculate the status of each device in the formation and its importance in the current mission phase. The normalized task weights are used to determine the set of active devices participating in the game; The effective interference neighbor set construction module calculates path loss based on the spatial location between devices and, combined with the frequency overlap under the current strategy, identifies the construction of effective interference neighbor sets for devices in both the spatial and frequency domains. The adjacency clustering module randomly selects devices and pre-selects new strategies, and constructs independent clusters based on all neighbor nodes involved by the device under the original and new strategies; The strategy update module performs parallel computation within each cluster, calculating the utility function based on three indicators: spectral security margin, frequency switching degree, and power emission factor. If the new strategy can improve the utility value, i.e., meet the better response conditions, the device strategy is updated to the new strategy; otherwise, it remains unchanged. The iteration module repeatedly executes the functions of the adjacency clustering module and the policy update module until the maximum number of iterations is reached or the system's total potential function converges, and outputs the final spectrum and power allocation scheme.

2. The electromagnetic interference suppression system for dynamic formation heterogeneous clusters based on adjacency clustering parallel optimization according to claim 1, characterized in that, The time-varying task weight calculation module is specifically used for, if the aircraft Deployment of device collection The total number of devices is Then define the device. At any moment Unified task weights for: ； in, It is a binary variable representing an aircraft. At any moment Does it exist in the formation? Indicates equipment The task importance coefficient at that moment must satisfy the normalization condition. .

3. The electromagnetic interference suppression system for dynamic formation heterogeneous clusters based on adjacency clustering parallel optimization according to claim 1, characterized in that, The effective interference neighbor set building module is specifically used for: Calculate path loss: equipment Transmit port to device Free space path loss at the receiver port for: ； in, for and The Euclidean distance between the two devices. The speed of light; Identify spatial neighbors : Filter out signals whose arrival strength exceeds the system noise floor. The equipment is used in the following manner: ； in, For equipment At any moment The transmission power; Identify frequency domain neighbors : Filter out devices with a center frequency difference smaller than that of the equipment Frequency domain neighbor threshold The equipment is used in the following manner: ； in, and Representing the equipment and For the working center frequency; Determine valid neighbors : Take space neighbors and frequency domain neighbors The intersection of the devices Effective Neighbors : .

4. The electromagnetic interference suppression system for dynamic formation heterogeneous clusters based on adjacency clustering parallel optimization according to claim 1, characterized in that, The core logic of clustering is as follows: In the In this iteration, for the randomly selected device Randomly generate a trial strategy Construct clusters ,in The device under the current strategy Neighborhood set, It is a device under a probing strategy The potential neighbor set will Treat them as a whole and remove them from the remaining devices until all devices have been grouped.

5. The electromagnetic interference suppression system for dynamic formation heterogeneous clusters based on adjacency clustering parallel optimization according to claim 4, characterized in that, The logic for policy updates in the policy update module is as follows: In each cluster Within this framework, only local information needs to be used to calculate utility; if a trial-and-error strategy is employed... Make the equipment If the utility increases, update the policy: ； in, .

6. The electromagnetic interference suppression system for dynamic formation heterogeneous clusters based on adjacency clustering parallel optimization according to claim 1, characterized in that, Spectrum security margin For quantification equipment Its anti-interference capability is expressed as: , This represents the theoretical maximum margin under no-interference conditions. and These are the cumulative spectral interference integrals from the concentrated transmitters and receivers of the interfering neighbors, respectively; Frequency switching The expression is used to punish drastic changes in frequency: ,in, For the previous moment Frequency strategy; Power emission factor The expression used to control energy consumption and radiated interference is: ； Then, equipment Local utility function for: ； in, , Frequency switching degree and weighting coefficients; For equipment The type identifier is set to 1 for transmitting devices and 0 for receiving devices.

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