An electromagnetic environment anti-disturbance control method for an airborne distributed electronic countermeasure system

Abstract

This invention discloses an electromagnetic environment disturbance immunity control method for an airborne distributed electronic countermeasures system, belonging to the field of airborne information system control technology. The method includes: distributed electronic countermeasures node synchronization, environmental anomaly disturbance determination, node observation anomaly correction, disturbance immunity control parameter generation, and environmental disturbance immunity control. It involves reversibly correcting observation data affected by gain compression or limiting to generate effective node observation data; further, it generates collaborative disturbance immunity control parameters based on the effective observation data and node operating constraints, and achieves closed-loop management of disturbance immunity control through control execution state retrieval and consistency verification. This invention can effectively distinguish between external suppression disturbances, local mutual interference, and node anomaly disturbances, improving the authenticity of observation data and the targeting of disturbance immunity control, and realizing stable operation and collaborative disturbance immunity capabilities of the airborne distributed electronic countermeasures system in complex electromagnetic environments.

Description

Technical Field

[0001] This invention relates to the field of airborne information system control technology, specifically to an electromagnetic environment disturbance immunity control method for an airborne distributed electronic countermeasures system. Background Technology

[0002] The electromagnetic environment disturbance rejection control method for airborne distributed electronic countermeasures systems refers to the processing method that, during the operation of an airborne distributed electronic countermeasures system, collects, analyzes, and controls the operating status and electromagnetic environment changes of each electronic countermeasures node to achieve stable operation and disturbance rejection of the electronic countermeasures nodes in a complex electromagnetic environment.

[0003] In practical applications, airborne electronic countermeasures systems typically operate in complex electromagnetic environments. These environments not only contain external suppression and interference signals but also exhibit mutual interference between the various electronic countermeasures nodes within the system. Furthermore, abnormal equipment status of the electronic countermeasures nodes themselves can cause abnormal changes in the received signals. In existing technologies, electronic countermeasures systems usually generate anti-disturbance control parameters directly based on changes in the strength of the received signals at the nodes. However, due to the lack of a node status alignment mechanism under a unified time reference, there are time deviations in the observation data of each node. At the same time, existing methods struggle to accurately distinguish between external suppression interference, internal mutual interference, and observation changes caused by abnormal node equipment, which can easily lead to misjudgment of the source of disturbance.

[0004] Furthermore, in environments with strong interference, the receiving link of electronic countermeasures nodes may experience gain compression or amplitude limiting, resulting in distortion of the received observation results. Existing methods lack an effective correction mechanism for distorted observation results, leading to inaccurate control parameter generation. At the same time, after the control parameters are issued and executed, existing technologies typically lack mechanisms for sampling the execution status and verifying the anti-disturbance effect, making the control execution effect unverifiable.

[0005] Therefore, there is an urgent need for an electromagnetic environment disturbance control method for airborne distributed electronic countermeasures systems that can achieve node state synchronization, accurate determination of disturbance sources, correction of observation data, and closed-loop verification of control execution. Summary of the Invention

[0006] To address the above issues and overcome the shortcomings of existing technologies, this invention provides a method for electromagnetic environment disturbance immunity control of an airborne distributed electronic countermeasures system. The technical solution adopted by this invention is as follows: This invention provides a method for electromagnetic environment disturbance immunity control of an airborne distributed electronic countermeasures system, which includes the following steps:

[0007] Step S1: Distributed electronic countermeasures node synchronization;

[0008] Step S2: Determine abnormal environmental disturbances;

[0009] Step S3: Correction of node observation anomalies;

[0010] Step S4: Generation of anti-disturbance control parameters;

[0011] Step S5: Environmental disturbance immunity control.

[0012] Furthermore, in step S1, the distributed electronic countermeasures node synchronization is used to eliminate time drift and state deviation between electronic countermeasures nodes under a unified time reference, ensuring that the operating status of each node is comparable and consistent within the same working cycle. Specifically, it involves collecting the local time information, working status information and transmission and reception parameter information of each electronic countermeasures node, aligning the time information of each node based on the airborne unified time source, and performing consistency verification processing on the node operating status to obtain distributed node operating status alignment data.

[0013] The distributed node operating status alignment data specifically includes: node time alignment identifier data, node local oscillator lock status data, node transmission operating parameter data, node reception link status data, and node operating health status data.

[0014] Further, in step S2, the environmental abnormal disturbance determination is used to identify whether there is abnormal disturbance in the current electromagnetic environment and to distinguish the type of disturbance source, so as to avoid misjudging local mutual interference or node abnormality as external suppression interference. Specifically, based on the distributed node operation status alignment data, the changes in the received signal of each node are correlated and compared with the node's own transmission status, and combined with the node's directional reception difference information and node operation health status data, the abnormal changes are classified by source to obtain electromagnetic environment disturbance determination data.

[0015] The determination of abnormal environmental disturbances specifically employs a multi-evidence consistent attribution method, including the following steps:

[0016] Step S21: Observation feature normalization processing, used to normalize the changes in the received signals of each electronic countermeasure node under a unified time reference, and eliminate the influence of sampling differences on the judgment results. Specifically, within a preset time window, the received power change, frequency band occupancy change and spectrum change of each node are extracted, and node received change feature data are generated. At the same time, the corresponding node's transmission frequency, transmission power, duty cycle, received gain status and limiting status information are extracted to form node operation related feature data.

[0017] Step S22: Anomaly candidate detection, used to determine whether there is an abnormal disturbance within the current time window. Specifically, it involves aggregating the change feature data received by each node, calculating the overall degree of abnormal change, and comparing it with a preset baseline threshold. When the overall degree of abnormal change exceeds the preset threshold, disturbance presence flag data is generated, and disturbance intensity level data is divided according to the degree of abnormal change.

[0018] Step S23: Plan consistency analysis, used to determine whether abnormal changes are related to the local transmission behavior. Specifically, the receiving change characteristic data of each node is compared with the transmission working parameters in the corresponding time window. When the abnormal frequency band overlaps with the transmission frequency, and the degree of receiving change is synchronous with the change in transmission power or duty cycle, plan consistency evidence data is generated.

[0019] Step S24: Spatial consistency analysis, used to determine whether abnormal changes have a consistent trend among multiple nodes. Specifically, it compares the degree of change received by multiple nodes. When multiple nodes show a synchronous upward or downward trend within the same time window, spatial consistency evidence data is generated.

[0020] Step S25: Health consistency analysis, used to determine whether abnormal changes are caused by abnormal operation of the node itself. Specifically, it is to make a comprehensive judgment on the node's local oscillator lock-in status, receive link limiting status, overflow status, as well as temperature, current and communication link quality status. When abnormal changes are concentrated on nodes with abnormal operating status, health consistency evidence data is generated.

[0021] Step S26: Multi-evidence attribution determination, used to classify disturbance sources based on plan consistency evidence data, spatial consistency evidence data, and health consistency evidence data. Specifically, the plan consistency evidence data, spatial consistency evidence data, and health consistency evidence data are comprehensively compared according to preset weights to generate disturbance source type data, including external abnormal radiation sources, local mutual interference sources, and node operation abnormal sources.

[0022] Step S27: Disturbance frequency band distribution generation, used to identify the main concentrated frequency bands of abnormal disturbances. Specifically, it involves summarizing the received change frequency bands of each node, generating disturbance frequency band distribution data, and summarizing it into electromagnetic environment disturbance judgment data.

[0023] The electromagnetic environment disturbance determination data specifically includes: disturbance presence indicator data, disturbance intensity level data, disturbance source type data, and disturbance frequency band distribution data.

[0024] Furthermore, in step S3, the node observation anomaly correction is used to eliminate the distortion effect caused by strong interference or link anomalies on the received observation results and improve the authenticity and stability of the observation data. Specifically, based on the electromagnetic environment disturbance judgment data, state correlation analysis is performed on the received data of nodes with abnormal disturbances. Combined with the node gain state information and signal limiting state information, the affected observation results are corrected to obtain the effective observation data of the nodes.

[0025] The node observation anomaly correction adopts an improved method of observation reversible correction, including the following steps:

[0026] Step S31: Abnormal observation location, used to identify observation samples affected by gain compression, limiting or overflow. Specifically, within a unified time window, read the node receiving gain status information, limiting trigger flag and overflow flag, mark the time period with limiting or overflow, and obtain the data of the affected observation interval.

[0027] Step S32: Gain inversion processing, used to eliminate the influence of the receiving link gain on the observed power. Specifically, based on the preset gain calibration value corresponding to the current receiving gain level of the node, the observed received power data is inversely converted to obtain the equivalent input power data.

[0028] Step S33: Amplitude limiting compensation processing, used to reversibly correct the observation results that have been limited. Specifically, within the same time window, a reference observation sample that has not been limited is selected, the reference power statistics are calculated, and the recovery factor is determined based on the proportional relationship between the reference power statistics and the limited observation power. The limited observation data is then compensated and corrected to obtain the corrected received power data.

[0029] Step S34: Spectrum occupancy correction, used to restore spectrum energy distortion caused by gain compression or clipping. Specifically, gain inversion and clipping compensation are performed on the observed energy at each frequency point, and frequency band occupancy identifiers are regenerated according to the preset spectrum determination threshold to obtain corrected spectrum occupancy data.

[0030] Step S35: Credibility assessment, used to evaluate the reliability of the corrected observation results, specifically generating observation credibility level data based on the limiting triggering situation, gain change magnitude, node link quality status, and cross-node observation consistency.

[0031] Step S36: Effective interval generation, used to determine the observation time period and frequency band range that can be used for subsequent control decisions. Specifically, based on the overflow flag, the proportion of continuous amplitude limiting time and the observation confidence level, low confidence time periods or frequency bands are eliminated to generate effective observation interval data.

[0032] The effective observation data of the node specifically includes corrected received power data, corrected spectrum occupancy data, observation confidence level data, and observation effective interval data.

[0033] Further, in step S4, the disturbance rejection control parameter generation is used to generate appropriate disturbance rejection control parameters based on the current effective observation results and the operating status of each node, so as to ensure that each node implements cooperative disturbance rejection actions under the condition of meeting the operating constraints. Specifically, based on the effective observation data of the node, combined with the current working mode of each electronic countermeasure node and the equipment operating boundary conditions, the transmission power, transmission frequency, waveform parameters and working sequence are calculated and generated to obtain the node disturbance rejection control parameter data.

[0034] The disturbance rejection control parameters are generated using an improved collaborative control parameter generation method, characterized by the following steps:

[0035] Step S41: Available observation screening is used to filter out low-confidence observation data and determine the weight of each node participating in coordination before generating control parameters. Specifically, based on the observation confidence level data and observation effective interval data in the node's effective observation data, observation information that can be used for control decision is screened. At the same time, combined with the local oscillator locking status data, node operating health status data and communication link quality status data of each node, weight data for node participation in coordination is generated.

[0036] Step S42: Constructing the feasible control domain, which is used to limit the range of control parameters that each node can execute under the device's operating boundary conditions. Specifically, based on each node's current operating mode, power limit, frequency band permitted range, waveform configuration range, and transmit / receive time slot constraints, a set of feasible parameters for transmit power, transmit frequency, waveform parameters, and operating timing is constructed.

[0037] Step S43: Candidate parameter combination generation, used to generate candidate control combinations that meet the anti-interference requirements within the set of feasible parameters. Specifically, based on the information of the main disturbed frequency bands and the available observation results, candidate transmit power, frequency, waveform and operating timing combinations that avoid the disturbed frequency bands and meet the operating constraints are selected from the set of feasible parameters of each node, and a set of candidate control parameters is generated.

[0038] Step S44: Multi-node consistency determination, used to determine whether multi-node collaborative execution is required and the scope of collaborative nodes. Specifically, based on the conflict relationship between node weight data and candidate control parameter combinations, the consistency of each candidate combination is compared. When the candidate combination meets the preset consistency conditions, a collaborative node set is generated. When there is a conflict, priority filtering or reduction of the collaborative scope is performed according to the node weight, and collaborative control identification data is generated.

[0039] Step S45: Control parameter encapsulation output, used to form control parameter data that can be directly issued and executed, specifically, generating node anti-disturbance control parameter data based on the final determined combination of control parameters;

[0040] The node anti-disturbance control parameter data specifically includes: transmit power adjustment parameter data, transmit frequency adjustment parameter data, waveform parameter adjustment data, working timing adjustment data, and cooperative control identification data.

[0041] Further, in step S5, the environmental anti-disturbance control is used to control each electronic countermeasures node to execute anti-disturbance control parameters and to verify the consistency of the execution results and the anti-disturbance effect of each node. Specifically, the node anti-disturbance control parameter data is sent to the corresponding node for execution, and the node's operating status and received signal changes after execution are sampled and verified for consistency to obtain node anti-disturbance execution status data.

[0042] The node anti-disturbance execution status data specifically includes: actual execution parameter feedback data, execution consistency verification result data, anti-disturbance effect change data, and execution anomaly flag data; wherein, the actual execution parameter feedback data is used to characterize the actual transmission parameters after node execution; the execution consistency verification result data is used to characterize whether there is an execution deviation between nodes; the anti-disturbance effect change data is used to characterize the change in the electromagnetic environment after control execution; and the execution anomaly flag data is used to characterize whether there are parameters that are not effective or execution failures.

[0043] The beneficial effects achieved by the present invention using the above solution are as follows:

[0044] (1) In view of the problems in the existing electromagnetic environment anti-disturbance control methods of airborne electronic countermeasures systems, there are distorted observation results of each distributed electronic countermeasures node under strong electromagnetic countermeasures environment, difficulty in accurately distinguishing the source of disturbance, and lack of closed-loop verification mechanism for anti-disturbance control, resulting in insufficient targeting of anti-disturbance control parameter generation and unverifiable control execution effect. This scheme creatively adopts a closed-loop anti-disturbance control method based on distributed node operation status alignment data, electromagnetic environment disturbance judgment data, node effective observation data and node anti-disturbance execution status data. Through node synchronization, disturbance attribution judgment, observation anomaly correction, collaborative control parameter generation and execution status back sampling verification, a complete anti-disturbance control closed loop is formed, realizing observable, correctable, executable and verifiable management of the anti-disturbance control process;

[0045] (2) In view of the problem that the local time drift and operational status deviation of each node in the existing distributed electronic countermeasures node operation status management method are not directly comparable due to the local time drift and operational status deviation of each node, which affects the accuracy of disturbance identification, this scheme creatively adopts a distributed electronic countermeasures node synchronization method. The time of each node is aligned by an airborne unified time source, and the consistency of the node local oscillator lock status, transmission working parameters, receiving link status and operational health status is checked to generate distributed node operation status alignment data, so as to realize the same periodic consistency management of the observation and operation status of each node.

[0046] (3) In view of the problem that the existing electronic countermeasures node receiving observation data processing methods have the problem that the receiving link gain compression, limiting or overflow under strong interference conditions leads to the distortion of the observation power data, which makes the anti-disturbance control decision basis inaccurate, this scheme creatively adopts the observation reversible correction improvement method. Through abnormal observation location, gain inversion processing, limiting compensation processing, spectrum occupancy correction and credibility assessment, the effective observation data of the node is generated, realizing the recovery of the interference-distorted observation results and the selection of effective intervals;

[0047] (4) In view of the problem that the existing electronic countermeasures system anti-disturbance control execution method lacks the consistency verification of execution status and anti-disturbance effect feedback mechanism after the control parameters are issued, resulting in the inability to detect and correct control execution deviations in a timely manner, this solution creatively adopts the environmental anti-disturbance control method. Through the issuance of control parameters, execution confirmation, actual execution parameter feedback comparison and anti-disturbance effect change analysis, node anti-disturbance execution status data is generated to realize the closed-loop verification and effect evaluation of anti-disturbance control execution status. Attached Figure Description

[0048] Figure 1 A flowchart illustrating an electromagnetic environment disturbance rejection control method for an airborne distributed electronic countermeasures system provided by the present invention;

[0049] Figure 2 This is a flowchart illustrating the process of determining environmental anomalies in step S2.

[0050] Figure 3 This is a flowchart illustrating the process of correcting anomalies observed at node S3.

[0051] Figure 4 A schematic diagram of the process for generating anti-disturbance control parameters in step S4.

[0052] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0053] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0054] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0055] Example 1, see Figure 1 The present invention provides an electromagnetic environment disturbance immunity control method for an airborne distributed electronic countermeasures system, the method comprising the following steps:

[0056] Step S1: Distributed electronic countermeasures node synchronization;

[0057] Step S2: Determine abnormal environmental disturbances;

[0058] Step S3: Correction of node observation anomalies;

[0059] Step S4: Generation of anti-disturbance control parameters;

[0060] Step S5: Environmental disturbance immunity control.

[0061] By performing the above operations, this solution addresses the problems in existing electromagnetic environment disturbance rejection control methods for airborne electronic countermeasures systems. These problems include distorted observation results of distributed electronic countermeasures nodes under strong electromagnetic countermeasures environments, difficulty in accurately distinguishing disturbance sources, and a lack of closed-loop verification mechanisms for disturbance rejection control. Consequently, the generation of disturbance rejection control parameters is not targeted enough, and the control execution effect cannot be verified. This solution creatively adopts a closed-loop disturbance rejection control method based on distributed node operating status alignment data, electromagnetic environment disturbance judgment data, effective node observation data, and node disturbance rejection execution status data. Through node synchronization, disturbance attribution judgment, observation anomaly correction, collaborative control parameter generation, and execution status feedback verification, a complete disturbance rejection control closed loop is formed, achieving observable, correctable, executable, and verifiable management of the disturbance rejection control process.

[0062] Specifically, for example, when multiple electronic countermeasures nodes simultaneously perform suppression jamming tasks, due to the influence of strong external suppression signals or local mutual interference, the receiving links of some nodes may be limited or compressed, causing distortion of the received power data. Traditional methods directly generate frequency avoidance parameters based on distorted observations, which may mistakenly identify local mutual interference as external interference, thus incorrectly switching frequencies and causing cooperative jamming failure. In contrast, this solution achieves node state alignment through a unified time base, distinguishes the source of disturbance based on multi-evidence attribution, restores the true observation state through observation anomaly correction, and finally generates targeted anti-disturbance control parameters. The effect is verified by the execution state re-acquisition, thereby ensuring that the control parameters match the real electromagnetic environment and improving the accuracy and reliability of anti-disturbance control.

[0063] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, the distributed electronic countermeasures node synchronization is used to eliminate time drift and state deviation between electronic countermeasures nodes under a unified time reference, and to ensure that the operating status of each node is comparable and consistent within the same working cycle. Specifically, it collects the local time information, working status information and transmission and reception parameter information of each electronic countermeasures node, aligns the time information of each node based on the airborne unified time source, and performs consistency verification on the node operating status to obtain distributed node operating status alignment data.

[0064] The distributed node operating status alignment data specifically includes: node time alignment identifier data, node local oscillator lock status data, node transmission operating parameter data, node receiving link status data, and node operating health status data. Specifically, the node time alignment identifier data characterizes the synchronization status of each node with the airborne unified time source; the node local oscillator lock status data characterizes whether the local oscillator is locked and the level of lock stability; the node transmission operating parameter data characterizes the current transmission frequency, transmission power, and duty cycle parameters; the node receiving link status data characterizes the receiving gain level, amplitude limiting trigger flag, and overflow flag; and the node operating health status data characterizes the temperature, current, and communication link quality status.

[0065] In this embodiment, the airborne distributed electronic countermeasures system includes multiple electronic countermeasures nodes, which are distributed and installed at different locations on the aircraft and are communicatively connected to the main control unit via an airborne data bus. Each electronic countermeasures node includes at least a receiving unit, a transmitting unit, a local oscillator unit, a control processing unit, a status monitoring unit, and a communication interface unit. The receiving unit is used to receive external electromagnetic signals and output received signal power data; the transmitting unit is used to perform electronic jamming or countermeasures signal transmission; the local oscillator unit is used to provide a frequency reference; the control processing unit is used to perform parameter control and status management; the status monitoring unit is used to collect temperature, current, and equipment operating status; and the communication interface unit is used to interact with the main control unit.

[0066] The distributed electronic warfare node synchronization is used to eliminate time drift and state deviation between electronic warfare nodes under a unified time reference, ensuring that the operating status of each node is comparable and consistent within the same working cycle. The airborne system is equipped with a unified time source, which can be an airborne master clock module, a navigation time source, or a dedicated time synchronization module, and broadcasts time synchronization frames to each electronic warfare node at a fixed period. Within each synchronization cycle, each electronic warfare node reads its local time count value and generates a local timestamp, while simultaneously collecting the local oscillator lock status, current transmission parameters, current receive link status, and operational health status, and uploads the above data to the main control unit.

[0067] After receiving the data uploaded by each node, the main control unit calculates the time deviation between the local time of each node and the unified time source. When the time deviation exceeds a preset threshold, a time correction command is sent to the corresponding node. The node adjusts its local time counting reference according to the time correction command. After completing the time correction, the main control unit performs a consistency check on the operating status of each node, including checking whether each node is in the same working mode, whether the transmission frequency is within the allowed frequency band, whether the transmission power exceeds the safety boundary, whether the receiving link is in a saturated state, and whether the communication link quality meets the reliable transmission requirements. When an abnormality in the node's operating status is detected, a status abnormality flag is generated and the abnormality type is recorded.

[0068] After completing time alignment and consistency verification, distributed node operating status alignment data is generated. This data specifically includes: node time alignment identifier data, node local oscillator lock status data, node transmit operating parameter data, node receive link status data, and node operating health status data. Specifically, the node time alignment identifier data indicates whether the node has completed time alignment and the current time deviation; the node local oscillator lock status data indicates whether the local oscillator is locked and the lock stability level; the node transmit operating parameter data indicates the current transmit frequency, transmit power, duty cycle, and operating mode; the node receive link status data indicates the receive gain level, limiting trigger flag, overflow flag, and current signal strength; and the node operating health status data indicates the node's temperature, current, voltage, and communication link quality level.

[0069] In this embodiment, the distributed node operational status alignment data is stored in the form of structured data frames. Each electronic countermeasures node corresponds to one data record, which includes node number, timestamp, synchronization status flag, local oscillator status information, transmission parameter information, receiver link status information, and health status information. The data records are arranged in order of node number to form a data set. Through the above synchronization and consistency verification process, the status alignment of each electronic countermeasures node under a unified time reference is achieved, providing a consistent and reliable operational status basis for subsequent electromagnetic environment disturbance judgment and anti-disturbance control.

[0070] Example 3, see Figure 1 , Figure 2 This embodiment is based on the above embodiment. In step S2, the environmental abnormal disturbance determination is used to identify whether there is abnormal disturbance in the current electromagnetic environment and to distinguish the type of disturbance source, so as to avoid misjudging local mutual interference or node abnormality as external suppression interference. Specifically, based on the distributed node operation status alignment data, the changes in the received signal of each node are correlated and compared with the node's own transmission status. Combined with the node direction reception difference information and node operation health status data, the abnormal changes are classified by source to obtain electromagnetic environment disturbance determination data.

[0071] Preferably, the node directional reception difference information is calculated based on the node installation orientation and the directional reception gain difference;

[0072] The determination of abnormal environmental disturbances specifically employs a multi-evidence consistent attribution method, including the following steps:

[0073] Step S21: Observation feature normalization processing, used to normalize the changes in the received signals of each electronic countermeasure node under a unified time reference, and eliminate the influence of sampling differences on the judgment results. Specifically, within a preset time window, the received power change, frequency band occupancy change and spectrum change of each node are extracted, and node received change feature data are generated. At the same time, the corresponding node's transmission frequency, transmission power, duty cycle, received gain status and limiting status information are extracted to form node operation related feature data.

[0074] In this embodiment, the preset time window is preferably set to 5ms to 50ms, and preferably 10ms in the scenario of rapid suppression of disturbances; the change in received power can be obtained by the difference between the average received power of the current window and the average received power of the previous window; the change in frequency band occupancy can be obtained by binarizing and statistically analyzing whether the energy of each frequency point in the current window exceeds a preset threshold; and the change in spectral shape can be characterized by at least one of the following: spectral centroid shift, change in the number of main peaks, or change in spectral width.

[0075] Furthermore, in order to reduce the impact of instantaneous impulse noise on subsequent attribution determination, median filtering or exponential moving average processing can be performed on the received power change and spectral change, and the transmit frequency, transmit power, duty cycle, receive gain status and limiting status can be constructed into a node operation associated feature vector according to a unified field order to ensure that the input structure of each node is consistent.

[0076] Step S22: Anomaly candidate detection, used to determine whether there is an abnormal disturbance within the current time window. Specifically, it involves aggregating the change feature data received by each node, calculating the overall degree of abnormal change, and comparing it with a preset baseline threshold. When the overall degree of abnormal change exceeds the preset threshold, disturbance presence flag data is generated, and disturbance intensity level data is divided according to the degree of abnormal change.

[0077] In this embodiment, the overall degree of abnormal change is preferably characterized by a coupled anomaly index of three types of observed change characteristics, and its calculation formula is as follows:

[0078] ;

[0079] In the formula, It represents the overall degree of abnormal change within the current time window, where N is the number of electronic countermeasure nodes participating in the judgment. It is the change in received power at the i-th node. It represents the change in frequency band occupancy at the i-th node. It is the spectral change of the i-th node, where i is the node index;

[0080] Using the above approach, the anomalous response can be amplified when changes in received power, frequency band occupancy, and spectral shape are simultaneously enhanced, thereby improving the detection sensitivity to complex anomalous disturbances. When only a single feature among the three types of changes fluctuates slightly, the overall degree of anomalous change increases more slowly, which is beneficial for suppressing the interference of occasional local noise on the detection of anomalous candidates.

[0081] The frequency band occupancy change is preferably calculated by comparing the energy of each frequency point in the current time window with the adaptive threshold to obtain the difference between the occupancy frequency point ratio and the occupancy ratio in the previous time window; the spectral shape change is preferably represented by a normalized combination of the spectral centroid shift and the spectral width change, wherein the spectral centroid can be obtained by weighted average of the energy of each frequency point, and the spectral width can be determined by the energy distribution variance.

[0082] The preset baseline threshold can be determined based on the statistical results of multiple time windows under normal steady-state operating conditions, preferably by adaptively generating the mean of normal operating conditions plus twice the standard deviation; in some embodiments, the normalization threshold can also be set to 0.42; when When the threshold is exceeded, the current window is determined to have a candidate for abnormal disturbance; when When the intensity exceeds the second or third intensity threshold, it can be marked as a medium-intensity perturbation or a high-intensity perturbation, respectively.

[0083] Step S23: Plan consistency analysis, used to determine whether abnormal changes are related to the local transmission behavior. Specifically, the receiving change characteristic data of each node is compared with the transmission working parameters in the corresponding time window. When the abnormal frequency band overlaps with the transmission frequency, and the degree of receiving change is synchronous with the change in transmission power or duty cycle, plan consistency evidence data is generated.

[0084] In this embodiment, the plan consistency evidence data can be jointly constructed by the overlap ratio between abnormal frequency bands and transmission frequencies, the degree of synchronization correlation between changes in received signals and changes in transmission power, and the degree of synchronization correlation between changes in received signals and changes in duty cycle. Preferably, a geometric coupling method is used to obtain the plan consistency evidence score.

[0085] ;

[0086] In the formula, It is the score for evidence of plan consistency. It is the overlap ratio between the abnormal frequency band and the transmission frequency. It refers to the degree of synchronous correlation between changes in received power and changes in transmitted power. It is the degree of synchronous correlation between the change in received data and the change in duty cycle;

[0087] The degree of synchronization between the change in received power and the change in transmitted power, and the degree of synchronization between the change in duty cycle, are preferably calculated by a normalized cross-correlation function within a sliding time window, and the maximum correlation value is taken as the synchronization index.

[0088] By adopting the above approach, the three pieces of evidence—abnormal frequency band overlap, power synchronization, and duty cycle synchronization—can jointly constrain plan consistency. When any one of these pieces of evidence is weak, the overall score will be suppressed, thereby avoiding misjudgment of local interference sources based solely on a high score for a single piece of evidence. When a node is in silent reception mode, test standby mode, or has not performed an effective transmission task within the current time window, the plan consistency evidence score can be suppressed to prevent invalid transmission plans from misleading the attribution of local interference.

[0089] Step S24: Spatial consistency analysis, used to determine whether abnormal changes have a consistent trend among multiple nodes. Specifically, it compares the degree of change received by multiple nodes. When multiple nodes show a synchronous upward or downward trend within the same time window, spatial consistency evidence data is generated.

[0090] In this embodiment, the spatial consistency evidence data is preferably constructed based on the pairwise correlation of the received change sequences between nodes, and the spatial consistency evidence score can be calculated using the following formula:

[0091] ;

[0092] In the formula, It is the score of evidence of spatial consistency. This represents the correlation between the i-th node and the j-th node in receiving the changed sequence within the current time window, where j is the index of the second node, and N is the number of nodes participating in the consistency analysis; in one implementation, Pearson correlation coefficient can be used for calculation; when there are large differences in node directionality, a direction compensation factor can be introduced in combination with the differences in node installation orientation and directional receiving gain to reduce the impact of node orientation differences on the overall consistency identification of external anomalous disturbances; furthermore, the local consistency of adjacent nodes and the global consistency of all nodes can be calculated separately. When the local consistency is high but the global consistency is insufficient, it can be judged as a local spatial coverage disturbance; when multiple nodes in different orientations show high consistency at the same time, it is more likely to be an external anomalous radiation source.

[0093] Step S25: Health consistency analysis, used to determine whether abnormal changes are caused by abnormal operation of the node itself. Specifically, it is to make a comprehensive judgment on the node's local oscillator lock-in status, receive link limiting status, overflow status, as well as temperature, current and communication link quality status. When abnormal changes are concentrated on nodes with abnormal operating status, health consistency evidence data is generated.

[0094] In this embodiment, the health consistency analysis preferably uses a hierarchical health anomaly rule for processing. Specifically, the local oscillator lockout state is prioritized as a high-priority health anomaly marker. When the receive link limiting state, overflow state, and temperature or current anomalies occur simultaneously, the node self-anomaly judgment weight is increased. When abnormal changes only occur consistently and concentratedly in a single node, and that node exhibits health anomalies for multiple consecutive time windows, candidate markers for node operational anomaly sources are generated first. When the health states of multiple nodes are all within the normal range, the health consistency evidence is weakened to highlight the credibility of the environmental source disturbance judgment.

[0095] Step S26: Multi-evidence attribution determination, used to classify disturbance sources based on plan consistency evidence data, spatial consistency evidence data, and health consistency evidence data. Specifically, it comprehensively compares the plan consistency evidence data, spatial consistency evidence data, and health consistency evidence data according to preset weights. When spatial consistency evidence is dominant and plan consistency evidence is low, it is determined to be an external abnormal radiation source; when plan consistency evidence is dominant, it is determined to be a local mutual interference source; when health consistency evidence is dominant and the abnormality is concentrated in a single node, it is determined to be a node operation abnormality source; and disturbance source type data is generated.

[0096] In this embodiment, the multi-evidence attribution determination preferably adopts the dominance ratio attribution method, constructing dominance indicators for external anomaly radiation sources, local mutual interference sources, and node operational anomaly sources, specifically:

[0097] ;

[0098] In the formula, The dominance of external anomalous radiation sources. It is the dominant source of local interference. It is the dominance of the source of node malfunction. It is the score for evidence of plan consistency. It is the score of evidence of spatial consistency. It is the score of evidence of health consistency;

[0099] The health consistency evidence score is preferably obtained by comprehensively summarizing various abnormal operating states of equipment. Specifically, it is based on the local oscillator lock-in status, receiving link limiting or overflow triggering status, temperature and current operating status, and communication link quality status to uniformly characterize various abnormalities. When the corresponding status is within the normal range, it is recorded as low abnormality level. When an abnormality occurs or approaches the preset safety threshold, it is recorded as medium or high abnormality level.

[0100] Furthermore, the health consistency evidence data is preferably determined based on the superposition of multiple anomalies within the same time window and their distribution characteristics among nodes: when a single anomaly occurs sporadically, it is only considered as weak health anomaly evidence; when multiple anomalies occur simultaneously or the same anomaly persists in multiple consecutive time windows, the strength of health anomaly evidence is increased; when the anomaly is mainly concentrated in a single node and the node's operating status continuously deviates from the normal range, the node is determined to have significant health anomaly characteristics.

[0101] Using the above method, the dominance of a certain type of evidence relative to the others can be directly characterized; when spatial consistency evidence is significantly superior to planning consistency evidence and health consistency evidence, An increase in radiation levels corresponds to an external source of abnormal radiation; when evidence of plan consistency prevails... An increase in this value corresponds to the identification of a source of local interference; when evidence of health consistency dominates and anomalies are concentrated in a single node... An increase in this value indicates an abnormality in the node's operation.

[0102] Finally, the source type with the highest dominance is determined as the source type of the current abnormal disturbance; when the difference between the highest dominance and the second highest dominance is less than the preset difference threshold, the current window can be marked as an attribution uncertainty state, and evidence can continue to be accumulated in subsequent time windows before the final judgment is made.

[0103] Step S27: Disturbance frequency band distribution generation, used to identify the main concentrated frequency bands of abnormal disturbances. Specifically, it involves summarizing the received change frequency bands of each node, generating disturbance frequency band distribution data, and summarizing it into electromagnetic environment disturbance judgment data.

[0104] Preferably, the abnormal frequency band intervals identified by each node are merged, and the frequency band priority is calculated based on the frequency band occurrence frequency, average abnormal intensity, and number of duration windows. Abnormal frequency bands that repeatedly occur in multiple consecutive time windows can be marked as stable disturbed frequency bands. For discrete frequency points that occur only once for a short period and have low intensity, suppression processing can be performed to reduce the impact of occasional noise frequency points on subsequent control decisions. The disturbance frequency band distribution data may further include the start and end range of the disturbance frequency band, center frequency, band width, occupancy rate, average abnormal intensity, and duration level, providing frequency band-level input for subsequent node observation anomaly correction and anti-disturbance control parameter generation.

[0105] The electromagnetic environment disturbance determination data specifically includes: disturbance presence indicator data, disturbance intensity level data, disturbance source type data, and disturbance frequency band distribution data; wherein, the disturbance presence indicator data is used to characterize whether there is an abnormal electromagnetic disturbance; the disturbance intensity level data is used to characterize the classification result of the disturbance intensity; the disturbance source type data is used to distinguish between external suppression disturbances, mutual interference between local equipment, or abnormal node disturbances; and the disturbance frequency band distribution data is used to characterize the main frequency bands where the disturbance is concentrated.

[0106] By performing the above operations, this solution addresses the problem in existing distributed electronic warfare node operation status management methods where local time drift and operational status deviations of each node prevent direct comparison of observation data, thus affecting the accuracy of disturbance identification. This solution creatively adopts a distributed electronic warfare node synchronization method, which aligns the time of each node using an airborne unified time source and performs consistency checks on the node's local oscillator lock status, transmission operating parameters, receiver link status, and operational health status. This generates distributed node operation status alignment data, achieving consistent management of the observation and operation status of each node within the same period.

[0107] Specifically, for example, due to the drift of the local clock, the electronic countermeasures nodes on both sides of the aircraft have a microsecond-level time deviation. When an external suppression signal appears instantaneously, the disturbance time recorded by some nodes is earlier or later than that of other nodes. Traditional methods may misjudge this as a local node anomaly. However, this solution, through unified time synchronization and state consistency verification, enables each node to record the received changes under the same time reference, so that disturbance identification is based on a consistent time basis, thereby improving the reliability of electromagnetic environment disturbance judgment results.

[0108] Example 4, see Figure 1 , Figure 3 This embodiment is based on the above embodiment. In step S3, the node observation anomaly correction is used to eliminate the distortion effect caused by strong interference or link anomaly on the received observation results and improve the authenticity and stability of the observation data. Specifically, based on the electromagnetic environment disturbance judgment data, state correlation analysis is performed on the received data of nodes with abnormal disturbances. Combined with the node gain state information and signal limiting state information, the affected observation results are corrected to obtain the effective observation data of the nodes.

[0109] The node observation anomaly correction adopts an improved method of observation reversible correction, including the following steps:

[0110] Step S31: Abnormal observation location, used to identify observation samples affected by gain compression, limiting or overflow. Specifically, within a unified time window, read the node receiving gain status information, limiting trigger flag and overflow flag, mark the time period with limiting or overflow, and obtain the data of the affected observation interval.

[0111] In this embodiment, the unified time window is preferably set to 2ms to 20ms, and preferably 5ms in high-dynamic electronic warfare scenarios; when the limiting trigger flag appears within at least two consecutive sampling periods, the corresponding sample segment is marked as the limiting affected interval; when the overflow flag appears, the corresponding sample segment is directly marked as the high-risk distortion interval; when the change in the receiving gain level between adjacent time windows exceeds the preset level threshold, the transition sample segment of the preset length before and after the level switch is marked as the gain unstable interval.

[0112] Furthermore, the data of the affected observation interval preferably includes the start time, end time, duration, anomaly type identifier, and corresponding frequency band range of the abnormal interval, so as to perform subsequent gain inversion and amplitude limiting compensation processing according to type;

[0113] Step S32: Gain inversion processing, used to eliminate the influence of the receiving link gain on the observed power. Specifically, based on the preset gain calibration value corresponding to the current receiving gain level of the node, the observed received power data is inversely converted to obtain the equivalent input power data.

[0114] In this embodiment, the equivalent input power is preferably calculated using the following formula:

[0115] ;

[0116] In the formula, It is the equivalent input power at time t. It is the observed received power at time t. It is the gain calibration value of the receiving gain level corresponding to time t;

[0117] In a preferred embodiment, the receiving gain levels can be set to 8 or 16 levels, each level corresponding to a pre-calibrated gain value, and the calibration error between levels is preferably controlled within ±0.5dB; the gain calibration value can be derived from the equipment's factory calibration table or an in-machine self-calibration table. When the node temperature exceeds a preset temperature drift threshold, a temperature correction term can be added to the gain calibration value to improve the inversion accuracy under high-temperature conditions; for samples in the transient range of gain switching, they can be marked as transitional candidate samples, and their subsequent confidence level can be reduced to avoid the propagation of inversion errors caused by level jitter;

[0118] Step S33: Amplitude limiting compensation processing, used to reversibly correct the observation results that have been limited. Specifically, within the same time window, a reference observation sample that has not been limited is selected, the reference power statistics are calculated, and the recovery factor is determined based on the proportional relationship between the reference power statistics and the limited observation power. The limited observation data is then compensated and corrected to obtain the corrected received power data.

[0119] In this embodiment, to reflect the reversible correction process of the observation, the corrected received power can preferably be determined by the following formula:

[0120] ;

[0121] In the formula, It is the corrected received power at time t. It is the limited observation power at time t. It is the statistical power value of the reference observation sample that did not experience amplitude limiting within the same time window. It is a recovery factor;

[0122] Preferably, the recovery factor The recovery factor can be determined jointly based on the proportion of the clipping duration, the stability of the gain, and the consistency of observations across nodes in the same frequency band. The preferred value range is 0.4 to 0.9. When the clipping duration is short, the gain level is stable, and the observation trends of neighboring nodes in the corresponding frequency band are consistent, the recovery factor should be increased. When the clipping duration is long, the link status fluctuates significantly, or the consistency across nodes is low, the recovery factor should be decreased.

[0123] Furthermore, the reference observation samples are preferably selected from adjacent unlimited samples before and after the same node, and a supplementary reference set can be constructed by combining observations from neighboring nodes in the same frequency band; the reference power statistics are preferably calculated using at least one of the median, truncated mean, or quantile mean to suppress the influence of abnormal peak values ​​on the recovery results. For observation intervals that simultaneously exhibit overflow and limiting, only low-confidence correction values ​​can be output, or if the duration exceeds a preset upper limit, it can be directly marked as an interval that cannot be fully recovered;

[0124] Step S34: Spectrum occupancy correction, used to restore spectrum energy distortion caused by gain compression or clipping. Specifically, gain inversion and clipping compensation are performed on the observed energy at each frequency point, and frequency band occupancy identifiers are regenerated according to the preset spectrum determination threshold to obtain corrected spectrum occupancy data.

[0125] In this embodiment, it is preferable to first perform gain inversion processing in step S32 on the observed energy of each frequency point, and then perform amplitude limiting compensation processing in step S33 on the frequency point energy falling into the amplitude limiting affected interval to obtain the corrected frequency point energy distribution; then compare the corrected frequency point energy with the spectrum determination threshold, generate an occupancy mark for the frequency points exceeding the threshold, and merge no less than a preset number of consecutive occupancy frequency points to form the corrected frequency band occupancy interval;

[0126] The spectrum determination threshold can be adaptively generated based on the current noise floor estimate, preferably determined by adding 6dB to the noise floor; for discrete occupancy phenomena where only a single frequency point exceeds the threshold for a short time, isolated frequency point suppression can be performed to reduce the impact of random noise or transient spikes on the spectrum occupancy results;

[0127] Step S35: Credibility assessment, used to evaluate the reliability of the corrected observation results, specifically generating observation credibility level data based on the limiting triggering situation, gain change magnitude, node link quality status, and cross-node observation consistency.

[0128] In this embodiment, the observation confidence score is preferably calculated using a multiplicative confidence model:

[0129] ;

[0130] In the formula, It is an observation reliability score. The percentage of samples with amplitude limits within the current time window. It is the normalized gain variation. It is a link quality score. It is a cross-node observation consistency index;

[0131] The cross-node observation consistency index is preferably calculated by the average correlation coefficient of the received change sequences of the current node and its neighboring nodes in the same frequency band; the link quality score is preferably quantified by constructing a graded scoring table based on the communication bit error rate, packet loss rate and link delay jitter.

[0132] By adopting the above approach, the clipping ratio, gain stability, link quality, and cross-node consistency can jointly constrain the observation reliability. When any one of these indicators decreases significantly, the overall reliability score will decrease accordingly, thus better meeting the reliability assessment requirements after anomaly observation correction.

[0133] Furthermore, the revised results can be categorized into three levels—high credibility, medium credibility, and low credibility—based on credibility scores; for example, when The time is marked as highly trustworthy when The time marker is considered medium trustworthy when The time stamp is marked as low trust;

[0134] In some implementations, a lightweight multilayer perceptron model can be used to nonlinearly fuse the limiting ratio, gain variation, link quality score, and cross-node consistency index. The multilayer perceptron is preferably a two-layer fully connected structure, and the number of hidden layer neurons is preferably 16 or 32, in order to further improve the reliability assessment stability under complex distortion scenarios. However, in general implementation scenarios, the above-mentioned weighted scoring method is preferred to balance interpretability and engineering feasibility.

[0135] Step S36: Effective interval generation, used to determine the observation time period and frequency band range that can be used for subsequent control decisions. Specifically, based on the overflow flag, the proportion of continuous amplitude limiting time and the observation confidence level, low confidence time periods or frequency bands are eliminated to generate effective observation interval data.

[0136] In this embodiment, when the observation confidence level is high, the corresponding time period or frequency band can be directly included in the effective range; when the observation confidence level is medium, the retention can be determined by further considering the disturbance source type and cross-node frequency band consistency; when the observation confidence level is low, or when there is a continuous overflow state within the corresponding time window, it is removed from the subsequent control decision input; preferably, when the continuous limiting time ratio exceeds 60%, the corresponding time window is marked as a low availability range; when the confidence score is lower than 0.55, the corresponding observation result is removed; for frequency band level results, only the range with a continuous width exceeding the minimum effective bandwidth threshold can be retained, and adjacent narrowband ranges can be merged to improve the frequency band stability when generating subsequent control parameters;

[0137] The effective observation data of the node specifically includes corrected received power data, corrected spectrum occupancy data, observation confidence level data, and observation effective interval data; wherein, the corrected received power data is used to characterize the received signal strength after eliminating the effect of gain compression; the corrected spectrum occupancy data is used to characterize the corrected frequency band energy distribution; the observation confidence level data is used to characterize the reliability of the current observation data; and the observation effective interval data is used to characterize the effective frequency band or time period that can be used for control decision-making.

[0138] By performing the above operations, this solution addresses the problem in existing electronic countermeasures node receiving observation data processing methods that suffer from distortion of observation power data due to receiver link gain compression, limiting, or overflow under strong interference conditions, which leads to inaccurate anti-disturbance control decision-making. This solution creatively adopts an observation reversible correction improvement method, which generates effective node observation data through abnormal observation location, gain inversion processing, limiting compensation processing, spectrum occupancy correction, and reliability assessment, thereby restoring the distorted observation results caused by interference and filtering the effective interval.

[0139] Specifically, for example, when an electronic countermeasures node receives a strong suppression interference signal, the receiving link triggers the amplitude limiting protection, causing the measured received power to remain at a fixed upper limit. Traditional methods cannot determine the actual trend of interference intensity changes. However, this scheme restores the trend of equivalent input power changes by inverting and compensating the receiving gain state and amplitude limiting state, and filters out unreliable intervals through reliability assessment, so that the generation of anti-disturbance control parameters is based on real observations, thereby improving the adaptability of anti-disturbance control.

[0140] Example 5, see Figure 1 , Figure 4 This embodiment is based on the above embodiment. In step S4, the anti-disturbance control parameter generation is used to generate appropriate anti-disturbance control parameters based on the current effective observation results and the operating status of each node, so as to ensure that each node implements cooperative anti-disturbance actions under the condition of meeting the operating constraints. Specifically, based on the effective observation data of the node, combined with the current working mode of each electronic countermeasure node and the equipment operating boundary conditions, the transmission power, transmission frequency, waveform parameters and working sequence are calculated and generated to obtain the node anti-disturbance control parameter data.

[0141] The disturbance rejection control parameters are generated using an improved collaborative control parameter generation method, characterized by the following steps:

[0142] Step S41: Available observation screening is used to filter out low-confidence observation data and determine the weight of each node participating in coordination before generating control parameters. Specifically, based on the observation confidence level data and observation effective interval data in the node's effective observation data, observation information that can be used for control decision is screened. At the same time, combined with the local oscillator locking status data, node operating health status data and communication link quality status data of each node, weight data for node participation in coordination is generated.

[0143] In this embodiment, when the node observation confidence score is lower than the preset confidence threshold, the corresponding node can be directly excluded from the cooperative candidate node set; when the node's local oscillator is in a lost-lock state, its local oscillator locking state score is reduced to the lowest level; when the communication link quality is lower than the preset threshold, the node is only allowed to participate in local degradation control and not in global cooperative control.

[0144] Step S42: Constructing the feasible control domain, which is used to limit the range of control parameters that each node can execute under the device's operating boundary conditions. Specifically, based on each node's current operating mode, power limit, frequency band permitted range, waveform configuration range, and transmit / receive time slot constraints, a set of feasible parameters for transmit power, transmit frequency, waveform parameters, and operating timing is constructed.

[0145] In this embodiment, the control feasible domain is preferably constructed in layers according to different node operating modes. When a node is in high-power suppression mode, a higher transmit power range is opened and the receive protection time slot is limited. When a node is in receptacle compatibility mode, the upper limit of the transmit duty cycle and the continuous transmit duration are limited to retain the necessary receptacle window. When the node temperature approaches the safety threshold or the current margin is insufficient, its power upper limit is dynamically lowered. Furthermore, the frequency band permission range is limited not only by the hardware frequency synthesis capability but also by the task-disabled frequency band, the friendly system protection frequency band, and the avoidance constraints of the identified major interference frequency band. The waveform configuration range may include at least one of modulation method, pulse width, bandwidth, repetition period, and encoding method. The transmit / receive time slot constraints may include the local receive protection window, the predetermined cooperative task window, and the synchronization effective time requirement of the bus-issued control command. Through the above processing, a set of feasible parameters for transmit power, transmit frequency, waveform parameters, and operating timing corresponding to each node is formed.

[0146] Step S43: Candidate parameter combination generation, used to generate candidate control combinations that meet the anti-interference requirements within the set of feasible parameters. Specifically, based on the information of the main disturbed frequency bands and the available observation results, candidate transmit power, frequency, waveform and operating timing combinations that avoid the disturbed frequency bands and meet the operating constraints are selected from the set of feasible parameters of each node, and a set of candidate control parameters is generated.

[0147] In this embodiment, to improve the adaptability of candidate control combinations to the current interference environment, it is preferable to use a cost-effectiveness ratio to optimize the candidate parameter combinations for each node, which can be specifically expressed as follows:

[0148] ;

[0149] In the formula, It is the comprehensive optimization index of the m-th candidate control combination of the i-th node. It is an indicator of anti-interference effectiveness. It is an operational safety indicator. It is a metric indicating the degree of matching with the current task mode. It is the observation confidence score of the m-th candidate control combination at the i-th node. It controls the cost of switching.

[0150] Before participating in subsequent calculations, each of the aforementioned characteristic quantities is preferably normalized, wherein the change in received power and the change in gain can be linearly normalized based on historical statistical ranges.

[0151] By adopting the above approach, the priority of candidate combinations can be improved by enhancing the anti-disturbance benefits, operational safety, and mode matching degree. At the same time, by suppressing control combinations with excessive switching costs through the denominator term, the generated candidate control parameter set can not only have good anti-disturbance capabilities, but also avoid affecting the stable execution of the system due to excessive frequency span, excessive waveform adjustment, or excessive time slot disturbance.

[0152] In this embodiment, the anti-interference effectiveness index is preferably determined based on the minimum distance between the candidate frequency and the main affected frequency band, the waveform's ability to avoid interference types, and the effective observation coverage ratio; the operational safety index is preferably constructed based on the ratio of the node's current transmit power to the rated upper limit, temperature margin, and current margin; the task matching index is preferably discretely scored based on the degree of matching between the candidate control combination and the current task mode in terms of waveform type, timing structure, and frequency planning; and the control switching cost is preferably calculated using normalization based on the frequency switching span, waveform switching complexity, and the degree of disturbance to the existing time slot arrangement.

[0153] Furthermore, all of the above indicators are preferably normalized to the [0,1] interval to ensure the comparability and stability of the cost-effectiveness ratio calculation results;

[0154] Furthermore, in some implementations, the top few candidate control combinations with the highest comprehensive optimization index can be generated by discrete enumeration combined with heuristic greedy screening, and these combinations can be used as the candidate control parameter set. When there are too many candidate combinations, only the combinations with the comprehensive optimization index in the top 30% or top 20% can be retained to proceed to the next consistency determination.

[0155] Step S44: Multi-node consistency determination, used to determine whether multi-node collaborative execution is required and the scope of collaborative nodes. Specifically, based on the conflict relationship between node weight data and candidate control parameter combinations, the consistency of each candidate combination is compared. When the candidate combination meets the preset consistency conditions, a collaborative node set is generated. When there is a conflict, priority filtering or reduction of the collaborative scope is performed according to the node weight, and collaborative control identification data is generated.

[0156] In this embodiment, to quantitatively describe the degree of collaborative conflict among candidate combinations of multiple nodes, it is preferable to construct a conflict cost function between nodes:

[0157] ;

[0158] in, It is the conflict cost between the candidate control combinations of node i and node j. It refers to the degree of frequency conflict. It refers to the degree of conflict in work schedules. It refers to the degree of waveform incompatibility;

[0159] Using the above approach, the overall conflict level can be amplified when frequency conflict, time slot conflict, and waveform incompatibility occur simultaneously; when any one of the three increases significantly, the total conflict value can also be significantly increased, making it more suitable for characterizing the concurrent constraint characteristics in multi-node cooperative control.

[0160] when If the conflict threshold is not exceeded, the corresponding candidate combination can be considered to meet the coordination consistency condition. Furthermore, when there is a conflict between multiple candidate combinations of nodes, it is preferable to retain the node with the higher participation weight in coordination first. If there is still a conflict between high-weight nodes, the comprehensive selection index of their candidate combinations is further compared, and the combination with higher anti-disturbance benefits and better operational security is retained first. When the conflict cannot be completely resolved in the current round, the coordination scope can be reduced, only a local coordination node set is retained, and the nodes not included in the coordination set are transferred to the single-node independent anti-disturbance control mode.

[0161] The preferred collaborative control identification data includes: whether multi-node collaboration is enabled, a set of collaborative node numbers, node master / slave role identification or equal collaboration identification, effective time, and collaboration duration window length, which are used for the unified distribution and execution verification of subsequent control parameters;

[0162] Step S45: Control parameter encapsulation output, used to form control parameter data that can be directly issued and executed, specifically, generating node anti-disturbance control parameter data based on the final determined combination of control parameters;

[0163] In this embodiment, the node anti-disturbance control parameter data is preferably encapsulated in the form of structured control instruction frames. Each control instruction frame may include parameter version number, node number, effective time, transmit power adjustment parameter, transmit frequency adjustment parameter, waveform parameter, working timing parameter and cooperative control identifier field; in implementation scenarios that need to support abnormal rollback, failure time field, rollback identifier field or candidate backup parameter number field may also be added.

[0164] Furthermore, the transmit power adjustment parameter data may include the target power value, power adjustment step size, and maximum allowable duration; the transmit frequency adjustment parameter data may include the target frequency point, frequency hopping interval, frequency switching sequence, or disabled frequency band avoidance information; the waveform parameter adjustment data may include at least one of modulation method, pulse width, bandwidth, and pulse repetition period; the working timing adjustment data may include the start and end times of the transmit time slot, the receive protection window, and the cooperative activation time. Through the above encapsulation process, node anti-disturbance control parameter data that can be directly issued by the main control unit to each electronic countermeasure node for execution is formed;

[0165] The node anti-disturbance control parameter data specifically includes: transmit power adjustment parameter data, transmit frequency adjustment parameter data, waveform parameter adjustment data, operating timing adjustment data, and cooperative control identifier data; wherein, the transmit power adjustment parameter data is used to control the increase or decrease of transmit power; the transmit frequency adjustment parameter data is used to control frequency switching or frequency hopping range; the waveform parameter adjustment data is used to control the modulation method or pulse width; the operating timing adjustment data is used to control the time slot allocation for transmission and reception; and the cooperative control identifier data is used to indicate whether the control parameter requires multi-node cooperative execution.

[0166] By performing the above operations, this solution addresses the problem in existing electronic countermeasures system anti-disturbance control execution methods that lack consistency verification of execution status and feedback mechanisms for anti-disturbance effects after control parameters are issued, resulting in the inability to detect and correct control execution deviations in a timely manner. This solution creatively adopts an environmental anti-disturbance control method, which generates node anti-disturbance execution status data through control parameter issuance, execution confirmation, actual execution parameter feedback comparison, and anti-disturbance effect change analysis, thereby achieving closed-loop verification and effect evaluation of the anti-disturbance control execution status.

[0167] Specifically, for example, in the process of multi-node collaborative frequency hopping anti-interference, if a node fails to complete the frequency switching at the predetermined time due to hardware execution delay, traditional methods cannot detect the execution abnormality of the node in time. However, this solution can identify frequency execution deviation and generate execution abnormality flags by comparing and verifying the execution parameters. At the same time, it can analyze and judge the anti-disturbance effect by analyzing the changes in the electromagnetic environment after execution, thereby realizing closed-loop optimization of anti-disturbance control and improving the overall anti-interference capability and collaborative operation reliability of the system.

[0168] Example 6, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S5, the environmental anti-disturbance control is used to control each electronic countermeasure node to execute anti-disturbance control parameters and to verify the consistency of the execution results and the anti-disturbance effect of each node. Specifically, the node anti-disturbance control parameter data is sent to the corresponding node for execution, and the node's operating status and received signal changes after execution are sampled and verified for consistency to obtain node anti-disturbance execution status data.

[0169] The node anti-disturbance execution status data specifically includes: actual execution parameter feedback data, execution consistency verification result data, anti-disturbance effect change data, and execution anomaly flag data; wherein, the actual execution parameter feedback data is used to characterize the actual transmission parameters after node execution; the execution consistency verification result data is used to characterize whether there is an execution deviation between nodes; the anti-disturbance effect change data is used to characterize the change in the electromagnetic environment after control execution; and the execution anomaly flag data is used to characterize whether there are parameters that are not effective or execution failures.

[0170] In this embodiment, the environmental disturbance immunity control forms a closed-loop control process through control parameter issuance, execution confirmation, status retrieval, and consistency verification; the main control unit encapsulates the node disturbance immunity control parameter data generated in step S4 into a control command frame and sends it to each electronic countermeasure node. The control command frame includes fields such as parameter version number, effective time, transmit power adjustment parameter, transmit frequency adjustment parameter, waveform parameter, working timing parameter, and cooperative control identifier.

[0171] After receiving the control command frame, each node adjusts the parameters according to the effective time and returns execution confirmation information to the main control unit; if a node fails to return confirmation information within the preset time window, the main control unit generates execution exception flag data.

[0172] After the control parameters are executed, each electronic countermeasure node automatically reads the current actual transmission power, actual transmission frequency, actual waveform configuration status and current working timing status, and uploads the information as actual execution parameter feedback data to the main control unit;

[0173] The main control unit compares the actual execution parameter feedback data with the original issued control parameters. When the difference between the two exceeds the preset tolerance range, it generates execution anomaly flag data and records the anomaly type. When in collaborative control mode, the main control unit further verifies the consistency between the execution parameters of each node. If there are inconsistencies in transmission frequency, working time slot conflicts, or waveform configuration mismatches, it generates execution consistency verification result data.

[0174] After completing the consistency check, the main control unit collects the corrected received power data and corrected spectrum occupancy data of each node in the next observation cycle and compares them with the observation data before execution. When the disturbance intensity level decreases or the occupancy rate of the disturbed frequency band decreases, the anti-disturbance effect change data is generated. When no significant improvement is observed or an abnormal increase occurs, an effect abnormality flag is generated, and the control parameters are regenerated or the system enters a degraded operation mode.

[0175] Through the above processing, node anti-disturbance execution status data is finally formed. The node anti-disturbance execution status data specifically includes actual execution parameter feedback data, execution consistency verification result data, anti-disturbance effect change data, and execution anomaly flag data, thereby realizing the executable, verifiable, and traceable management of the anti-disturbance control process.

[0176] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0177] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0178] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for electromagnetic environment disturbance suppression control of an airborne distributed electronic countermeasures system, characterized in that: The method includes the following steps: Step S1: Distributed electronic countermeasures nodes synchronize to obtain distributed node running status alignment data; Step S2: Environmental anomaly disturbance determination. Based on the distributed node operation status alignment data, the changes in the received signals of each node are correlated and compared with the node's own transmission status. Combined with the node's directional reception difference information and node operation health status data, the sources of abnormal changes are classified to obtain electromagnetic environment disturbance determination data. Step S3: Node observation anomaly correction. Based on the electromagnetic environment disturbance judgment data, state correlation analysis is performed on the received data of nodes with abnormal disturbances. Combined with node gain state information and signal limiting state information, the affected observation results are corrected to obtain valid node observation data. Step S4: Generation of anti-disturbance control parameters. Based on the effective observation data of the nodes, combined with the current working mode and equipment operating boundary conditions of each electronic countermeasure node, the transmission power, transmission frequency, waveform parameters and working sequence are calculated and generated to obtain the node anti-disturbance control parameter data. Step S5: Environmental disturbance immunity control. The disturbance immunity control parameter data of the node is sent to the corresponding node for execution, and the node's operating status and received signal changes after execution are sampled and verified for consistency to obtain the node disturbance immunity execution status data.

2. The electromagnetic environment disturbance rejection control method for an airborne distributed electronic countermeasures system according to claim 1, characterized in that: In step S1, the distributed electronic countermeasures nodes synchronize by collecting local time information, working status information and transmission and reception parameter information of each electronic countermeasures node, aligning the time information of each node based on the airborne unified time source, and performing consistency verification on the node operating status to obtain distributed node operating status alignment data. The distributed node operating status alignment data specifically includes: node time alignment identifier data, node local oscillator lock status data, node transmission operating parameter data, node reception link status data, and node operating health status data.

3. The electromagnetic environment disturbance suppression control method for an airborne distributed electronic countermeasures system according to claim 2, characterized in that: In step S2, the determination of abnormal environmental disturbances specifically employs a multi-evidence consistent attribution method, including the following steps: Step S21: Observation feature normalization processing. Within a preset time window, extract the changes in received power, frequency band occupancy, and spectral shape of each node, and generate node received change feature data. At the same time, extract the corresponding node's transmission frequency, transmission power, duty cycle, received gain status, and limiting status information to form node operation related feature data. Step S22: Anomaly candidate detection. Aggregate the change feature data received by each node, calculate the overall degree of abnormal change, and compare it with the preset baseline threshold. When the overall degree of abnormal change exceeds the preset threshold, generate disturbance presence flag data and classify disturbance intensity level data according to the degree of abnormal change. Step S23: Plan consistency analysis, compare the reception change characteristic data of each node with the transmission operating parameters in the corresponding time window. When the abnormal frequency band overlaps with the transmission frequency, and the degree of reception change is synchronous with the change in transmission power or duty cycle, generate plan consistency evidence data. Step S24: Spatial consistency analysis, compare the degree of change in reception of multiple nodes, and generate spatial consistency evidence data when multiple nodes show a synchronous upward or downward trend within the same time window; Step S25: Health consistency analysis, which comprehensively judges the node local oscillator lock-in status, receive link limiting status, overflow status, and temperature, current and communication link quality status. When abnormal changes are concentrated on nodes with abnormal operating status, health consistency evidence data is generated. Step S26: Multi-evidence attribution determination. The plan consistency evidence data, spatial consistency evidence data, and health consistency evidence data are comprehensively compared according to preset weights to generate disturbance source type data, including external abnormal radiation sources, local mutual interference sources, and node operation abnormal sources. Step S27: Disturbance frequency band distribution generation. The received frequency bands of each node are summarized and processed to generate disturbance frequency band distribution data and summarize it into electromagnetic environment disturbance judgment data.

4. The electromagnetic environment disturbance rejection control method for an airborne distributed electronic countermeasures system according to claim 3, characterized in that: In step S2, the electromagnetic environment disturbance determination data specifically includes: disturbance presence indicator data, disturbance intensity level data, disturbance source type data, and disturbance frequency band distribution data.

5. The electromagnetic environment disturbance rejection control method for an airborne distributed electronic countermeasures system according to claim 4, characterized in that: In step S3, the node observation anomaly correction adopts an observation reversible correction improvement method, which includes the following steps: Step S31: Anomaly Observation Location. Within a unified time window, read the node's receiving gain status information, amplitude limiting trigger flag, and overflow flag. Mark the time periods with amplitude limiting or overflow to obtain the data of the affected observation interval. Step S32: Gain inversion processing: Based on the preset gain calibration value corresponding to the current receiving gain level of the node, the observed received power data is inversely converted to obtain the equivalent input power data. Step S33: Amplitude limiting compensation processing. Within the same time window, select reference observation samples that have not experienced amplitude limiting, calculate the reference power statistics, and determine the recovery factor based on the proportional relationship between the reference power statistics and the amplitude-limited observation power. Compensate and correct the amplitude-limited observation data to obtain the corrected received power data. Step S34: Spectrum occupancy correction. Gain inversion and amplitude limiting compensation are performed on the observed energy at each frequency point, and the frequency band occupancy identifier is regenerated according to the preset spectrum judgment threshold to obtain the corrected spectrum occupancy data. Step S35: Credibility assessment. Based on the limiting triggering situation, gain change magnitude, node link quality status, and cross-node observation consistency, generate observation credibility level data. Step S36: Generating valid intervals. Based on the overflow flag, the proportion of continuous amplitude limiting time, and the observation confidence level, low confidence time periods or frequency bands are removed to generate valid observation interval data.

6. The electromagnetic environment disturbance rejection control method for an airborne distributed electronic countermeasures system according to claim 5, characterized in that: In step S3, the effective observation data of the node specifically includes corrected received power data, corrected spectrum occupancy data, observation confidence level data, and observation effective interval data.

7. The electromagnetic environment disturbance rejection control method for an airborne distributed electronic countermeasures system according to claim 6, characterized in that: In step S4, the generation of disturbance-resistant control parameters employs an improved method for generating cooperative control parameters, which includes the following steps: Step S41: Observation screening: Based on the observation confidence level data and observation effective interval data in the node's effective observation data, the observation information that can be used for control decision-making is screened. At the same time, combined with the local oscillator locking status data, operational health status data and communication link quality status data of each node, weight data for node participation in coordination is generated. Step S42: Construct the feasible domain of control. Based on the current working mode, power limit, frequency band permitted range, waveform configuration range and transmit / receive time slot constraints of each node, construct a set of feasible parameters for transmit power, transmit frequency, waveform parameters and working timing. Step S43: Candidate parameter combination generation. Based on the information of the main disturbed frequency bands and the available observation results, select candidate transmit power, frequency, waveform and operating timing combinations that avoid the disturbed frequency bands and meet the operating constraints from the feasible parameter set of each node, and generate a candidate control parameter set. Step S44: Multi-node consistency determination. Based on the conflict relationship between node weight data and candidate control parameter combinations, the consistency of each candidate combination is compared. When the candidate combination meets the preset consistency conditions, a set of collaborative nodes is generated. When there is a conflict, priority is selected or the scope of collaboration is reduced according to the node weight to generate collaborative control identification data. Step S45: Control parameter encapsulation output. Based on the final determined combination of control parameters, generate node anti-disturbance control parameter data.

8. The electromagnetic environment disturbance rejection control method for an airborne distributed electronic countermeasures system according to claim 7, characterized in that: In step S4, the node anti-disturbance control parameter data specifically includes: transmit power adjustment parameter data, transmit frequency adjustment parameter data, waveform parameter adjustment data, working timing adjustment data, and cooperative control identification data.

9. The electromagnetic environment disturbance rejection control method for an airborne distributed electronic countermeasures system according to claim 8, characterized in that: In step S5, the node anti-disturbance execution status data specifically includes: actual execution parameter feedback data, execution consistency verification result data, anti-disturbance effect change data, and execution anomaly flag data.

Images