A combined anti-jamming method based on data link physical layer

Abstract

The application discloses a kind of based on data chain physical layer combination anti-interference method, belong to data chain communication technical field.The method includes the following steps: obtaining data chain signal, and identifying interference source, generate dynamic high-quality frequency point library;Based on dynamic high-quality frequency point library, generate initial frequency hopping sequence;After RS encoding data chain signal is spread spectrum processing;After spread spectrum processing data chain signal is frequency hopping modulation;Real-time monitoring communication channel quality, if triggering adaptive frequency selection condition, then adaptive frequency selection is carried out;The frequency hopping sequence after adjustment is issued to unmanned aerial vehicle.The application is combined spread spectrum, frequency hopping and adaptive frequency selection technology, and carries out frequency point quality evaluation and interference source prediction, effectively solve the deficiency of adaptive frequency selection in frequency point selection accuracy, switching speed and interference prediction ability in prior art, improve the anti-interference ability and reliability of communication.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data link communication, and particularly relates to a combined anti-interference method based on a data link physical layer. BACKGROUND

[0002] With the rapid development of modern communication technology, data links have been widely used in military and civilian fields. Data links are mainly used to realize information sharing and cooperative combat between different platforms, and their stability, reliability and anti-interference ability are crucial.

[0003] Existing data link systems mainly rely on a single anti-interference method, such as frequency hopping technology or spread spectrum technology, to deal with interference.

[0004] However, in the process of implementing the technical scheme of the present application, the present application has found that the above-mentioned technology at least has the following technical problems:

[0005] In the prior art, the frequency switching mode of the frequency hopping technology is relatively fixed, which is easy to be detected and interfered by the enemy; the anti-interference performance of the spread spectrum technology is limited in a strong interference environment. In addition, although the adaptive frequency selection method in the prior art can effectively avoid interference, it has problems such as inaccurate frequency selection, large switching delay, insufficient prediction ability of the interference source, etc., which increases the risk of communication interruption. SUMMARY

[0006] The present application provides a combined anti-interference method based on a data link physical layer, which solves the problems of inaccurate frequency selection, large switching delay and insufficient prediction ability of the interference source in the prior art adaptive frequency selection method, realizes intelligent and accurate selection of frequency points, rapid switching and effective prediction of the interference source, and thus improves the anti-interference ability and reliability of communication.

[0007] The present application provides a combined anti-interference method based on a data link physical layer, which includes the following steps: scanning the working frequency band by the transmitting end machine, acquiring the data link signal, identifying the interference source, and generating a dynamic high-quality frequency point library;

[0008] The step of acquiring the data link signal and identifying the interference source to generate a dynamic high-quality frequency point library includes:

[0009] The frequency spectrum sensing module of the transmitting end machine scans a plurality of frequency points in the working frequency band to acquire the data link signal feature information of each frequency point in real time;

[0010] The acquired data link signal feature information is analyzed to identify the interference source signal and acquire multiple data of the interference source signal;

[0011] Based on the multiple data of the interference source signal, the quality of each frequency point is evaluated, and the frequency points meeting the preset quality standard are screened out.

[0012] The screened frequency points are recorded as high-quality frequency points, and are summarized and arranged to generate a dynamic high-quality frequency point library;

[0013] The dynamic high-quality frequency point library is synchronized to the unmanned aerial vehicle terminal in real time;

[0014] Based on the dynamic high-quality frequency point library, the optimal frequency point set is selected, specifically, a certain number of high-quality frequency points are selected, sorted or combined to generate an initial frequency hopping sequence;

[0015] The data link signal is RS encoded, and the RS encoded data link signal is spread spectrum processed;

[0016] Based on the initial frequency hopping sequence, the spread spectrum processed data link signal is frequency hopping modulated;

[0017] The communication channel quality is monitored in real time, and if the adaptive frequency selection condition is triggered, adaptive frequency selection is performed to generate an adjusted frequency hopping sequence;

[0018] The adjusted frequency hopping sequence is sent to the unmanned aerial vehicle, and the unmanned aerial vehicle receives the frequency hopping signal according to the adjusted frequency hopping sequence to restore the original data link signal, completing the communication process.

[0019] Further, the adaptive frequency selection step includes:

[0020] A real-time spectrum map containing each frequency point is constructed, and the interference sources are analyzed to predict the interference behavior of the interference sources;

[0021] Based on the real-time spectrum map and the interference source analysis result, each frequency point is comprehensively evaluated, the priority of the frequency point is dynamically adjusted, and a backup frequency point library is formed;

[0022] Based on the interference source prediction result, pre-synchronization operation is performed in advance on the backup frequency point;

[0023] When the switching condition is triggered, the frequency point switching is performed based on the obtained physical data of the current frequency point, and the adaptive frequency selection is completed.

[0024] Further, the step of constructing a real-time spectrum map containing each frequency point includes:

[0025] The physical data of each frequency point is collected in real time by scanning the working frequency band by the broadband radio frequency sensor carried by the unmanned aerial vehicle;

[0026] The collected physical data of each frequency point is bound with geographic information system coordinates to generate a frequency-space joint distribution matrix;

[0027] The historical interference event database is fused to label the activity track of the persistent interference source and the spatio-temporal distribution hot area of the instantaneous interference;

[0028] Construct a three-dimensional spectrum map of each frequency point with a timestamp.

[0029] Further, the step of analyzing the interference source and predicting the interference behavior of the interference source comprises:

[0030] Extracting time-frequency domain features of the interference source, the time-frequency domain features comprising modulation distortion degree, power surge slope, and spectral spread width;

[0031] Identifying deceptive jamming according to the modulation distortion degree and determining the strength level of suppressive jamming according to the power surge slope;

[0032] Calculating the interference coverage radius based on the spectral spread width and positioning the physical location of the interference source in combination with the direction of arrival estimation;

[0033] Inputting the current time-frequency domain features into a long short-term memory network prediction model to predict the interference migration path and the activity probability of the interference source.

[0034] Further, the steps of identifying deceptive jamming according to the modulation distortion degree and determining the strength level of suppressive jamming according to the power surge slope comprise:

[0035] Extracting modulation signal feature parameters from the interference source, including carrier frequency, modulation index, and symbol rate;

[0036] Analyzing the difference between the extracted modulation signal feature parameters and the preset ideal modulation parameters, and calculating the modulation distortion degree through a modulation distortion degree formula:

[0037] ;

[0038] In the formula, is the modulation distortion degree, is the actual modulation parameter, is the ideal modulation parameter, is the average value of the modulation parameter, is the number of modulation parameters;

[0039] When is not less than the preset distortion degree threshold, it is determined that there is deceptive jamming;

[0040] Real-time monitoring of data link signal power, recording the occurrence time of power surge event and the peak time ;

[0041] Calculating the power surge time and recording the power change amount , wherein represents the power peak value, represents the baseline power before the power surge.

[0042] Further, the step of calculating the interference coverage radius based on the spectrum diffusion width comprises:

[0043] According to the power surge slope formula, the power surge slope is calculated:

[0044]

[0045] In the formula, is the power surge slope;

[0046] When is not less than the preset slope threshold, it is determined as high-intensity suppression interference;

[0047] The interference coverage radius is calculated by the interference coverage radius formula:

[0048]

[0049] In the formula, is the interference coverage radius, is the interference signal power, is the propagation loss at the reference distance, is the reference distance, is the propagation loss exponent, is the spectrum diffusion width.

[0050] Further, the step of comprehensively evaluating each frequency point comprises:

[0051] By combining the spectrum map and the interference source analysis result through the machine learning algorithm, a frequency point evaluation index system is constructed to comprehensively evaluate each frequency point;

[0052] The evaluation index system includes the current interference intensity, the expected interference probability, the transmission loss, and the multipath effect index of the frequency point;

[0053] Based on the comprehensive evaluation result, the priority of the frequency point is dynamically adjusted;

[0054] The frequency points are arranged through the comprehensive evaluation result, and the first K frequency points are selected to form a backup frequency point library, and K is not 0.

[0055] Further, the step of monitoring the communication channel quality in real time comprises:

[0056] The interference power value, the signal-to-noise ratio, and the bit error rate of the current communication frequency point are collected in real time by the signal analysis unit of the unmanned aerial vehicle terminal;

[0057] The collected interference power value, signal-to-noise ratio, and bit error rate are compared with the historical quality baseline of the corresponding frequency point in the dynamic high-quality frequency point library, and the channel quality deviation is calculated;

[0058] ​​When the interference power value is not less than a preset interference threshold, or the signal-to-noise ratio continuously decreases to a preset signal-to-noise ratio threshold, or the error code rate mutation value reaches a preset mutation threshold, it is determined that the adaptive frequency selection condition is triggered.

[0059] If the channel quality deviation degree is not less than a preset deviation threshold, it is determined that the adaptive frequency selection condition is triggered.

[0060] Further, the step of calculating the channel quality deviation degree comprises:

[0061] The deviation degrees of the interference power value, the signal-to-noise ratio and the error code rate are calculated:

[0062] ;

[0063] ;

[0064] ;

[0065] In the formula, is the interference power deviation degree, is the signal-to-noise ratio deviation degree, is the error code rate deviation degree, is the interference power value, is the signal-to-noise ratio, is the error code rate, is the historical interference power average value, is the historical signal-to-noise ratio average value, is the historical error code rate average value;

[0066] The channel quality deviation degree is obtained through the channel quality deviation degree formula:

[0067] ;

[0068] In the formula, is the channel quality deviation degree, , , all are weight factors.

[0069] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0070] 1. By combining adaptive frequency selection with machine learning algorithms, high-quality frequency points are intelligently screened, so as to accurately identify and avoid interference frequency points, realize intelligent and accurate selection of frequency points, and effectively solve the problem of inaccurate adaptive frequency selection in the prior art.

[0071] 2. Through pre-synchronization operation, the synchronization of the backup frequency point is completed in advance, so that the frequency point can be quickly switched when interference occurs, realizing fast frequency switching, effectively solving the problem of large switching delay of adaptive frequency selection in the existing technology, and improving the real-time performance and reliability of communication.

[0072] 3. Through multi-dimensional spectrum environment perception and interference source behavior prediction, the long short-term memory network prediction model is used to predict the interference behavior of the interference source in advance and achieve effective prediction of the interference source. This effectively solves the problem of insufficient interference source prediction ability of adaptive frequency selection in existing technologies and improves anti-interference capability and communication quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A flowchart of a combined anti-interference method based on the data link physical layer is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0074] The present embodiment addresses the shortcomings of existing adaptive frequency selection technologies in frequency selection accuracy, switching speed, and interference prediction capabilities by providing a combined anti-interference method based on the data link physical layer. By comprehensively evaluating frequency quality, combining pre-synchronization operations to achieve rapid frequency switching, and using a long-short-term memory network prediction model to accurately predict interference source behavior, intelligent and precise frequency selection and rapid switching are achieved, improving the system's adaptability to complex electromagnetic environments and the reliability of the communication link.

[0075] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0076] like Figure 1 FIG2 is a flowchart of a combined anti-interference method based on the data link physical layer provided by an embodiment of the present application. The method includes the following steps: scanning the working frequency band by a transmitter, acquiring the data link signal, identifying the interference source, and generating a dynamic high-quality frequency point library;

[0077] The steps for acquiring data link signals, identifying interference sources, and generating a dynamic high-quality frequency library include:

[0078] The spectrum sensing module of the transmitter scans multiple frequency points within the working frequency band and obtains the data link signal characteristic information of each frequency point in real time;

[0079] Analyze the acquired data link signal characteristic information, identify the interference source signal, and obtain multiple data of the interference source signal;

[0080] Based on the acquired multiple data of the interference source signal, quality of each frequency point is evaluated, and the step of quality evaluation includes: through the spectrum sensing module of the transmitting terminal, data link signal characteristic information of each frequency point in the working frequency band is monitored and analyzed in real time, and the interference source signal and its multiple data are identified; through the preset frequency point quality standard, performance of each frequency point under the interference condition is evaluated, and the frequency point meeting the preset quality standard is screened out.

[0081] The screened frequency point is recorded as a high-quality frequency point, and is summarized to form a dynamic high-quality frequency point library;

[0082] The dynamic high-quality frequency point library is synchronized to the unmanned aerial vehicle terminal in real time;

[0083] Based on the dynamic high-quality frequency point library, an optimal frequency point set is selected, specifically, a certain number of high-quality frequency points are selected, sorted or combined to generate an initial frequency hopping sequence;

[0084] The step of generating the initial frequency hopping sequence includes: selecting a certain number of high-quality frequency points from the dynamic high-quality frequency point library to form a high-quality frequency point set; according to a preset frequency hopping rule or algorithm, the selected high-quality frequency point set is sorted or combined, and the sorted or combined frequency point set is determined as the initial frequency hopping sequence;

[0085] The data link signal is RS encoded, and the RS encoded data link signal is spread spectrum processed;

[0086] Based on the initial frequency hopping sequence, the spread spectrum processed data link signal is frequency hopping modulated;

[0087] The communication channel quality is monitored in real time, and if the adaptive frequency selection condition is triggered, that is, the interference power of the detected interference source is not less than the preset interference threshold, or the signal-to-noise ratio continuously decreases to the preset signal-to-noise ratio threshold, or the error code rate mutation value reaches the preset mutation threshold, adaptive frequency selection is performed to generate an adjusted frequency hopping sequence;

[0088] The adjusted frequency hopping sequence is sent to the unmanned aerial vehicle, and the unmanned aerial vehicle receives the frequency hopping signal according to the adjusted frequency hopping sequence to restore the original data link signal, and completes the communication process.

[0089] Further, the spectrum sensing module of the transmitting terminal acquires physical data of the current frequency point in real time, including the interference intensity and communication quality of the frequency point, and senses the electromagnetic environment, constructs a real-time spectrum map containing the interference intensity, signal quality and surrounding environment characteristics of each frequency point, and dynamically updates; at the same time, the interference source is analyzed, the type, position and interference mode of the main interference source are identified through signal analysis algorithm, and the interference behavior is predicted;

[0090] Based on the machine learning algorithm combined with the spectrum map and the analysis result of the interference source, each frequency point is comprehensively evaluated considering the current interference intensity, expected interference probability, transmission loss, multipath effect and other indicators of the frequency point. Based on the comprehensive evaluation result, the priority of the frequency point is dynamically adjusted, and the frequency points with less interference, high transmission quality and matching communication service priority are preferentially selected. Meanwhile, the diversity and dispersion of the frequency points are considered, and a backup frequency point library is formed;

[0091] Based on the prediction result of the interference source, pre-synchronization operation is performed on the backup frequency point in advance, that is, the receiver and transmitter of the backup frequency point exchange and calibrate the synchronization signal in advance while the current frequency point is communicating;

[0092] The communication channel quality is monitored in real time. If the adaptive frequency selection condition is triggered, adaptive frequency selection is performed, the frequency point is switched, and adaptive frequency selection is completed. Since the pre-synchronization operation has been completed, the switching process is almost seamless and will not cause obvious communication interruption or data loss.

[0093] Further, the step of constructing a real-time spectrum map containing each frequency point includes:

[0094] The physical data of each frequency point is collected in real time by scanning the working frequency band by a broadband radio frequency sensor carried by a drone. The physical data includes signal power intensity, signal-to-noise ratio and multipath fading coefficient;

[0095] The collected physical data of each frequency point is bound with geographic information system coordinates to generate a frequency-space joint distribution matrix;

[0096] The historical interference event database is fused to label the activity track of the persistent interference source and the spatio-temporal distribution hot zone of the transient interference;

[0097] A three-dimensional spectrum map with a timestamp for each frequency point is constructed.

[0098] Further, the step of analyzing the interference source and predicting the interference behavior of the interference source includes:

[0099] The time-frequency domain features of the interference source are extracted, including modulation distortion degree, power surge slope and spectrum spread width;

[0100] The spoofing interference is identified according to the modulation distortion degree, and the suppression interference intensity level is determined according to the power surge slope;

[0101] The interference coverage radius is calculated based on the spectrum spread width, and the physical position of the interference source is located combined with the direction of arrival estimation;

[0102] The current time-frequency domain features are input into a long short-term memory network prediction model to predict the interference migration path and activity probability of the interference source.

[0103] Furthermore, the steps of identifying deceptive interference according to the modulation distortion and determining the intensity level of suppression interference according to the power surge slope include:

[0104] Extract modulation signal characteristic parameters from interference sources, including carrier frequency, modulation index, and symbol rate;

[0105] The difference between the extracted modulation signal characteristic parameters and the preset ideal modulation parameters is analyzed, and the modulation distortion is calculated using the modulation distortion formula:

[0106] ;

[0107] Where, is the modulation distortion, is the actual modulation parameter, is the ideal modulation parameter, is the average value of the modulation parameter, is the number of modulation parameters;

[0108] when When the distortion is not less than the preset threshold, it is determined to be deceptive interference;

[0109] Real-time monitoring of data link signal power and recording of power surge events and peak time ;

[0110] Calculating power surge time , and record the power change ,in Indicates the peak power, Represents the baseline power before the power surge.

[0111] Furthermore, the step of calculating the interference coverage radius based on the spectrum diffusion width includes:

[0112] Calculate the power surge slope according to the power surge slope formula:

[0113] ;

[0114] Where, is the power surge slope;

[0115] when When the slope is not less than the preset threshold, it is determined to be high-intensity suppression interference;

[0116] The interference coverage radius is calculated using the interference coverage radius formula:

[0117] ;

[0118] Where, a reference distance, a reference distance, a reference distance, a reference distance, a reference distance, a reference distance.

[0119] Further, the step of comprehensively evaluating each frequency point comprises:

[0120] By combining the spectrum map and the interference source analysis result through a machine learning algorithm, a frequency point evaluation index system is constructed to comprehensively evaluate each frequency point;

[0121] The evaluation index system comprises the current interference intensity, the expected interference probability, the transmission loss, and the multipath effect index of the frequency point;

[0122] Based on the comprehensive evaluation result, the priority of the frequency point is dynamically adjusted;

[0123] The frequency points are arranged through the comprehensive evaluation result, and the first K frequency points are selected to form a backup frequency point library, and K is not 0.

[0124] Further, the step of monitoring the communication channel quality in real time comprises:

[0125] The interference power value, the signal-to-noise ratio, and the bit error rate of the current communication frequency point are collected in real time by the signal analysis unit of the unmanned aerial vehicle terminal;

[0126] The collected interference power value, signal-to-noise ratio, and bit error rate are compared with the historical quality baseline of the corresponding frequency point in the dynamic high-quality frequency point library to calculate the channel quality deviation degree;

[0127] When the interference power value is not less than the preset interference threshold, or the signal-to-noise ratio continuously decreases to the preset signal-to-noise ratio threshold, or the bit error rate mutation value reaches the preset mutation threshold, it is determined that the adaptive frequency selection condition is triggered;

[0128] If the channel quality deviation degree is not less than the preset deviation threshold, it is determined that the adaptive frequency selection condition is triggered.

[0129] Further, the step of calculating the channel quality deviation degree comprises:

[0130] The deviation degrees of the interference power value, the signal-to-noise ratio, and the bit error rate are calculated:

[0131] ;

[0132] ;

[0133] ;

[0134] wherein, a deviation of interference power, a deviation of signal to noise ratio, a deviation of bit error rate, an interference power value, a signal to noise ratio, a bit error rate, a historical interference power average, a historical signal to noise ratio average, a historical bit error rate average;

[0135] The channel quality deviation is obtained by a channel quality deviation formula:

[0136]

[0137] wherein, is the channel quality deviation, are weight factors.

[0138] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, apparatus, or computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a "circuit" or "module." Furthermore, the present application can take the form of a computer program product on one or more computer readable storage medium (s) having computer readable program code embodied in the medium.

[0139] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 The flowchart illustrations and / or block diagrams Figure 1 means for carrying out the functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0140] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flowchart illustrations and / or block diagrams block or blocks. Figure 1 The flowchart illustrations and / or block diagrams Figure 1 ​​​the function specified in the one or more blocks.

[0141] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide a process for implementing the flowchart Figure 1 the flowchart or flowcharts and / or a block Figure 1 the steps of the function specified in the one or more blocks.

[0142] Although preferred embodiments of the application have been described, those skilled in the art will recognize that additional modifications and variations can be made thereto without departing from the spirit and scope of the application. It is therefore intended that the appended claims cover all such modifications and variations as fall within the scope of the application.

[0143] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore intended that the invention be covered within the scope of the appended claims, and their equivalents.

Claims

1. A combined anti-interference method based on the data link physical layer, characterized in that: The following steps are involved: Scan the operating frequency band through the transmitter, obtain data link signals, identify interference sources, and generate a dynamic high-quality frequency point library; The steps for acquiring data link signals, identifying interference sources, and generating a dynamic high-quality frequency library include: The spectrum sensing module of the transmitter scans multiple frequency points within the working frequency band and obtains the data link signal characteristic information of each frequency point in real time; Analyzing the acquired data link signal characteristic information, identifying interference source signals therein, and acquiring multiple data of the interference source signals; Based on the multiple data of the interference source signal, the quality of each frequency point is evaluated and the frequency points that meet the preset quality standards are selected; The selected frequency points are recorded as high-quality frequency points, summarized and sorted, and a dynamic high-quality frequency point library is generated; Synchronize the dynamic high-quality frequency point library to the drone terminal in real time; Based on the dynamic high-quality frequency library, the optimal frequency set is selected. Specifically, a certain number of high-quality frequencies are selected, sorted or combined, and an initial frequency hopping sequence is generated. Performing RS encoding on the data link signal and performing spread spectrum processing on the RS-encoded data link signal; Performing frequency hopping modulation on the data link signal after the spread spectrum processing based on the initial frequency hopping sequence; Monitor the communication channel quality in real time. If the adaptive frequency selection condition is triggered, adaptive frequency selection is performed to generate an adjusted frequency hopping sequence. The adjusted frequency hopping sequence is sent to the UAV, which receives the frequency hopping signal according to the adjusted frequency hopping sequence, restores the original data link signal, and completes the communication process.

2. A data link physical layer combined anti-interference method according to claim 1, characterized in that: The steps of adaptive frequency selection include: Build a real-time spectrum map containing each frequency point, analyze the interference source, and predict the interference behavior of the interference source; Based on real-time spectrum maps and interference source analysis results, each frequency point is comprehensively evaluated, the priority of the frequency points is dynamically adjusted, and a backup frequency point library is formed; Based on the interference source prediction results, pre-synchronization operation is performed on the backup frequency point in advance; By obtaining the physical data of the current frequency point, when the switching condition is triggered, the frequency point is switched to complete the adaptive frequency selection.

3. A combined anti-interference method based on the data link physical layer as claimed in claim 2, characterized in that: The steps to build a real-time spectrum map containing each frequency point include: The broadband RF sensor carried by the drone scans the working frequency band and collects physical data of each frequency point in real time; Bind the collected physical data of each frequency point with the geographic information system coordinates to generate a frequency-space joint distribution matrix; Integrate the historical interference event database to mark the activity tracks of persistent interference sources and the temporal and spatial distribution hotspots of instantaneous interference; Construct a three-dimensional spectrum map of each frequency point with a time stamp.

4. The data link physical layer combined anti-interference method according to claim 2, characterized in that: The steps for analyzing the interference source and predicting its interference behavior include: Extracting time-frequency domain features of the interference source, wherein the time-frequency domain features include modulation distortion, power surge slope, and spectrum spread width; Identify deceptive interference based on modulation distortion, and determine the intensity level of suppressive interference based on the power surge slope; The interference coverage radius is calculated based on the spectrum diffusion width, and the physical location of the interference source is estimated by combining the direction of arrival. The current time-frequency domain features are input into the long short-term memory network prediction model to predict the interference migration path and activity probability of the interference source.

5. The data link physical layer combined anti-interference method according to claim 4, characterized in that: The steps of identifying deceptive interference based on modulation distortion and determining the intensity level of suppressive interference based on the power surge slope include: Extract modulation signal characteristic parameters from interference sources, including carrier frequency, modulation index, and symbol rate; The difference between the extracted modulation signal characteristic parameters and the preset ideal modulation parameters is analyzed, and the modulation distortion is calculated using the modulation distortion formula: ； Where, is the modulation distortion, is the actual modulation parameter, is the ideal modulation parameter, is the average value of the modulation parameter, is the number of modulation parameters; when When the distortion is not less than the preset threshold, it is determined to be deceptive interference; Real-time monitoring of data link signal power and recording of power surge events and peak time ; Calculating power surge time , and record the power change ,in Indicates the peak power, Represents the baseline power before the power surge.

6. The data link physical layer combined anti-interference method according to claim 5, characterized in that: The steps for calculating the interference coverage radius based on the spectrum diffusion width include: Calculate the power surge slope according to the power surge slope formula: ； Where, is the power surge slope; when When the slope is not less than the preset threshold, it is determined to be high-intensity suppression interference; The interference coverage radius is calculated using the interference coverage radius formula: ； Where, is the interference coverage radius, is the interference signal power, is the propagation loss at the reference distance, is the reference distance, is the propagation loss index, is the spectrum diffusion width.

7. The data link physical layer combined anti-interference method according to claim 2, characterized in that: The steps for comprehensive evaluation of each frequency point include: By combining the spectrum map and interference source analysis results with a machine learning algorithm, a frequency point evaluation index system is constructed to conduct a comprehensive evaluation of each frequency point. The evaluation index system includes the current interference intensity of the frequency point, the expected interference probability, the transmission loss, and the multipath effect index; Dynamically adjust the frequency priority based on comprehensive evaluation results; Arrange the frequencies based on the comprehensive evaluation results, and select the top K frequencies to form a backup frequency library, where K is not 0.

8. The data link physical layer combined anti-interference method according to claim 1, characterized in that: The steps for real-time monitoring of communication channel quality include: The signal analysis unit of the drone terminal collects the interference power value, signal-to-noise ratio and bit error rate of the current communication frequency in real time; Compare the collected interference power values, signal-to-noise ratio, and bit error rate with the historical quality baseline of the corresponding frequency points in the dynamic high-quality frequency point library to calculate the channel quality deviation; When the interference power value is not less than the preset interference threshold, or the signal-to-noise ratio continuously drops to the preset signal-to-noise ratio threshold, or the bit error rate mutation value reaches the preset mutation threshold, it is determined that the adaptive frequency selection condition is triggered; If the channel quality deviation is not less than the preset deviation threshold, it is determined that the adaptive frequency selection condition is triggered.

9. The data link physical layer combined anti-interference method according to claim 8, characterized in that: The steps of calculating the channel quality deviation include: Calculate the deviation of interference power value, signal-to-noise ratio and bit error rate: ； ； ； Where, is the interference power deviation, is the signal-to-noise ratio deviation, is the bit error rate deviation, is the interference power value, is the signal-to-noise ratio, is the bit error rate, is the historical interference power mean, is the historical mean signal-to-noise ratio, is the historical bit error rate mean; The channel quality deviation is obtained by the channel quality deviation formula: ； Where, is the channel quality deviation, 、 、 are all weight factors.

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