A microwave electric tunable filter control system and method for anti-interference

Abstract

The present invention discloses a microwave electrically tunable filter control system and method for anti-interference, which specifically relates to the field of communication anti-interference technology. The interference suppression of microwave signals is achieved through the following steps: first, based on a preset frequency range, the spectrum changes of microwave signals are monitored in real time to identify the type and source of interference signals. Then, when the microwave electrically tunable filter is initially adjusted, a single-channel strategy is adopted to implement preset suppression measures based on the interference frequency characteristics to effectively filter out interference signals; finally, a feedback control mechanism is used to monitor the filter performance during the adjustment process, and the strategy is dynamically corrected to ensure the stability and efficiency of the anti-interference performance; the present invention evaluates the performance of the microwave filter in real time through the environmental change characteristic index and the environmental complexity characteristic index, and generates positive feedback or negative feedback signals through a fuzzy reasoning model as the basis for adjusting the work of the filter, and adopts a feedback control mechanism to improve the ability to cope with the interference environment and ensure efficient anti-interference performance.

Description

Technical Field

[0001] The present invention relates to the technical field of communication anti-interference, and more specifically, to a microwave electrically tunable filter control system and method for anti-interference. Background Art

[0002] With the development of modern communication, radar, navigation and other technologies, high-frequency electronic equipment has put forward higher and higher requirements on the anti-interference ability of signals. In a complex electromagnetic environment, external interference signals often affect the transmission of normal signals and reduce the working performance of the system. This interference may come from multiple sources, such as radiation from other equipment, environmental noise, intermodulation interference of adjacent signals, etc.

[0003] In order to improve the anti-interference ability of the system, effective filtering methods are needed to suppress interference signals and ensure the transmission quality of normal signals. As a component that can dynamically adjust the frequency response, microwave electrically tunable filters have important applications in anti-interference technology. By adjusting its operating frequency range, it can selectively filter out unwanted interference signals and maintain signal stability and clarity during frequency changes.

[0004] The basic working principle of microwave electrically tunable filters: The basic working principle of microwave electrically tunable filters is to adjust the parameters of the filter according to the frequency characteristics of the input signal so that the signal within a specific frequency range is transmitted or suppressed.

[0005] Traditional microwave electrically tunable filter control usually has certain limitations in anti-interference, especially in complex electromagnetic environments. In addition, the existing control methods may have a long response time and cannot respond to rapidly changing interference signals in a timely manner. Therefore, a microwave electrically tunable filter control system and method for anti-interference is proposed herein to solve the above problems. Summary of the invention

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A microwave electric tunable filter control method for anti-interference includes the following steps:

[0008] Step 1: Based on a preset frequency range, obtain the spectrum characteristic information of the microwave signal, and confirm the type and source of the interference signal by real-time monitoring the spectrum change of the microwave signal;

[0009] Step 2: When the microwave electrically tunable filter is initially adjusted, a single-channel strategy is adopted to execute preset suppression measures corresponding to the interference signal according to the frequency characteristics of the monitored interference signal to ensure that the interference frequency is effectively filtered out;

[0010] Step 3: During the adjustment process, a feedback control mechanism is used to monitor the performance of the microwave filter, and the strategy type is modified through different types of feedback signals to ensure the stability and efficiency of the anti-interference performance.

[0011] In a preferred embodiment, by real-time monitoring of the spectrum changes of the microwave signal, the type and source of the interference signal are confirmed to be:

[0012] Acquire time series data of the microwave signal to be monitored;

[0013] Performing spectrum analysis on the microwave signal by using a spectrum analysis tool to obtain frequency distribution and intensity information of the signal;

[0014] According to the spectrum analysis results, real-time monitoring is performed, and at the same time, according to the preset spectrum change abnormality standard, whether there is spectrum change abnormality in the signal is identified, and then potential interference signals are determined;

[0015] When abnormal spectrum changes are detected, the spectrum distribution of the interference signal is obtained through dynamic monitoring of spectrum analysis, and the characteristic data of the interference signal is generated. It is then compared with the known interference signal database to confirm the type and source of the interference signal.

[0016] In a preferred embodiment, monitoring the performance of the microwave filter refers to:

[0017] Focus on the rate of change and fluctuation of environmental characteristics over time, which is used to assess the severity of environmental changes and generate an environmental change characteristic index;

[0018] The complexity of the environment is measured by the correlation and information entropy between environmental features, which is used to evaluate the multi-dimensional interactive complexity of the environment and generate an environmental complexity characteristic index.

[0019] In a preferred embodiment, the feedback control mechanism refers to:

[0020] The environmental change characteristic index and the environmental complexity characteristic index are substituted into the pre-trained fuzzy inference model, and the performance of the current microwave filter is divided into a high-quality anti-interference state and a low-quality anti-interference state. A positive feedback signal is generated in the high-quality anti-interference state, and a negative feedback signal is generated in the low-quality anti-interference state.

[0021] In a preferred embodiment, the logic for obtaining the environmental change characteristic index is:

[0022] Obtain the environmental feature data X(t) of one environmental feature of a single channel, where t is the time series index. It is assumed that the data is a discrete time series and the recording time interval is Δt.

[0023] Use the sliding window averaging method to smooth the environmental feature data to reduce the impact of noise: ; n is the window size; This is the result of smoothing;

[0024] Use numerical differentiation method to calculate the rate of change of environmental characteristics, that is, the instantaneous rate of change of environmental characteristics:

[0025] ; is the current rate of change of environmental characteristics;

[0026] Calculate the amplitude of environmental characteristic fluctuations to measure the severity of environmental changes:

[0027] ; is the average value of environmental characteristics, is the i-th environmental characteristic data, N is the total amount of environmental characteristics, is the current fluctuation range of environmental characteristics;

[0028] Combine the change rate and fluctuation range to calculate the environmental change value:

[0029] ; is the current environmental change value of the environmental feature;

[0030] Obtain the change threshold corresponding to each environmental feature of the single channel, calculate the ratio of the environmental change value corresponding to each environmental feature to the change threshold according to the type of environmental feature, and use the maximum ratio result as the environmental change characteristic index of the current environment of the single channel.

[0031] In a preferred embodiment, the logic for obtaining the environmental complexity index is:

[0032] Get multiple preset environmental feature data , where k is the dimension of the feature and t is the time index;

[0033] Perform feature correlation analysis and calculate the correlation matrix R between various environmental features, where each element express and Relevance:

[0034] ; is the total number of environmental features;

[0035] Shannon entropy is used to calculate the entropy value of environmental characteristics, indicating the uncertainty of information, and the complexity of the environment is measured by entropy value: ; represents the entropy value of the i-th environmental feature, express The probability distribution of

[0036] The relationship between features can be comprehensively considered through their entropy value and correlation matrix to obtain the local complexity of environmental features:

[0037] ; represents the local complexity of the i-th environmental feature;

[0038] The weighted sum of the local complexity of all environmental characteristics is the result of the environmental complexity index.

[0039] In a preferred embodiment, modifying the strategy type through different types of feedback signals refers to:

[0040] When the type of the feedback signal is a positive feedback signal, a single-channel strategy is adopted, and when the type of the feedback signal is a negative feedback signal, a multi-channel strategy is adopted.

[0041] In a preferred embodiment, a microwave electrically tunable filter control system for anti-interference includes:

[0042] The interference signal confirmation module obtains the spectrum characteristic information of the microwave signal based on the preset frequency range, and confirms the type and source of the interference signal by monitoring the spectrum changes of the microwave signal in real time;

[0043] The interference signal suppression module adopts a single-channel strategy during the initial adjustment of the microwave electronically tunable filter. According to the frequency characteristics of the monitored interference signal, it executes the preset suppression measures corresponding to the interference signal to ensure that the interference frequency is effectively filtered out.

[0044] The feedback module uses a feedback control mechanism to monitor the performance of the microwave filter and generate different types of feedback signals during the adjustment process;

[0045] The correction module corrects the strategy type through different types of feedback signals to ensure the stability and efficiency of anti-interference performance.

[0046] Technical effects and advantages of the present invention:

[0047] By real-time monitoring of the spectrum changes of microwave signals and combining existing spectrum analysis technology, the present invention can accurately identify and locate the type of interference signals and their frequency characteristics. This enables the system to promptly detect abnormal fluctuations or mutations in the spectrum, determine the existence of interference signals and their sources, and avoid the common problem of interference not being identified or processed in a timely manner in traditional methods.

[0048] The present invention adopts a regulation mechanism that combines single-channel and multi-channel strategies. When the interference signal is relatively simple and single, the system can use a single-channel strategy to quickly respond and efficiently filter out the interference; when the interference environment is complex or changeable, the system can effectively enhance its ability to adapt to complex or dynamic interference signals by processing signals in parallel through multiple channels. The use of a feedback control mechanism further enhances the system's ability to flexibly respond to interference environments and ensures efficient anti-interference performance.

[0049] The present invention evaluates the performance of the microwave filter in real time through the environmental change characteristic index and the environmental complexity characteristic index, and generates positive feedback or negative feedback signals through the fuzzy reasoning model, which serves as the basis for adjusting the operation of the filter. When the environmental changes are more drastic and the interference is complex, the system automatically adjusts to a negative feedback mechanism and switches to a multi-channel strategy to enhance the anti-interference ability of the filter; conversely, when the environment is relatively stable, positive feedback is used to enhance the stability of the system. This feedback mechanism can ensure that the microwave filter always maintains the best anti-interference effect under different environmental conditions.

[0050] The present invention can make quick adjustments when interference occurs by continuously monitoring spectrum changes, environmental characteristics, and filter performance, ensuring that the system can maintain stable communication quality in a high-interference environment. The feedback control mechanism combines environmental changes and complexity analysis to effectively predict and optimize the performance of the filter in a changing environment, improving the stability of the system and the reliability of long-term operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0052] Figure 1 The schematic diagram is a control method of a microwave electrically tunable filter for anti-interference in the present invention.

[0053] Figure 2 The schematic diagram of a microwave electrically tunable filter control system for anti-interference in the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Reference Figure 1 - Figure 2 The following embodiments are obtained:

[0056] Example 1: The microwave electrically tunable filter adjusts its filtering characteristics through electronic control means, allowing signals to pass at different frequencies. It is widely used in communication, radar and other systems to improve signal clarity and anti-interference ability. In the anti-interference system, the electrically tunable filter can be flexibly adjusted according to actual needs to resist external electromagnetic interference.

[0057] By dynamically adjusting the frequency response and bandwidth of the microwave electronically tunable filter, the system can automatically adjust the frequency according to the change of the interference signal to prevent the interference signal from entering the signal channel. The filter can be adjusted according to real-time environmental conditions or through automated algorithms to ensure efficient operation of the communication link and reduce errors or packet loss.

[0058] In the anti-interference strategy, microwave electrically tunable filter regulation can be used as an active defense mechanism. For example, when the system detects the frequency range of interference signals, the filter will quickly adjust its working state to weaken or block these interference signals so that the main signal is not affected, thereby ensuring the stability of the communication system.

[0059] Microwave electronically tunable filter: A filter that can adjust its frequency response electronically. Microwave electronically tunable filters can precisely control the frequency range of the signal to filter out interfering signals and pass useful signals.

[0060] Anti-interference: refers to the system being able to maintain a stable working state under the influence of external interference signals to ensure accurate transmission of information. In electronic systems, anti-interference design is the key to ensuring the normal operation of equipment in complex environments.

[0061] Frequency response: refers to the response characteristics of an electronic device or filter to signals of different frequencies, usually described by gain or attenuation. In an electronically tunable filter, the frequency response determines which signals can pass and which signals will be filtered out.

[0062] In the process of regulating the anti-interference microwave electrically tunable filter, the existing technology usually encounters the following problems:

[0063] The control response time of traditional electrically tunable filters is long, especially when the frequency and bandwidth change greatly. This response delay may affect the real-time performance and accuracy of the system under high-frequency interference, especially when rapid adjustment is required in a dynamic environment. In the anti-interference performance processing of microwave electrically tunable filters, if the problem of long adjustment response time is encountered, the following methods can be used for optimization to improve the real-time performance and accuracy of the system: In a dynamic environment, the real-time response requirements may be very high, and the response can be accelerated by parallel signal processing:

[0064] Multi-channel parallel processing: Adopt multi-channel receiving and processing strategies to improve the response speed of the overall system by sharing the processing load of different frequency bands.

[0065] It should be noted that multi-channel parallel processing is not adopted in the system from the beginning, because this method usually increases the complexity of the system, hardware cost and signal processing challenges.

[0066] The hardware resources required for multi-channel parallel processing include multiple receiving channels, signal processing units, tuners, and data channels in the microwave electrically tunable filter, and significantly increase power consumption. The scheduling and load distribution of multi-channel parallel processing in a dynamic environment usually requires complex algorithms to determine the working status of each channel. This scheduling algorithm may require real-time monitoring of the system status and dynamic adjustment of the working frequency band and processing strategy of each channel, which will increase the additional burden on the microwave electrically tunable filter.

[0067] In the early stages of a system, high-frequency rapid adjustment or complex anti-interference capabilities may not be required. Designers often optimize based on actual needs. If the requirements are relatively simple (for example, operating in a relatively static environment), a single-channel system is sufficient. Multi-channel parallel processing is usually considered when there is a need to cope with higher complexity and dynamic changes. In some application scenarios (such as fixed-frequency communication systems or relatively stable environments), the system does not require rapid response and frequent adjustments. At this time, a single-channel design is simpler, more reliable, and more economical.

[0068] Therefore, the present invention proposes a microwave electrically tunable filter control method for anti-interference, comprising the following steps:

[0069] Step 1: Based on the preset frequency range, obtain the spectrum characteristic information of the microwave signal, confirm the type and source of the interference signal by real-time monitoring the spectrum changes of the microwave signal; obtain the spectrum characteristics of the microwave signal and determine the frequency range and distribution of the signal. The spectrum characteristics are mainly the representation of the signal in the frequency domain, reflecting the frequency components, intensity and distribution of the signal. This process is carried out through the spectrum analysis technology in the prior art, which can accurately identify the center frequency and bandwidth range of the signal, and provide a basis for subsequent interference identification and filter adjustment.

[0070] By monitoring the changes in the signal spectrum in real time, detect whether there is an interference signal and analyze its spectrum distribution. If there is an interference signal, it is necessary to confirm its frequency characteristics (such as frequency range and amplitude) and distinguish it from the target signal. This step uses the spectrum analysis algorithm in the prior art (such as fast Fourier transform FFT) to scan the signal spectrum in real time and detect changes. The types of interference signals are broadband noise interference, narrowband interference, pulse interference, intermittent interference, etc.

[0071] Specifically, the microwave signal of the area to be monitored is obtained through a receiving antenna or sensor. The signal can come from different communication equipment or radar systems. A digital spectrum analyzer or other spectrum analysis tool is used to perform Fourier transform (FFT) or other spectrum analysis algorithms on the collected time series data to obtain a spectrum distribution diagram of the signal, indicating the signal strength and the corresponding frequency position. The spectrum changes of the signal are continuously monitored to detect whether there are abnormal changes (such as a sudden increase in the spectrum peak, an expansion of the frequency range, etc.), which may indicate the presence of interference signals. When the spectrum is obviously abnormal, the system determines that there may be interference in the signal according to preset rules or algorithms (such as amplitude threshold, frequency drift, etc.), and confirms the frequency and intensity of the interference. By analyzing the characteristic data of the interference signal such as frequency, intensity, and duration, the description information of the interference signal is generated. This information can be compared with the existing interference signal database to confirm the type and possible source of the interference signal. Once the type and source of the interference signal are confirmed, the microwave electrically tunable filter will automatically adjust the relevant parameters of the communication system to minimize the impact of interference on communication quality.

[0072] Generally speaking, spectrum changes may be considered abnormal when the following situations occur:

[0073] Frequency deviation is outside the predetermined range: For example, the frequency drift of the signal exceeds the normal operating range of the device, which usually triggers an alarm mechanism.

[0074] Amplitude fluctuation exceeding threshold: Sudden changes in signal amplitude (such as sudden increase or decrease) are also considered abnormal if they exceed the set standard threshold. These thresholds can be set based on historical data or normal operating range.

[0075] High-frequency power changes: In real-time spectrum analysis, if the power spectrum density of a signal changes frequently and for no apparent reason, it can be considered that there are abnormal changes. For example, a sudden increase or decrease in power in a certain frequency band may indicate the presence of an interference signal.

[0076] Signal periodic or non-periodic variations: Some spectrum analysis tools detect periodic variations in frequency and amplitude that may indicate potential interference if they are unusual (e.g., they vary outside of an expected period).

[0077] Step 2: When initially adjusting the microwave electrically tunable filter, a single-channel strategy is adopted. According to the frequency characteristics of the monitored interference signal, the preset suppression measures corresponding to the interference signal are executed to ensure that the interference frequency is effectively filtered out; for example, the center frequency and bandwidth of the microwave electrically tunable filter are adjusted according to the spectrum characteristics of the interference signal to ensure that the interference signal is effectively filtered out. Adopt a single-channel strategy: adjust the working parameters of the filter according to the monitored interference signal frequency. This method can quickly respond to and accurately filter interference signals of known frequencies, but may not be flexible enough for interference with complex or changing spectra.

[0078] Step 3: During the adjustment process, a feedback control mechanism is used to monitor the performance of the microwave filter, and the strategy type is corrected through different types of feedback signals to ensure the stability and efficiency of the anti-interference performance. During the filter adjustment process, its performance is monitored and dynamically adjusted through the feedback mechanism. If the single-channel strategy fails to achieve the expected effect (for example, the filtering effect is insufficient), the strategy can be corrected through a negative feedback signal. Feedback mechanism: Generate a feedback signal by monitoring the interference suppression effect of the filter output. If the current single-channel strategy fails to effectively filter the interference signal, the strategy type will be adjusted to improve the anti-interference effect. Under the multi-channel parallel strategy, multiple channels of the filter can process signals in different frequency ranges at the same time, thereby improving the adaptability to complex interference environments. The multi-channel strategy can handle a wider range of interference signals in the spectrum, especially when the interference signal has complex or dynamically changing characteristics, and can maintain a high anti-interference performance.

[0079] By real-time monitoring of the spectrum changes of microwave signals, the type and source of interference signals are confirmed as follows:

[0080] Acquire time series data of the microwave signal to be monitored;

[0081] Performing spectrum analysis on the microwave signal by using a spectrum analysis tool to obtain frequency distribution and intensity information of the signal;

[0082] According to the spectrum analysis results, real-time monitoring is performed, and at the same time, according to the preset spectrum change abnormality standard, whether there is spectrum change abnormality in the signal is identified, and then potential interference signals are determined;

[0083] When abnormal spectrum changes are detected, the spectrum distribution of the interference signal is obtained through dynamic monitoring of spectrum analysis, and the characteristic data of the interference signal is generated. It is then compared with the known interference signal database to confirm the type and source of the interference signal.

[0084] Monitoring the performance of microwave filters means: paying attention to the rate of change and fluctuation of environmental characteristics over time, which is used to evaluate the severity of environmental changes and generate an environmental change characteristic index; measuring the complexity of the environment through the correlation and information entropy between environmental characteristics, which is used to evaluate the multi-dimensional interactive complexity of the environment and generate an environmental complexity characteristic index. Monitoring the performance of microwave filters can be evaluated by the environmental change characteristic index and the environmental complexity characteristic index, which helps predict the stability and effectiveness of the filter under different environmental conditions. By tracking the rate of change, fluctuation, correlation and information entropy of environmental characteristics, we can gain a deep understanding of the response performance of the filter and its performance in a changing and complex environment, thereby optimizing system design, improving reliability and extending service life.

[0085] The feedback control mechanism refers to: substituting the environmental change characteristic index and the environmental complexity characteristic index into the pre-trained fuzzy inference model, dividing the performance of the current microwave filter into a high-quality anti-interference state and a low-quality anti-interference state, generating a positive feedback signal in the high-quality anti-interference state, and generating a negative feedback signal in the low-quality anti-interference state.

[0086] In the feedback control mechanism, the role of the fuzzy reasoning model is to judge the performance status of the current microwave filter based on the environmental change characteristic index and the environmental complexity characteristic index, and to adjust its anti-interference ability through positive and negative feedback. The working principle of fuzzy logic is explained in detail below.

[0087] First, the environmental change characteristic index and environmental complexity characteristic index are used as input variables. These two indexes can be expressed in fuzzy language, for example: Environmental change characteristic index: can be divided into drastic change, medium change and smooth change. Environmental complexity characteristic index: can be divided into high complexity, medium complexity and low complexity. The fuzzification of these two input variables enables the system to handle the dynamic changes of the environment in a more flexible way, rather than a single numerical judgment.

[0088] In the fuzzy reasoning model, the rule base is the core part. Based on prior knowledge or actual experience, a set of rules is established to reflect the relationship between input variables and output results. Taking this example, the output is the anti-interference state of the microwave filter, which can be divided into two states: high-quality anti-interference and low-quality anti-interference. Some examples of the rule base may be as follows: If the environmental change characteristic index is drastic and the environmental complexity characteristic index is highly complex, then the filter state is low-quality anti-interference. If the environmental change characteristic index is stable and the environmental complexity characteristic index is low complexity, then the filter state is high-quality anti-interference. If the environmental change characteristic index is moderately changing and the environmental complexity characteristic index is moderately complex, then the filter state is moderately anti-interference.

[0089] The process of fuzzy reasoning is generally divided into the following steps:

[0090] Fuzzification: The input environmental change characteristic index and environmental complexity characteristic index are converted into corresponding fuzzy sets through fuzzification. For example, if the value of the environmental change characteristic index is 70, and it is divided according to the set fuzzy rules, it may be classified as the fuzzy set of medium change.

[0091] Rule reasoning: Based on the input fuzzy set, find the matching rules in the rule base and perform reasoning to obtain the fuzzy output. For example, if the input is medium-variable and medium-complex, the fuzzy result of medium anti-interference can be obtained according to the rule base.

[0092] Defuzzification: The output of fuzzy inference is a fuzzy set, which needs to be converted into a clear control signal through the defuzzification process. Here, the control signal is expressed as positive feedback or negative feedback. For example, if the filter is in a high-quality anti-interference state, a positive feedback signal will be generated; if it is in a low-quality anti-interference state, a negative feedback signal will be generated.

[0093] High-quality anti-interference state (positive feedback): When the fuzzy inference system determines that the filter is in a high-quality anti-interference state, a positive feedback signal is generated. This signal will strengthen the working state of the filter or motivate the system to take measures to maintain the current performance and ensure that the system continues to operate stably.

[0094] Low-quality anti-interference state (negative feedback): When the system determines that the filter is in a low-quality anti-interference state, a negative feedback signal is generated, which prompts the system to take corrective measures, such as adjusting the filter's operating parameters or adding a compensation mechanism to improve its anti-interference ability.

[0095] The practical application of fuzzy logic is as follows: If the current environmental change characteristic index shows that the environmental change is more drastic, and the environmental complexity characteristic index shows that the environmental complexity is high, then the filter's anti-interference ability may be weak, and the system will determine that it is in a low-quality anti-interference state through the fuzzy reasoning model, and then generate a negative feedback signal to prompt the system to adjust. On the contrary, if the environment is relatively stable and less complex, the filter's anti-interference ability is strong, and the system will generate a positive feedback signal to encourage the system to maintain the current stable state.

[0096] The fuzzy reasoning model can effectively evaluate the performance status of the microwave filter by inputting the environmental change characteristic index and the environmental complexity characteristic index, and generate appropriate feedback signals based on the reasoning results. Positive feedback signals promote the maintenance of high-quality anti-interference status, while negative feedback signals help the system adjust to cope with low-quality anti-interference status. This feedback mechanism helps to improve the stability and anti-interference ability of microwave filters in dynamic environments.

[0097] The calculation process of the environmental change characteristic index involves extracting multiple information levels from environmental data, quantifying the changes and severity of environmental characteristics through a series of calculation methods, and finally obtaining a comprehensive index that describes the current environmental changes. Its acquisition logic reflects the idea of ​​comprehensively evaluating environmental changes through multi-dimensional indicators such as smoothing, change rate, and fluctuation amplitude. The acquisition logic of the environmental change characteristic index is:

[0098] Obtain the environmental feature data X(t) of one environmental feature (such as temperature, humidity, air quality, etc.) of a single channel, where t is the time series index. It is assumed that the data is a discrete time series and the recording time interval is Δt;

[0099] Since environmental data is usually accompanied by noise and irregular fluctuations, in order to obtain a more stable and true trend, the original data needs to be smoothed. The sliding window average method averages a certain number of samples near the data point to reduce the interference of short-term fluctuations. Use the sliding window average method to smooth the environmental feature data to reduce the impact of noise: ; n is the window size; To smooth the results; by smoothing, random noise can be removed so that the data reflects clearer long-term trends, which is crucial for the accuracy of subsequent analysis, especially when the environment changes slowly and is disturbed.

[0100] The rate of change describes the speed at which environmental characteristics change at a certain moment, and reflects the increase or decrease of environmental parameters in a short period of time. Through the numerical differentiation method, the instantaneous rate of change of environmental characteristics at time t can be calculated:

[0101] ; The current rate of change of environmental characteristics; the rate of change reflects the instantaneous severity of changes in environmental characteristics and can help determine the speed of environmental changes.

[0102] Calculate the amplitude of environmental characteristic fluctuations to measure the severity of environmental changes:

[0103] ; is the average value of environmental characteristics, is the i-th environmental characteristic data, N is the total amount of environmental characteristics, The current fluctuation range of environmental characteristics; the fluctuation range can reveal the range of changes in environmental characteristics. If the fluctuation range is large, it means that the environmental changes are more drastic, which may be related to extreme weather or other emergencies.

[0104] Combine the change rate and fluctuation range to calculate the environmental change value:

[0105] ; is the current environmental change value of the environmental feature, , All are preset non-zero proportional coefficients; the environmental change value can provide a comprehensive indicator by combining the change rate and fluctuation amplitude to help assess the severity of environmental changes. If the environmental change value is high, it means that the environmental change may be more drastic and needs attention.

[0106] For a single-channel environmental feature (such as temperature, humidity, etc.), the change values ​​corresponding to different environmental features can be calculated through the above steps. Next, according to the type of environmental feature (such as temperature change, humidity fluctuation, etc.), compare the ratio of the change value corresponding to each environmental feature to the preset change threshold, and use the maximum ratio result as the environmental change characteristic index of the current environment of the single channel. A higher environmental change characteristic index may mean that the environmental change is more drastic, and corresponding countermeasures may need to be taken.

[0107] The environmental complexity index is an indicator that quantifies environmental complexity through multi-dimensional environmental characteristics. Its core idea is to reflect the overall complexity of an environmental system by comprehensively considering the relationship between environmental characteristics and the complexity of the characteristics themselves. The logic for obtaining the environmental complexity index is:

[0108] First, you need to obtain multiple environmental feature data. These features can include air quality, temperature, humidity, noise, light intensity, etc., or select appropriate environmental features according to the specific application scenario. Assume that there are multiple environmental features, each of which has observations at multiple time points (represented by index t). Therefore, the dimension of the data matrix is ​​k×t, where k is the dimension of the feature (i.e., the number of environmental features), t is the length of the time series, and multiple preset environmental feature data , corresponding to the data matrix, where k is the dimension of the feature and t is the time index;

[0109] Perform feature correlation analysis and calculate the correlation matrix R between various environmental features, where each element express and Correlation: Correlation can be calculated using methods such as Pearson correlation coefficient and Spearman rank correlation coefficient, with the aim of revealing the linear or nonlinear relationship between features.

[0110] ; is the total number of environmental features; If the value is close to 1, it means that the two features are highly correlated; if it is close to 0, it means that there is no significant correlation between them.

[0111] Shannon entropy is used to calculate the entropy value of environmental characteristics, indicating the uncertainty of information, and the complexity of the environment is measured by entropy value: ; represents the entropy value of the i-th environmental feature, express The higher the entropy value, the greater the uncertainty or complexity of the feature, and vice versa. The default function base is 10.

[0112] The relationship between features can be comprehensively considered through entropy value and correlation matrix to obtain the local complexity of environmental features:

[0113] ; represents the local complexity of the i-th environmental feature;

[0114] The result of weighted summation of the local complexity of all environmental features is the environmental complexity index. The weight can be determined according to the importance of the feature or its influence in the environment. If the weights of all features are equal, they can be simply summed. By comprehensively considering the entropy value (uncertainty) of environmental features and the correlation between features, the environmental complexity index provides a measure of the overall environmental complexity of the environment in which the microwave electrically tunable filter is located, which can accurately reflect the complexity of the application environment of the microwave electrically tunable filter.

[0115] Modifying the strategy type through different types of feedback signals refers to:

[0116] When the type of the feedback signal is a positive feedback signal, a single-channel strategy is adopted, and when the type of the feedback signal is a negative feedback signal, a multi-channel strategy is adopted.

[0117] Positive feedback signals usually indicate some kind of enhancement of the signal or output in the system, which means that when the input signal is fed back into the system, it will intensify its original trend. In the adjustment of microwave filters, positive feedback signals often cause the system to respond more strongly to interference. When using a single-channel strategy, the system will choose a simpler, centralized and intuitive filtering method to avoid too much complex processing, ensure fast response, and maintain stability. In terms of anti-interference performance, the single-channel strategy is usually able to more keenly identify and respond to interference signals, but its processing capabilities may be limited in the face of complex interference environments.

[0118] When using a single-channel strategy with positive feedback signals, the system's responsiveness can be optimized by strengthening specific signal paths. This strategy is suitable for scenarios where the interference source is relatively single or the system state needs to be adjusted quickly. This strategy simplifies system design, reduces complexity, and helps improve the system's stability and response speed in a simple interference environment.

[0119] The relationship between negative feedback signals and multi-channel strategies: Negative feedback signals mean that the output signal in the system will have an inhibitory effect on the input signal, that is, the system will reduce or offset the impact of interference signals through feedback mechanisms. In the adjustment process of microwave filters, negative feedback signals help balance the response of the system and avoid over-amplifying the impact of interference signals. When using a multi-channel strategy, the system will process through multiple different signal paths, which can effectively disperse the interference source and optimize the performance of multiple frequency bands or signal channels at the same time, thereby achieving stronger anti-interference capabilities and more accurate filtering effects.

[0120] When negative feedback signals are applied, the multi-channel strategy can achieve higher robustness through parallel signal processing, and can cope with complex and changing interference environments, especially when multiple frequency bands or multiple sources of interference need to be processed simultaneously. In this way, the system can more flexibly adjust the response of each channel, reduce the impact of overall interference on system performance, and improve system stability and efficiency.

[0121] Selecting a single-channel or multi-channel strategy through positive and negative feedback signals is actually optimizing the system's anti-interference ability according to the characteristics of the interference environment. Positive feedback signals tend to simplify and strengthen the response of specific channels and are suitable for situations where interference is relatively simple; while negative feedback signals enhance the system's anti-interference ability through multi-channel parallel processing and adapt to complex and changing interference environments. This flexible feedback control mechanism can ensure that the system maintains high stability and efficiency under various working conditions.

[0122] Embodiment 2: A microwave electrically tunable filter control system for anti-interference, comprising:

[0123] The interference signal confirmation module obtains the spectrum characteristic information of the microwave signal based on the preset frequency range, and confirms the type and source of the interference signal by monitoring the spectrum changes of the microwave signal in real time;

[0124] The interference signal suppression module adopts a single-channel strategy during the initial adjustment of the microwave electronically tunable filter. According to the frequency characteristics of the monitored interference signal, it executes the preset suppression measures corresponding to the interference signal to ensure that the interference frequency is effectively filtered out.

[0125] The feedback module uses a feedback control mechanism to monitor the performance of the microwave filter and generate different types of feedback signals during the adjustment process;

[0126] The correction module corrects the strategy type through different types of feedback signals to ensure the stability and efficiency of anti-interference performance.

[0127] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0128] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0129] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0131] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A microwave electrically tunable filter control method for anti-interference, characterized in that: The following steps are involved: Step 1: Based on a preset frequency range, obtain the spectrum characteristic information of the microwave signal, and confirm the type and source of the interference signal by real-time monitoring the spectrum change of the microwave signal; Step 2: When the microwave electrically tunable filter is initially adjusted, a single-channel strategy is adopted to execute preset suppression measures corresponding to the interference signal according to the frequency characteristics of the monitored interference signal to ensure that the interference frequency is effectively filtered out; Step 3: During the adjustment process, a feedback control mechanism is used to monitor the performance of the microwave filter, and the strategy type is modified through different types of feedback signals to ensure the stability and efficiency of the anti-interference performance; Monitoring the performance of microwave filters means: paying attention to the rate of change and fluctuation of environmental characteristics over time, which is used to evaluate the severity of environmental changes and generate an environmental change characteristic index; measuring the complexity of the environment through the correlation and information entropy between environmental characteristics, which is used to evaluate the multi-dimensional interactive complexity of the environment and generate an environmental complexity characteristic index; monitoring the performance of microwave filters is to evaluate the stability and effectiveness of the filter under different environmental conditions through the environmental change characteristic index and the environmental complexity characteristic index; The feedback control mechanism refers to: substituting the environmental change characteristic index and the environmental complexity characteristic index into the pre-trained fuzzy inference model, dividing the performance of the current microwave filter into a high-quality anti-interference state and a low-quality anti-interference state, generating a positive feedback signal in the high-quality anti-interference state, and generating a negative feedback signal in the low-quality anti-interference state; Modifying the strategy type through different types of feedback signals refers to: When the type of the feedback signal is a positive feedback signal, a single-channel strategy is adopted, and when the type of the feedback signal is a negative feedback signal, a multi-channel strategy is adopted.

2. A microwave electrically tunable filter control method for anti-interference according to claim 1, characterized in that: By real-time monitoring of the spectrum changes of microwave signals, the type and source of interference signals are confirmed as follows: Acquire time series data of the microwave signal to be monitored; Performing spectrum analysis on the microwave signal by using a spectrum analysis tool to obtain frequency distribution and intensity information of the signal; According to the spectrum analysis results, real-time monitoring is performed, and at the same time, according to the preset spectrum change abnormality standard, whether there is spectrum change abnormality in the signal is identified, and then potential interference signals are determined; When abnormal spectrum changes are detected, the spectrum distribution of the interference signal is obtained through dynamic monitoring of spectrum analysis, and the characteristic data of the interference signal is generated. It is then compared with the known interference signal database to confirm the type and source of the interference signal.

3. The microwave electrically tunable filter control method for anti-interference according to claim 2, characterized in that: The logic for obtaining the environmental change characteristic index is: Obtain the environmental feature data X(t) of one environmental feature of a single channel, where t is the time series index. It is assumed that the data is a discrete time series and the recording time interval is Δt. Use the sliding window averaging method to smooth the environmental feature data to reduce the impact of noise: ; n is the window size; This is the result of smoothing; Use numerical differentiation method to calculate the rate of change of environmental characteristics, that is, the instantaneous rate of change of environmental characteristics: ; is the current rate of change of environmental characteristics; Calculate the amplitude of environmental characteristic fluctuations to measure the severity of environmental changes: ; is the average value of environmental characteristics, is the i-th environmental characteristic data, N is the total amount of environmental characteristics, is the current fluctuation range of environmental characteristics; Combine the change rate and fluctuation range to calculate the environmental change value: ; is the current environmental change value of the environmental feature, All are preset non-zero scale factors; Obtain the change threshold corresponding to each environmental feature of the single channel, calculate the ratio of the environmental change value corresponding to each environmental feature to the change threshold according to the type of environmental feature, and use the maximum ratio result as the environmental change characteristic index of the current environment of the single channel.

4. The microwave electrically tunable filter control method for anti-interference according to claim 3, characterized in that: The logic for obtaining the environmental complexity index is as follows: Get multiple preset environmental feature data , where k is the dimension of the feature and t is the time index; Perform feature correlation analysis and calculate the correlation matrix R between various environmental features, where each element express Relevance: ; is the total number of environmental features; Shannon entropy is used to calculate the entropy value of environmental characteristics, indicating the uncertainty of information, and the complexity of the environment is measured by entropy value: ; represents the entropy value of the i-th environmental feature, express The probability distribution of The relationship between features is comprehensively considered through its entropy value and correlation matrix to obtain the local complexity of environmental features: ; represents the local complexity of the i-th environmental feature; The weighted sum of the local complexity of all environmental characteristics is the result of the environmental complexity index.

5. A microwave electrically tunable filter control system for anti-interference, implemented based on a microwave electrically tunable filter control method for anti-interference according to any one of claims 1 to 4, characterized in that: include: The interference signal confirmation module obtains the spectrum characteristic information of the microwave signal based on the preset frequency range, and confirms the type and source of the interference signal by monitoring the spectrum changes of the microwave signal in real time; The interference signal suppression module adopts a single-channel strategy during the initial adjustment of the microwave electronically tunable filter. According to the frequency characteristics of the monitored interference signal, it executes the preset suppression measures corresponding to the interference signal to ensure that the interference frequency is effectively filtered out. The feedback module uses a feedback control mechanism to monitor the performance of the microwave filter and generate different types of feedback signals during the adjustment process; The correction module corrects the strategy type through different types of feedback signals to ensure the stability and efficiency of anti-interference performance.

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