An anti-UAV jamming system and method based on radio cracking technology

Abstract

The invention discloses an anti-UAV interference system and method based on radio cracking technology, which relate to the technical field of UAVs. The anti-UAV interference system comprises an environmental monitoring management module, a signal interference identification module, an instantaneous interference suppression module, an intermittent interference management module, a continuous interference countermeasure module, an effect evaluation module and a feedback adjustment module. The technical key points are as follows: the system aims to use radio cracking technology to perform efficient and intelligent anti-interference processing on various types of interference faced by UAV signals, so as to ensure the stable flight and control of UAVs in complex electromagnetic environments. The system integrates multiple functional modules, and adopts different strategies for instantaneous, intermittent and continuous interference, so as to achieve accurate and rapid anti-interference adjustment. The stability and reliability of UAVs in complex electromagnetic environments are significantly improved, and a strong guarantee is provided for the safe flight and efficient operation of UAVs.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to an anti-UAV jamming system and method based on radio cracking technology. Background Art

[0002] A drone is an unmanned aerial vehicle that is controlled by a radio remote control device and a self-contained program control device. There are many types of drones, which can be divided into fixed-wing drones, multi-rotor drones, unmanned helicopters, unmanned airships, etc. according to their structure; they can be divided into military drones and civilian drones according to their application areas. Military drones are mostly used for reconnaissance, strike, communication relay and other tasks, requiring high sensitivity, high flight speed and intelligence; civilian drones are widely used in agricultural monitoring, building inspection, environmental monitoring and other fields, focusing on ease of operation and cost-effectiveness; drone systems are usually composed of subsystems such as flight platforms, mission payloads, data links and ground control, involving a number of high-tech technologies such as sensor technology, communication technology, and intelligent control technology; with the continuous development of technology, drones will play an important role in more fields.

[0003] The existing application publication number is CN111162867A, and the document named "Anti-UAV Interference System and Its Implementation Method" points out that: the anti-UAV interference system includes a satellite monitoring unit, a trajectory analysis unit, a dynamic evaluation unit, a central processing unit, a warning prompt unit, a signal interference unit, a communication interception unit, a hijacking countermeasure unit, an emergency processing unit and a launch control unit. The trajectory analysis unit and the dynamic evaluation unit are connected to the satellite monitoring unit through a GPRS network, and the central processing unit is electrically connected to the trajectory analysis unit, the dynamic evaluation unit, the warning prompt unit, the signal interference unit, the communication interception unit, the hijacking countermeasure unit and the emergency processing unit respectively; the trajectory analysis unit and the dynamic evaluation unit are used to simultaneously analyze whether the UAV has suspicious behavior, and the central processing unit takes corresponding measures against the UAV only when it receives two suspicious signals at the same time, thereby improving the accuracy of the interference system judgment and making the anti-UAV interference system more mature; although it can further accurately judge the accuracy of the interference, it does not give specific measures or countermeasures, or the means of dealing with signal interference are single.

[0004] Combining the above documents and existing technologies, traditional UAVs are susceptible to interference in complex electromagnetic environments, and the anti-interference ability and signal reception quality of UAVs cannot be further improved. Due to the different specific circumstances of interference, the measures that need to be taken must be different, so as to ensure the anti-interference effect. However, the traditional means of dealing with interference are single, and although they can play a certain role, their effectiveness and efficiency cannot be further guaranteed. Therefore, traditional anti-interference strategies have the problems of being fixed, lacking flexibility, and being difficult to adapt to complex and changing interference environments. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides an anti-UAV interference system and method based on radio cracking technology. The system uses radio cracking technology to perform efficient and intelligent anti-interference processing on various types of interference faced by UAV signals, ensuring the stable flight and control of UAVs in complex electromagnetic environments. The system integrates multiple functional modules and adopts different strategies for instantaneous, intermittent and continuous interference to achieve accurate and rapid anti-interference adjustment, solving the problems raised in the background technology.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] An anti-UAV jamming system based on radio cracking technology, comprising:

[0010] The environmental monitoring management module obtains the meteorological data of the area where the target UAV is located. When the meteorological data contains lightning signals, the plan adjustment mechanism is executed to select any adjacent area without lightning signals as the flight path of the target UAV.

[0011] The signal interference identification module monitors the signals received by the target drone’s GNSS, analyzes the signal characteristics with machine learning algorithms, and identifies and classifies the interference types, including instantaneous, intermittent, and continuous.

[0012] The transient interference suppression module triggers the filtering adjustment strategy when the interference type is identified as transient;

[0013] The intermittent interference management module triggers the signal recovery strategy when the interference type is identified as intermittent;

[0014] The continuous interference countermeasure module triggers the receiving and processing strategy when it identifies that the interference type is continuous;

[0015] The effect evaluation module, after executing any strategy, obtains the evaluation data set within the set period T, builds the evaluation calculation model, and generates the estimated value of the operation effect under the corresponding strategy based on the preprocessed evaluation data set;

[0016] The feedback adjustment module, upon receiving the estimated value of the operating effect, calibrates the corresponding strategy, compares the estimated value of the operating effect with the preset estimated threshold, and chooses whether to execute the corresponding strategy optimization instruction based on the comparison result.

[0017] Furthermore, the process of analyzing signal features in combination with machine learning algorithms is as follows:

[0018] Signal processing: pre-processing of the captured signal, including at least bandpass filtering and amplification;

[0019] Feature extraction: extracting features reflecting signal characteristics from the preprocessed signal, including at least power spectrum density, frequency offset and phase change characteristics;

[0020] Interference identification: Use machine learning algorithms to classify and identify the extracted features, and adopt decision tree algorithms for training. During the training process, a data set containing different types of interference signals is used for training. During real-time monitoring, the extracted features are input into the trained machine learning model to identify the interference type of the current signal.

[0021] Furthermore, the steps of the triggered filter adjustment strategy are as follows:

[0022] Interference characteristic identification: obtain the characteristics of instantaneous interference and run the filter carried by the target drone;

[0023] The characteristics of the instantaneous interference include at least interference frequency and bandwidth;

[0024] Filter parameter setting: Initially set the filter parameters based on the characteristics of instantaneous interference;

[0025] The filter parameters of the initial setting filter include cut-off frequency, bandwidth and attenuation;

[0026] The cut-off frequency is equal to the interference frequency in the characteristic of instantaneous interference; the bandwidth in the filtering parameter is equal to the bandwidth in the characteristic of instantaneous interference; the initial setting value of the attenuation amount is X, and X>0.

[0027] Furthermore, the triggered signal recovery strategy steps are as follows:

[0028] Using wavelet transform signal processing technology, the interfered signal segment is reconstructed to restore the original signal;

[0029] Time window technology is used to select interference-free time periods for signal reception.

[0030] Furthermore, the triggered receiving and processing strategy steps are:

[0031] Frequency hopping technology is used to enable the GNSS receiver to switch between several different frequencies.

[0032] Furthermore, the evaluation data set includes: the strength and bit error rate of the communication signal, the height and speed of the target drone, and the positioning accuracy; the preprocessing process of the evaluation data set is as follows:

[0033] At the same time t, the strength and bit error rate of the communication signal are extracted, and after dimensionless processing, weighted calculation is performed to generate the communication quality index. The formula is:

[0034] ;

[0035] In the formula, represents the communication quality index, t=1, 2, ..., n, n=T, a1, a2 are weight coefficients, and their value range is [0, 1]. and Respectively represent the strength and bit error rate of the communication signal;

[0036] At the same time t, the height and speed of the target UAV are extracted, and after dimensionless processing, weighted calculation is performed to generate a stability index based on the formula:

[0037] ;

[0038] In the formula, represents the stability index, b1 and b2 are weight coefficients, and their value ranges are [0, 1]. and Represent the height and speed of the target UAV respectively.

[0039] Furthermore, the process of running the evaluation calculation model is as follows:

[0040] Fluctuation calculation: Summarize the corresponding communication quality index, stability index and positioning accuracy at each time t within the period T, extract the maximum and minimum values ​​of the communication quality index, the maximum and minimum values ​​of the stability index, and the maximum and minimum values ​​of the positioning accuracy, and calculate the communication fluctuation value, stability fluctuation value and accuracy fluctuation value according to the formula:

[0041] ;

[0042] ;

[0043] ;

[0044] Where Csi, Mad and Lsb represent the communication fluctuation value, stability fluctuation value and accuracy fluctuation value respectively. and Respectively represent the maximum and minimum values ​​of the communication quality index, and Represent the maximum and minimum values ​​of the stability index, respectively. and Respectively represent the maximum and minimum values ​​of positioning accuracy;

[0045] Operation effect calculation: Based on the normalized communication fluctuation value, stability fluctuation value, and accuracy fluctuation value, a nonlinear calculation formula is established to generate an estimated operation effect value:

[0046] ;

[0047] Where PEP represents the estimated value of the operating effect, α, β and γ are adjustment coefficients, and their value ranges are all [0, 1], Csi_th represents the threshold parameter, and 0<Csi_th<2.

[0048] Furthermore, the process of comparing the estimated value of the running effect with the preset estimated threshold is as follows:

[0049] When the estimated value of the running effect exceeds the estimated threshold, no response action is taken;

[0050] When the estimated value of the running effect does not exceed the estimated threshold, the corresponding strategy optimization instruction is executed;

[0051] Among them, when the calibrated corresponding strategy is the trigger filter adjustment strategy, the corresponding strategy optimization instruction content executed is:

[0052] Reduce the bandwidth of the filter according to a preset amount; the basis of the setting amount is: 1 / 3*dk_0; where dk represents the initial bandwidth; simultaneously increase the attenuation according to a preset amount, the basis of the preset amount is: 1 / 3*js_0; where js represents the initial attenuation;

[0053] When the calibrated corresponding strategy is the trigger signal recovery strategy, the corresponding strategy optimization instruction content executed is:

[0054] The sliding average method is used to analyze whether the interfered signal segment is smooth;

[0055] If it is smooth, the number of decomposition layers of the wavelet transform is reduced according to the interval value L;

[0056] If it is not smooth, increase the number of decomposition layers of the wavelet transform according to the interval value L;

[0057] Synchronously reduce the time window according to the set reduction amount, and the reduction amount is based on: 10%*kc_0; where kc_0 represents the original window;

[0058] When the calibrated corresponding strategy is the trigger receiving processing strategy, the corresponding strategy optimization instruction content executed is:

[0059] The length of the frequency hopping sequence is increased according to a preset increase amount, and the basis of the increase amount is: 10%*cd_0; wherein cd_0 represents the length of the current frequency hopping sequence.

[0060] An anti-UAV interference method based on radio cracking technology includes the following steps:

[0061] S1. Obtain the meteorological data of the area where the target UAV is located. When the meteorological data contains a lightning signal, execute the plan adjustment mechanism and select any adjacent area without a lightning signal as the flight path of the target UAV.

[0062] S2. Monitor the signals received by the target UAV GNSS, analyze the signal characteristics with machine learning algorithms, and identify and classify the interference types; the interference types include instantaneous, intermittent, and continuous;

[0063] When the interference type is identified as transient, the filter adjustment strategy is triggered;

[0064] When the interference type is identified as intermittent, the signal recovery strategy is triggered;

[0065] When the interference type is identified as continuous, the receiving and processing strategy is triggered;

[0066] S3. After executing any strategy, obtain the evaluation data set within the set period T, build the evaluation calculation model, and generate the estimated value of the operation effect under the corresponding strategy based on the preprocessed evaluation data set;

[0067] S4. Upon receiving the estimated value of the operating effect, calibrate the corresponding strategy, and compare the estimated value of the operating effect with a preset estimated threshold value, and choose whether to execute the corresponding strategy optimization instruction based on the comparison result.

[0068] (III) Beneficial effects

[0069] The present invention provides an anti-UAV jamming system and method based on radio cracking technology, which has the following beneficial effects:

[0070] (1) This solution achieves effective management and countermeasures against different types of interference to UAV GNSS signals;

[0071] In response to instantaneous interference, the filter adjustment strategy is triggered and the filter parameters are accurately set to effectively suppress interference signals and ensure clear signal transmission. For intermittent interference, the signal recovery strategy is used to reconstruct the signal through wavelet transform technology and select interference-free time periods in combination with time window technology to receive signals, which significantly improves the stability and accuracy of the signal. In the face of continuous interference, the frequency hopping technology of the receiving and processing strategy enables the GNSS receiver to flexibly switch between multiple frequencies, effectively avoiding the impact of continuous interference. It not only solves the technical problem that drones are susceptible to interference in complex electromagnetic environments, but also greatly improves the anti-interference ability and signal reception quality of drones, ensuring the safe and stable flight and efficient operation of drones in various complex environments.

[0072] (2) After executing any anti-interference strategy, this solution can accurately obtain and preprocess the evaluation data set within a set period, and then generate an estimated value of the operation effect; by comprehensively considering multiple key factors such as communication signal quality, drone flight stability and positioning accuracy, a nonlinear evaluation model is established, which significantly improves the accuracy and sensitivity of the evaluation;

[0073] It effectively solves the problems of single evaluation index and linear evaluation results in traditional evaluation methods, and realizes a comprehensive, objective and accurate evaluation of the execution effect of UAV anti-interference strategy. This not only provides a scientific basis for optimizing UAV anti-interference strategy, further ensures the safe and stable flight of UAV in complex electromagnetic environment, but also provides guidance for subsequent optimization and adjustment actions.

[0074] (3) This scheme implements a dynamic feedback and adjustment mechanism for the execution effect of the anti-interference strategy;

[0075] This mechanism can intelligently trigger and execute corresponding strategy optimization instructions based on the comparison results of the estimated operating effect and the preset threshold, thereby continuously improving the effectiveness and adaptability of the anti-interference strategy;

[0076] Detailed optimization measures are designed for different strategies such as filter adjustment, signal recovery and reception processing, such as adjusting filter parameters, wavelet transform decomposition layers and time window size, and increasing the length of frequency hopping sequence. These measures are dynamically adjusted based on actual operation results to ensure the accuracy and effectiveness of the optimization direction.

[0077] This solution effectively solves the problems of fixedness, lack of flexibility and difficulty in adapting to complex and changing interference environments in traditional anti-interference strategies, significantly improves the stability and reliability of UAVs in complex electromagnetic environments, and provides strong guarantees for the safe flight and efficient operation of UAVs; at the same time, the solution also gradually approaches the optimal anti-interference effect through continuous iterative optimization, demonstrating good self-learning and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a modular schematic diagram of the anti-UAV jamming system in the present invention;

[0079] Figure 2 The figure is a flowchart of the overall steps of the anti-UAV interference method in the present invention. DETAILED DESCRIPTION

[0080] 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.

[0081] Embodiment 1:

[0082] See also Figure 1 ,This embodiment provides an anti-UAV jamming system based on radio cracking technology;

[0083] This system aims to use radio cracking technology to efficiently and intelligently deal with various types of interference faced by drone signals, ensuring the stable flight and control of drones in complex electromagnetic environments. The system integrates multiple functional modules and adopts different strategies for instantaneous, intermittent and continuous interference to achieve accurate and rapid anti-interference adjustment.

[0084] The anti-UAV jamming system includes various functional modules, namely, environmental monitoring and management module, signal jamming identification module, instantaneous jamming suppression module, intermittent jamming management module, continuous jamming countermeasure module, effect evaluation module and feedback adjustment module, forming a closed-loop system.

[0085] The functional modules and operation process are described as follows:

[0086] Environmental monitoring management module:

[0087] Obtain the meteorological data of the area where the target UAV is located. When the meteorological data contains lightning signals, the plan adjustment mechanism is executed to select any adjacent area without lightning signals as the flight path of the target UAV.

[0088] The purpose of setting the above-mentioned environmental monitoring management module is to eliminate the interference of environmental factors. As for signal interference, if the drone passes through a lightning area, it is easily interfered by lightning in the natural environment;

[0089] How to obtain meteorological data in the area: retrieve weather forecast data from the meteorological station;

[0090] Lightning signals are lightning warning signals, which are divided into three levels, represented by yellow, orange and red respectively;

[0091] Yellow: Lightning activity may occur within 6 hours, which may cause lightning disaster accidents. Orange: There is a high possibility of lightning activity within 2 hours, or the area has been affected by lightning activity and may continue, so the possibility of lightning disaster accidents is relatively high. Red: There is a very high possibility of lightning activity within 2 hours, or strong lightning activity has already occurred and may continue, so the possibility of lightning disaster accidents is very high.

[0092] Signal interference identification module:

[0093] Monitor the signals received by the target drone’s GNSS, analyze the signal characteristics with machine learning algorithms, and identify and classify the types of interference; the types of interference include instantaneous, intermittent, and continuous;

[0094] Among them, the signal received by the GNSS of the monitoring target drone is the signal capture:

[0095] Use a highly sensitive radio receiver, such as a software radio platform (e.g. SDR) or a dedicated GNSS receiver, to capture the signal received by the drone’s GNSS. The receiver should be set to continuous working mode to ensure that changes in the signal can be monitored in real time.

[0096] The process of analyzing signal features in combination with machine learning algorithms is as follows:

[0097] Signal processing: Pre-processing the captured signal, including bandpass filtering, amplification, digitization and other steps to remove noise and interference and improve the signal-to-noise ratio; digitize the signal using a pre-set (i.e. appropriate) sampling rate and quantization bit number to facilitate subsequent feature extraction and interference identification;

[0098] Feature extraction: Extract features that can reflect signal characteristics from the preprocessed signal, i.e., time domain and frequency domain features such as power spectrum density, frequency offset, and phase change; transform domain analysis techniques (such as Fourier transform, wavelet transform, etc.) can also be used to extract signal features in the transform domain;

[0099] Interference identification: Use machine learning algorithms to classify and identify the extracted features. Support vector machines (SVM), neural networks, and decision tree algorithms can be selected for training and optimization. During the training process, data sets containing different types of interference signals should be used for training to ensure that the algorithm can accurately identify different types of interference. During real-time monitoring, the extracted features are input into the trained machine learning model to quickly identify the interference type of the current signal.

[0100] Example:

[0101] Taking the example of a drone being subjected to electromagnetic interference during flight, assume that the receiver captures the following signal characteristics: the power spectral density of the signal shows an obvious peak in a certain frequency range, and the peak changes rapidly over time, which is caused by instantaneous interference; the frequency offset of the signal fluctuates periodically, and the fluctuation amplitude is large, which is caused by intermittent interference; the phase change of the signal is relatively stable, but the overall power level decreases, which is caused by continuous interference; by matching these features with the trained machine learning model, the interference type of the current signal can be quickly identified.

[0102] By adopting the above technical solutions, real-time monitoring and effective response to environmental factors, especially lightning signals, as well as accurate identification and classification of UAV GNSS signal interference are achieved;

[0103] This solution can not only retrieve data from the meteorological station to prevent lightning interference, dynamically adjust the flight path of the drone to avoid lightning areas, thus significantly reducing the impact of the natural environment on the flight safety of the drone; it can also use highly sensitive signal capture technology and advanced machine learning algorithms to conduct in-depth analysis of the signals received by the drone's GNSS, quickly and accurately identify various types of interference, such as instantaneous, intermittent, and continuous, and provide key information for subsequent interference suppression and flight control;

[0104] This comprehensive technical solution effectively solves the technical problem that UAVs are easily affected by meteorological and electromagnetic interference in complex environments, improves the flight stability and safety of UAVs, ensures their reliable and efficient operation in various application scenarios, and lays a solid technical foundation for the widespread application of UAVs.

[0105] Transient interference suppression module:

[0106] When the interference type is identified as transient, the filter adjustment strategy is triggered;

[0107] Among them, the triggered filter adjustment strategy is as follows:

[0108] Interference characteristic identification: obtain the characteristics of instantaneous interference and run the filter carried by the target drone;

[0109] Filter parameter setting: Initially set the filter parameters based on the characteristics of instantaneous interference;

[0110] Specifically, the characteristics of instantaneous interference include at least: interference frequency and bandwidth;

[0111] The type of filter carried by the target UAV is: band-stop filter or notch filter;

[0112] The filter parameters of the initial setting filter include: cut-off frequency, bandwidth and attenuation;

[0113] Among them, the cut-off frequency is equal to the interference frequency in the characteristic of instantaneous interference; the bandwidth in the filtering parameter is equal to the bandwidth in the characteristic of instantaneous interference; the initial setting value of the attenuation amount is X, and X>0;

[0114] Example:

[0115] Assume that the drone suddenly encounters instantaneous interference during flight, with an interference frequency of 1.5 GHz and a bandwidth of 10 MHz; at this time, the instantaneous interference suppression module will trigger the filtering adjustment strategy:

[0116] First, the characteristics of the transient interference are obtained and a band-stop filter is selected to suppress it;

[0117] The filter cutoff frequency is initially set to 1.5 GHz, the bandwidth is 10 MHz, and the attenuation is 30 dB;

[0118] Therefore, in the above example, the value of X is 30.

[0119] Intermittent Interference Management Module:

[0120] When the interference type is identified as intermittent, the signal recovery strategy is triggered;

[0121] Among them, the triggered signal recovery strategy is as follows:

[0122] Wavelet transform signal processing technology is used to reconstruct the interfered signal segment and restore the original signal; at the same time, time window technology is used to select interference-free time periods for signal reception;

[0123] Specifically, in terms of signal reconstruction, a signal processing technology such as wavelet transform or Kalman filtering may be used. In this embodiment, a wavelet transform signal processing technology is used. When the interference type is determined to be intermittent, the interfered signal segment is reconstructed by using the wavelet transform signal processing technology, and the original signal is restored by filtering out noise and interference components.

[0124] It should be noted that wavelet transform is suitable for analyzing non-stationary signals and can effectively separate noise and interference components in the signal; Kalman filtering is a recursive estimation algorithm that can predict and update the signal in real time to improve the accuracy and stability of the signal;

[0125] Time window technology is a method of receiving signals by selecting time periods with weak or no interference;

[0126] It is based on the different distribution characteristics of signals and interference in time, and avoids or reduces the impact of interference by setting a reasonable time window;

[0127] The implementation steps are as follows:

[0128] Interference monitoring: real-time monitoring of intermittent interference in the signal transmission environment, including the intensity, frequency and duration of interference; time window setting: according to the interference monitoring results, set the time window. Within the time window, the interference is weak or non-existent, so this time period can be selected for signal reception; signal reception and processing: receiving signals within the time window, and performing necessary processing and analysis to ensure the accuracy and reliability of the signal;

[0129] Example:

[0130] Assume that intermittent interference is detected, which is manifested as periodic interruptions in the signal transmission process; at this time, the intermittent interference management module will trigger the signal recovery strategy:

[0131] Signal reconstruction: Wavelet transform technology is used to reconstruct the interfered signal segment, and the original signal is restored by separating the noise and interference components in the signal. The signal quality after reconstruction is significantly improved and the bit error rate is significantly reduced.

[0132] Time window technology: At the same time, time window technology is used to select time periods with weak or no interference for signal reception. By monitoring the interference situation in real time, a reasonable time window is set and the signal is received within the time window. This method effectively avoids the impact of interference on signal transmission and improves signal stability and reliability.

[0133] Continuous interference countermeasure module:

[0134] When the interference type is identified as continuous, the receiving and processing strategy is triggered;

[0135] The triggered receiving and processing strategy is as follows:

[0136] Frequency hopping technology is used to switch the GNSS receiver between several different frequencies;

[0137] Specifically, frequency hopping technology is a method of reducing the risk of continuous interference by quickly switching signals between multiple frequencies; in GNSS (Global Navigation Satellite System) receivers, frequency hopping technology can enable them to quickly switch between different frequencies to avoid the frequency bands that are continuously interfered with;

[0138] The implementation steps are as follows:

[0139] Frequency hopping sequence generation: pre-generate a set of frequency hopping sequences, which includes multiple available frequency points and their switching order; receiver configuration: configure the frequency hopping sequence into the GNSS receiver so that it can switch according to the frequency indicated by the sequence.

[0140] Example:

[0141] Assume that continuous malicious interference is detected in the system, which is manifested as continuous blocking of signals on a specific frequency band; at this time, the continuous interference countermeasure module will trigger the receiving processing strategy:

[0142] Active interference suppression (optional): Deploy interference suppression filters to selectively attenuate or block the frequency band where the interference signal is located; at the same time, adjust the direction of the directional antenna to align it with the direction of the useful signal and avoid the direction of the interference signal; after these measures, the impact of the interference signal is significantly reduced, and the signal reception quality of the GNSS receiver is improved;

[0143] Frequency hopping technology: Enable frequency hopping technology in GNSS receivers to quickly switch between different frequencies; reduce the risk of interference by avoiding frequency bands that are continuously interfered with.

[0144] By adopting the above technical solutions, effective management and countermeasures of different types of interference to drone GNSS signals are achieved; for instantaneous interference, the interference signal is effectively suppressed by triggering the filter adjustment strategy and accurately setting the parameters using the band-stop or notch filter, ensuring clear signal transmission;

[0145] For intermittent interference, the signal recovery strategy is used to reconstruct the signal through wavelet transform technology and combine the time window technology to select the interference-free period to receive the signal, which significantly improves the stability and accuracy of the signal;

[0146] In the face of continuous interference, the frequency hopping technology of the receiving processing strategy enables the GNSS receiver to flexibly switch between multiple frequencies, effectively avoiding the impact of continuous interference;

[0147] The comprehensive application of this series of technical solutions not only solves the technical problem that UAVs are susceptible to interference in complex electromagnetic environments, but also greatly improves the anti-interference capability and signal reception quality of the UAV navigation system, ensuring the safe and stable flight and efficient operation of UAVs in various complex environments, and providing solid technical support for the widespread application and further development of UAVs.

[0148] The effect evaluation module, after executing any strategy, obtains the evaluation data set within the set period T, builds the evaluation calculation model, and generates the estimated value of the operation effect under the corresponding strategy based on the preprocessed evaluation data set;

[0149] Among them, the value of the period T is greater than 0, and the predetermined period is selected according to the actual situation;

[0150] The evaluation data set includes: the strength and bit error rate of the communication signal, the height and speed of the target drone, and the positioning accuracy;

[0151] It should be noted that the communication signal refers to the communication signal between the target drone and the remote controller or ground station. The strength and bit error rate of the communication signal reflect the signal quality. The height and speed of the target drone refer to the distance between the target drone and the ground and the current flight speed of the target drone. The positioning accuracy refers to the signal reception of the GPS on the target drone.

[0152] The preprocessing process of the evaluation dataset is as follows:

[0153] S101, extracting the strength and bit error rate of the communication signal at the same time t, performing weighted calculation after dimensionless processing, and generating a communication quality index based on the formula:

[0154] ;

[0155] In the formula, represents the communication quality index, t=1, 2, ..., n, n=T, a1, a2 are weight coefficients, and their value range is [0, 1]. and Respectively represent the strength and bit error rate of the communication signal;

[0156] S102, extracting the height and speed of the target UAV at the same time t, performing weighted calculation after dimensionless processing, and generating a stability index based on the formula:

[0157] ;

[0158] In the formula, represents the stability index, b1 and b2 are weight coefficients, and their value ranges are [0, 1]. and Respectively represent the height and speed of the target UAV;

[0159] Similarly, the positioning accuracy is also dimensionless;

[0160] It should be noted that the weight coefficient is determined by the coefficient of variation method, which is a method of assigning weights to each indicator according to the degree of variation between the current value of each evaluation indicator and the target value; if the numerical difference of a certain indicator is large, it can clearly distinguish the evaluated objects, indicating that the indicator has rich discrimination information, and thus the indicator should be given a larger weight; conversely, if the numerical difference of each evaluated object on a certain indicator is small, then the ability of this indicator to distinguish the evaluated objects is weak, and thus the indicator should be given a smaller weight; this method directly uses the information contained in each indicator to obtain the weight of the indicator through calculation, so it is objective;

[0161] The process of running the evaluation calculation model is as follows:

[0162] Fluctuation calculation: Summarize the corresponding communication quality index, stability index and positioning accuracy at each time t within the period T, extract the maximum and minimum values ​​of the communication quality index, the maximum and minimum values ​​of the stability index, and the maximum and minimum values ​​of the positioning accuracy, and calculate the communication fluctuation value, stability fluctuation value and accuracy fluctuation value according to the formula:

[0163] ;

[0164] ;

[0165] ;

[0166] Where Csi, Mad and Lsb represent the communication fluctuation value, stability fluctuation value and accuracy fluctuation value respectively. and Respectively represent the maximum and minimum values ​​of the communication quality index, and Represent the maximum and minimum values ​​of the stability index, respectively. and Respectively represent the maximum and minimum values ​​of positioning accuracy;

[0167] Operation effect calculation: Based on the normalized communication fluctuation value, stability fluctuation value, and accuracy fluctuation value, a nonlinear calculation formula is established to generate an estimated operation effect value:

[0168] ;

[0169] Where PEP represents the estimated value of the operation effect, α, β and γ are adjustment coefficients used to control the influence of different factors on the estimated value of the operation effect, and the value range is [0, 1]. Csi_th represents the threshold parameter, which is used to adjust the sensitivity of the influence of the communication fluctuation value on the final result, and 0<Csi_th<2;

[0170] The logic is as follows:

[0171] Combination of exponential and logarithmic functions: The part combines the exponential function and the logarithmic function, so that the influence of the fluctuation value of the communication quality index on the estimated value of the operation effect presents a nonlinear relationship. When Csi is small, the value of this part is close to 1. As Csi increases, the value of this part gradually decreases, and the decreasing speed becomes faster and faster.

[0172] Combination of quadratic terms and logarithmic functions: The quadratic term is partially used to enhance the influence of the fluctuation value of the stability index on the final result. When Mad is small, the value of this part is close to 0. As Mad increases, the value of this part gradually increases, and the increase speed is getting faster and faster.

[0173] The impact of the logarithmic function: The logarithmic function is used to partially reflect the impact of the positioning accuracy fluctuation value on the estimated value of the operation effect. When Lsb is small, the value of this part is close to 0. As Lsb increases, the value of this part gradually increases, and the increase speed gradually accelerates, but the growth rate will gradually slow down (due to the nature of the logarithmic function);

[0174] The role of adjustment parameters: α, β, and γ are adjustment coefficients, which are used to adjust the influence of different factors on the estimated value of the operating effect according to the actual application scenario and data characteristics. By adjusting these parameters, the formula can be better adapted to different evaluation needs;

[0175] Function of the threshold parameter: Csi_th is a threshold parameter used to adjust the sensitivity of the impact of communication fluctuations on the final result. When Csi is much smaller than Csi_th, the value of this part is close to 1, indicating that the impact of communication fluctuations on the final result is small. When Csi approaches or exceeds Csi_th, the value of this part decreases rapidly, indicating that the impact of communication fluctuations on the final result increases significantly.

[0176] In summary, the accuracy of formula calculation results is increased by introducing mathematical functions.

[0177] By adopting the above technical solutions, a comprehensive effect evaluation module is built. This module can accurately obtain and preprocess the evaluation data set within a set period after executing any anti-interference strategy, and then generate an estimated value of the operation effect;

[0178] This solution comprehensively considers multiple key factors such as communication signal quality, drone flight stability and positioning accuracy, establishes a nonlinear evaluation model, and significantly improves the accuracy and sensitivity of the evaluation;

[0179] This technical solution effectively solves the problems of single evaluation indicators and linear evaluation results in traditional evaluation methods, and realizes a comprehensive, objective and accurate evaluation of the execution effect of UAV anti-interference strategies. This not only provides a scientific basis for optimizing UAV anti-interference strategies, further ensuring the safe and stable flight of UAVs in complex electromagnetic environments, but also provides guidance for subsequent optimization and adjustment actions, promoting the continuous development and progress of UAV technology.

[0180] The feedback adjustment module, upon receiving the estimated value of the operation effect, calibrates the corresponding strategy, compares the estimated value of the operation effect with the preset estimated threshold, and chooses whether to execute the corresponding strategy optimization instruction according to the comparison result;

[0181] The process of comparing the estimated value of the running effect with the preset estimated threshold is as follows:

[0182] When the estimated value of the operation effect exceeds the estimated threshold, it means that the effect of the corresponding strategy after operation has reached the target, and no response action is taken;

[0183] When the estimated value of the running effect does not exceed the estimated threshold, it means that the effect of the corresponding strategy after running does not meet the standard, then the corresponding strategy optimization instruction is executed;

[0184] When the corresponding strategy of calibration is:

[0185] When the filter adjustment strategy is triggered, the corresponding strategy optimization instructions executed are as follows:

[0186] Reduce the filter bandwidth by a preset amount to increase interference suppression;

[0187] Simultaneously increase the attenuation amount according to a preset amount to further reduce the amplitude of the interference signal;

[0188] The basis for setting the amount is: 1 / 3*dk_0; where dk represents the initial bandwidth;

[0189] The basis of the established quantity is: 1 / 3*js_0; where js represents the initial attenuation;

[0190] It should be noted that the choice of 1 / 3 as the adjustment factor is based on a compromise in experience and engineering practice. It can significantly improve the filtering performance and enhance the suppression of interference without excessively affecting the signal quality or causing system instability. 1 / 3 is a commonly used ratio that strikes a balance between performance and stability.

[0191] When the corresponding strategy of calibration is:

[0192] When the signal recovery strategy is triggered, the corresponding strategy optimization instructions executed are as follows:

[0193] The sliding average method is used to analyze whether the interfered signal segment is smooth;

[0194] If it is smooth, then the number of decomposition layers of the wavelet transform is reduced according to the interval value L to avoid distortion caused by over-decomposition;

[0195] If it is not smooth, increase the number of decomposition layers of the wavelet transform according to the interval value L to better capture the details of the high-frequency signal;

[0196] The recommended adjustment amount is 1 layer (i.e. L=1) each time to avoid uncertainty caused by excessive adjustments;

[0197] Reduce the time window by a set reduction amount to avoid or reduce the impact of interference;

[0198] The basis for the reduction amount is: 10%*kc_0; where kc_0 represents the original window;

[0199] Set the minimum value of the reduction amount allowed range, which is 10% to 30% of the original window.

[0200] When the corresponding strategy of calibration is:

[0201] When the receiving processing strategy is triggered, the corresponding strategy optimization instructions executed are as follows:

[0202] Increase the length of the frequency hopping sequence by a preset increment to adapt to different interference environments and signal reception requirements;

[0203] The basis of the increase is: 10%*cd_0; where cd_0 represents the length of the current frequency hopping sequence;

[0204] It should be noted that the increase in the preset frequency hopping sequence length should be flexibly set according to the complexity of the interference environment and the signal reception requirements, and the standard setting is 10% to 20% of the current length; for example, if the current length is 100, it can be increased to 110 to 120 when the interference increases; the above increase is set according to the minimum value in the standard setting;

[0205] After executing the corresponding strategy optimization instruction, the estimated value of the running effect is compared with the preset estimated threshold again according to the situation until no response action is taken or no further optimization instructions can be issued.

[0206] By adopting the above technical solution, a dynamic feedback and adjustment mechanism for the execution effect of the anti-interference strategy is realized;

[0207] This mechanism can intelligently trigger and execute corresponding strategy optimization instructions based on the comparison results of the estimated operating effect and the preset threshold, thereby continuously improving the effectiveness and adaptability of the anti-interference strategy;

[0208] Detailed optimization measures are designed for different strategies such as filter adjustment, signal recovery and reception processing, such as adjusting filter parameters, wavelet transform decomposition layers and time window size, and increasing the length of frequency hopping sequence. These measures are dynamically adjusted based on actual operation results to ensure the accuracy and effectiveness of the optimization direction.

[0209] This solution effectively solves the problems of fixedness, lack of flexibility and difficulty in adapting to complex and changing interference environments in traditional anti-interference strategies, significantly improves the stability and reliability of UAVs in complex electromagnetic environments, and provides strong guarantees for the safe flight and efficient operation of UAVs; at the same time, the solution also gradually approaches the optimal anti-interference effect through continuous iterative optimization, demonstrating good self-learning and adaptability.

[0210] Embodiment 2:

[0211] See also Figure 2 Based on Example 1, this embodiment also provides an anti-UAV interference method based on radio cracking technology, including the following steps:

[0212] S1. Obtain the meteorological data of the area where the target UAV is located. When the meteorological data contains a lightning signal, execute the plan adjustment mechanism and select any adjacent area without a lightning signal as the flight path of the target UAV.

[0213] S2. Monitor the signals received by the target UAV GNSS, analyze the signal characteristics with machine learning algorithms, and identify and classify the interference types; the interference types include instantaneous, intermittent, and continuous;

[0214] When the interference type is identified as transient, the filter adjustment strategy is triggered;

[0215] When the interference type is identified as intermittent, the signal recovery strategy is triggered;

[0216] When the interference type is identified as continuous, the receiving and processing strategy is triggered;

[0217] S3. After executing any strategy, obtain the evaluation data set within the set period T, build the evaluation calculation model, and generate the estimated value of the operation effect under the corresponding strategy based on the preprocessed evaluation data set;

[0218] S4. Upon receiving the estimated value of the operating effect, calibrate the corresponding strategy, and compare the estimated value of the operating effect with a preset estimated threshold value, and choose whether to execute the corresponding strategy optimization instruction based on the comparison result.

[0219] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in 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.

[0220] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0221] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the 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.

Claims

1. An anti-UAV jamming system based on radio cracking technology, characterized in that: include: The environmental monitoring management module obtains the meteorological data of the area where the target UAV is located. When the meteorological data contains lightning signals, the plan adjustment mechanism is executed to select any adjacent area without lightning signals as the flight path of the target UAV. The signal interference identification module monitors the signals received by the target drone’s GNSS, analyzes the signal characteristics with machine learning algorithms, and identifies and classifies the interference types, including instantaneous, intermittent, and continuous. The transient interference suppression module triggers the filtering adjustment strategy when the interference type is identified as transient; The intermittent interference management module triggers the signal recovery strategy when the interference type is identified as intermittent; The continuous interference countermeasure module triggers the receiving and processing strategy when it identifies that the interference type is continuous; The effect evaluation module, after executing any strategy, obtains the evaluation data set within the set period T, builds the evaluation calculation model, and generates the estimated value of the operation effect under the corresponding strategy based on the preprocessed evaluation data set; the evaluation data set includes: the strength and bit error rate of the communication signal, the height and speed of the target drone, and the positioning accuracy; The feedback adjustment module, upon receiving the estimated value of the operating effect, calibrates the corresponding strategy, compares the estimated value of the operating effect with the preset estimated threshold, and chooses whether to execute the corresponding strategy optimization instruction based on the comparison result.

2. According to claim 1, an anti-UAV jamming system based on radio cracking technology is characterized by: The process of analyzing signal features in combination with machine learning algorithms is as follows: Signal processing: pre-processing of the captured signal, including at least bandpass filtering and amplification; Feature extraction: extracting features reflecting signal characteristics from the preprocessed signal, including at least power spectrum density, frequency offset and phase change characteristics; Interference identification: Use machine learning algorithms to classify and identify the extracted features, and adopt decision tree algorithms for training. During the training process, a data set containing different types of interference signals is used for training. During real-time monitoring, the extracted features are input into the trained machine learning model to identify the interference type of the current signal.

3. According to claim 1, the anti-UAV jamming system based on radio cracking technology is characterized by: The steps of the triggered filter adjustment strategy are as follows: Interference characteristic identification: obtain the characteristics of instantaneous interference and run the filter carried by the target drone; The characteristics of the instantaneous interference include at least interference frequency and bandwidth; Filter parameter setting: Initially set the filter parameters based on the characteristics of instantaneous interference; The filter parameters of the initial setting filter include cut-off frequency, bandwidth and attenuation; The cut-off frequency is equal to the interference frequency in the characteristic of instantaneous interference; the bandwidth in the filtering parameter is equal to the bandwidth in the characteristic of instantaneous interference; the initial setting value of the attenuation amount is X, and X>0.

4. According to claim 1, the anti-UAV jamming system based on radio cracking technology is characterized by: The steps of the triggered signal recovery strategy are as follows: Using wavelet transform signal processing technology, the interfered signal segment is reconstructed to restore the original signal; Time window technology is used to select interference-free time periods for signal reception.

5. According to claim 1, the anti-UAV jamming system based on radio cracking technology is characterized by: The triggered receiving and processing strategy steps are: Frequency hopping technology is used to enable the GNSS receiver to switch between several different frequencies.

6. According to claim 1, the anti-UAV jamming system based on radio cracking technology is characterized by: The preprocessing process of the evaluation dataset is as follows: At the same time t, the strength and bit error rate of the communication signal are extracted, and after dimensionless processing, weighted calculation is performed to generate the communication quality index. The formula is: ； In the formula, represents the communication quality index, t∈T, a1 and a2 are weight coefficients, and their value ranges are [0, 1]. and Respectively represent the strength and bit error rate of the communication signal; At the same time t, the height and speed of the target UAV are extracted, and after dimensionless processing, weighted calculation is performed to generate a stability index based on the formula: ； In the formula, represents the stability index, b1 and b2 are weight coefficients, and their value ranges are [0, 1]. and Represent the height and speed of the target UAV respectively.

7. The anti-UAV jamming system based on radio cracking technology according to claim 6 is characterized by: The process of running the evaluation calculation model is as follows: Fluctuation calculation: Summarize the corresponding communication quality index, stability index and positioning accuracy at each time t within the period T, extract the maximum and minimum values ​​of the communication quality index, the maximum and minimum values ​​of the stability index, and the maximum and minimum values ​​of the positioning accuracy, and calculate the communication fluctuation value, stability fluctuation value and accuracy fluctuation value according to the formula: ； ； ； Where Csi, Mad and Lsb represent the communication fluctuation value, stability fluctuation value and accuracy fluctuation value respectively. and Respectively represent the maximum and minimum values ​​of the communication quality index, and Represent the maximum and minimum values ​​of the stability index, respectively. and Respectively represent the maximum and minimum values ​​of positioning accuracy; Operation effect calculation: Based on the normalized communication fluctuation value Csi0, stability fluctuation value Mad0 and precision fluctuation value Lsb0, a nonlinear calculation formula is established to generate an estimated operation effect value: ； Where PEP represents the estimated value of the operating effect, α, β and γ are adjustment coefficients, and their value ranges are all [0, 1], Csi_th represents the threshold parameter, and 0<Csi_th<2.

8. The anti-UAV jamming system based on radio cracking technology according to claim 7 is characterized by: The process of comparing the estimated value of the running effect with the preset estimated threshold is as follows: When the estimated value of the running effect exceeds the estimated threshold, no response action is taken; When the estimated value of the running effect does not exceed the estimated threshold, the corresponding strategy optimization instruction is executed; Among them, when the calibrated corresponding strategy is the trigger filter adjustment strategy, the corresponding strategy optimization instruction content executed is: Reduce the bandwidth of the filter according to a preset amount; the basis of the setting amount is: 1 / 3*dk_0; where dk_0 represents the initial bandwidth; simultaneously increase the attenuation according to a preset amount, the basis of the preset amount is: 1 / 3*js_0; where js_0 represents the initial attenuation; When the calibrated corresponding strategy is the trigger signal recovery strategy, the corresponding strategy optimization instruction content executed is: The sliding average method is used to analyze whether the interfered signal segment is smooth; If it is smooth, the number of decomposition layers of the wavelet transform is reduced according to the interval value L; If it is not smooth, increase the number of decomposition layers of the wavelet transform according to the interval value L; Synchronously reduce the time window according to the set reduction amount, and the reduction amount is based on: 10%*kc_0; where kc_0 represents the original window; When the calibrated corresponding strategy is the trigger receiving processing strategy, the corresponding strategy optimization instruction content executed is: The length of the frequency hopping sequence is increased according to a preset increase amount, and the basis of the increase amount is: 10%*cd_0; wherein cd_0 represents the length of the current frequency hopping sequence.

9. An anti-UAV jamming method based on radio cracking technology, using any system described in claims 1 to 8, characterized in that: The steps include: S1. Obtain the meteorological data of the area where the target UAV is located. When the meteorological data contains a lightning signal, execute the plan adjustment mechanism and select any adjacent area without a lightning signal as the flight path of the target UAV. S2. Monitor the signals received by the target UAV GNSS, analyze the signal characteristics with machine learning algorithms, and identify and classify the interference types; the interference types include instantaneous, intermittent, and continuous; When the interference type is identified as transient, the filter adjustment strategy is triggered; When the interference type is identified as intermittent, the signal recovery strategy is triggered; When the interference type is identified as continuous, the receiving and processing strategy is triggered; S3. After executing any strategy, obtain the evaluation data set within the set period T, build the evaluation calculation model, and generate the estimated value of the operation effect under the corresponding strategy based on the preprocessed evaluation data set; S4. Upon receiving the estimated value of the operating effect, calibrate the corresponding strategy, and compare the estimated value of the operating effect with a preset estimated threshold value, and choose whether to execute the corresponding strategy optimization instruction based on the comparison result.

Images