Tracking type unmanned aerial vehicle countering method and system

Abstract

The invention provides a tracking type unmanned aerial vehicle countering method and system, and belongs to the technical field of unmanned aerial vehicle countering, and the method comprises the steps: monitoring a communication frequency band of a tracking type unmanned aerial vehicle in real time, and determining real-time signal data and a signal feature vector; extracting a frequency hopping behavior of the tracking type unmanned aerial vehicle, determining frequency hopping data, and analyzing the frequency hopping data to determine a frequency hopping type; determining a communication interference signal and a frequency hopping interference signal of the unmanned aerial vehicle; transmitting a communication interference signal and a frequency hopping interference signal, and acquiring interference signal data of the tracking type unmanned aerial vehicle after the communication interference signal and the frequency hopping interference signal are transmitted; and dynamically adjusting a communication interference signal and a frequency hopping interference signal, and evaluating a countering effect. The method can optimize the countering strategy in real time, improves the pertinence and accuracy of the interference effect, guarantees the effectiveness of countering measures in a complex environment, provides a more flexible and more efficient technical means for the safety management of the unmanned aerial vehicle, remarkably enhances the defense capability, and remarkably improves the intelligent level of the monitoring and management of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle countermeasures, and in particular to a tracking type unmanned aerial vehicle countermeasure method and system. Background Art

[0002] With the rapid development of drones, tracking drones are increasingly used in military reconnaissance, public safety monitoring, etc. However, the subsequent abuse of drones and potential security threats have triggered an urgent need for countermeasures. Existing countermeasures are mostly focused on physically intercepting or jamming drones, lacking the ability to dynamically monitor and jam communication frequency bands.

[0003] The problems faced by traditional countermeasure technologies include insufficient response to rapid changes in drone frequency bands, lack of accurate transmission of jamming signals, and lack of dynamic adjustment capabilities. This results in unsatisfactory countermeasure effects in complex operating environments.

[0004] Therefore, the present invention provides a tracking type UAV countermeasure method and system. Summary of the invention

[0005] The present invention provides a tracking type unmanned aerial vehicle countermeasure method and system, by determining the real-time signal data and signal feature vector of the unmanned aerial vehicle, determining the frequency hopping data and frequency modulation type of the unmanned aerial vehicle, determining and transmitting the communication interference signal and the frequency hopping interference signal of the unmanned aerial vehicle according to the signal feature and the frequency hopping type of the tracking type unmanned aerial vehicle, dynamically adjusting the communication interference signal and the frequency hopping interference signal according to the real-time signal data and the interference signal data of the tracking type unmanned aerial vehicle, and evaluating the countermeasure effect, the countermeasure strategy can be optimized in real time, the pertinence and accuracy of the interference effect can be improved, the effectiveness of the countermeasures in complex environments can be ensured, more flexible and efficient technical means are provided for the safety management of unmanned aerial vehicles, the defense capability is significantly enhanced, and the intelligent level of unmanned aerial vehicle monitoring and management is significantly improved.

[0006] On the one hand, the present invention provides a tracking type UAV countermeasure method, comprising: 101: Real-time monitoring of the communication frequency band of the tracking UAV, determining the real-time signal data, and performing feature extraction on the real-time signal data to determine the signal feature vector; 102: extract the frequency hopping behavior of the tracking UAV and determine the frequency hopping data, and analyze the frequency hopping data to determine the frequency hopping type; 103: Based on the signal characteristics and frequency hopping type of the tracking UAV, determine the communication interference signal and frequency hopping interference signal of the UAV; 104: transmitting a communication interference signal and a frequency hopping interference signal, and obtaining interference signal data of the tracking UAV after transmitting the communication interference signal and the frequency hopping interference signal; 105: Based on the real-time signal data and interference signal data of the tracking UAV, dynamically adjust the communication interference signal and frequency hopping interference signal, and evaluate the countermeasure effect.

[0007] According to a tracking drone countermeasure method provided by the present invention, the communication frequency band of the tracking drone is monitored in real time, real-time signal data is determined, and feature extraction is performed on the real-time signal data to determine a signal feature vector, including: Determine the communication frequency band of the tracking UAV, monitor all signal activities within the communication frequency band of the tracking UAV in real time based on the spectrum analyzer, and determine the real-time signal data; Preprocess the real-time signal data and eliminate interference signals, and extract the real-time sub-spectrum data and real-time sub-power data corresponding to each timestamp in the real-time signal data after preprocessing and eliminating interference signals; All real-time sub-spectrum data are time-sorted based on the timestamp to determine the time spectrum data, and at the same time, all real-time power sub-data are time-sorted based on the timestamp to determine the time power data; The signal feature vector of the tracked UAV is determined based on the time spectrum data and the time power data.

[0008] According to a tracking UAV countermeasure method provided by the present invention, a signal feature vector of a tracking UAV is determined based on time spectrum data and time power data, including: Determine a window function, extract the earliest timestamp and the latest timestamp of all timestamps, and determine a sliding window of the time spectrum data based on the window function, the earliest timestamp, the latest timestamp, and the number of all timestamps; The time spectrum data is divided into a plurality of time sub-frequency domain data based on a sliding window, and a division sequence number of each time sub-frequency domain data is determined. Meanwhile, the time power data is divided into a plurality of time sub-power data based on a sliding window, and a division sequence number of each time sub-power data is determined. Applying a window function to each time sub-frequency domain data and each time sub-power data; Extracting the time sub-frequency domain data and the time sub-power data after applying the window function corresponding to the same segmentation number, and determining the first power spectrum density of each segmentation number; Performing logarithmic transformation on the first power spectrum density of each segmentation number to determine the second power spectrum density of each segmentation number; Determine the frequency range based on all the time sub-frequency domain data after the window function is applied, and at the same time, determine the power range based on all the time sub-power data after the window function is applied; Draw a power spectrum density graph based on the frequency range, power range, and second power spectrum densities of all segmentation numbers; The power spectrum density graph is analyzed to determine all signal features of the tracking UAV, and the signal feature vector is determined based on all signal features.

[0009] According to a tracking drone countermeasure method provided by the present invention, the frequency hopping behavior of the tracking drone is extracted and the frequency hopping data is determined, including: Extract each frequency hopping behavior and the corresponding timestamp in the real-time signal data after preprocessing and interference signal elimination, and determine the frequency hopping sub-data based on each frequency hopping behavior and all corresponding timestamps; The frequency hopping data is determined based on the frequency hopping sub-data of all the frequency hopping behaviors.

[0010] According to a tracking drone countermeasure method provided by the present invention, frequency hopping data is analyzed to determine the frequency hopping type, including: Extracting the earliest timestamp of each frequency hopping behavior, sorting all frequency modulation behaviors based on the earliest timestamp, and determining a first sequence; Determine a frequency hopping sequence of each frequency hopping behavior based on the frequency hopping sub-data of each frequency hopping behavior in the frequency hopping data; in, represents the frequency hopping sequence of the i-th frequency hopping behavior in the first sequence, , Respectively represent the 1st frequency, the jth frequency, and the iN1th frequency of the i-th frequency hopping behavior in the first sequence, and iN1 represents the number of frequencies in the frequency hopping sub-data of the i-th frequency hopping behavior in the first sequence; Determine a comprehensive frequency hopping sequence of the tracking UAV based on the frequency hopping sequences of all frequency hopping behaviors in the first sequence; in, represents the comprehensive frequency hopping sequence of the tracking UAV, Respectively indicate frequency ,frequency ,frequency , N3 represents the number of frequencies in the integrated frequency hopping sequence; Determine the time interval sequence of the tracking UAV based on the earliest timestamp of every two frequency hopping behaviors in the first sequence; in, represents the time series of the tracking UAV, Respectively represent time intervals , time interval , time interval , N2 represents the number of time intervals in the time interval sequence; A frequency hopping type is determined based on the first sequence, the frequency hopping sequence, and the time interval sequence.

[0011] According to a tracking drone countermeasure method provided by the present invention, a frequency hopping type is determined based on a first sequence, a frequency hopping sequence, and a time interval sequence, including: in, Indicates frequency The probability in frequency hopping data, The jth frequency of the ith frequency hopping behavior in the first sequence is equal to the frequency When the value is 1, represents the sum of the frequencies of all the frequency hopping sub-data in the frequency hopping data, N4 represents the number of frequency hopping sub-data in the frequency hopping data, Indicates time interval The probability in frequency hopping data, represents the time interval between the earliest timestamp of the ith frequency hopping behavior and the earliest timestamp of the i+1th frequency hopping behavior in the first sequence, Indicates that the time interval between the earliest timestamp of the i-th frequency hopping behavior in the first sequence and the earliest timestamp of the i+1-th frequency hopping behavior is equal to the time interval When the value is 1, represents the correlation value of all frequency hopping sequences in the first sequence, k represents the delay coefficient, represents the jth frequency of the i+kth frequency hopping behavior in the first sequence, represents the average frequency of the frequency hopping sequence of the i-th frequency hopping behavior in the first sequence, represents the average frequency of the integrated frequency hopping sequence, Indicates the frequency hopping type of the frequency hopping data. Indicates random frequency hopping, 2 means periodic frequency hopping, 3 indicates pseudo-random frequency hopping, 4 indicates other frequency hopping, A indicates condition A, , They represent the lower limit and upper limit of uniform probability respectively. , They represent the interval Poisson lower limit and interval Poisson upper limit respectively, B represents condition B, , Respectively represent the lower limit of concentration probability and the upper limit of concentration probability, FL and FU represent the fixed lower limit of interval and the fixed upper limit of interval, C represents condition C, RL and RU represent the lower limit of pseudo-random probability and the upper limit of pseudo-random probability, AL and AU represent the lower limit of interval pseudo-random probability and the upper limit of interval pseudo-random probability, Indicates frequency The probability in the frequency hopping data is within the set uniform probability range, Indicates time interval The probability in the frequency hopping data is within the set interval Poisson range, Indicates frequency The probability in the frequency hopping data is within the set concentration probability range, Indicates time interval The probability in the frequency hopping data is within a fixed range of the set interval, Indicates frequency The probability in the frequency hopping data is within the set pseudo-random probability range. Indicates time interval The probability in the frequency hopping data is within the pseudo-random range of the set interval, represents setting uniform probability quantity, represents the number of set interval Poisson, represents the number of set concentration probabilities, Indicates setting a fixed number of intervals. Indicates setting the number of pseudo-random probabilities. Indicates a pseudo-random number at a set interval.

[0012] According to a tracking drone countermeasure method provided by the present invention, a communication interference signal and a frequency hopping interference signal are transmitted, including: Determine a first power of the communication interference signal and a second power of the frequency modulation interference signal based on the signal strength of the tracking drone; A directional antenna is used to transmit an interference signal, and a power amplifier is used to enhance the strength of the communication interference signal based on a first power of the communication interference signal. At the same time, a power amplifier is used to enhance the strength of the FM interference signal based on a second power of the FM interference signal.

[0013] On the other hand, the present invention also provides a tracking type UAV countermeasure system, comprising: Monitoring module: real-time monitoring of the communication frequency band of the tracking UAV, determining the real-time signal data, and extracting the features of the real-time signal data to determine the signal feature vector; Extraction module: extract the frequency hopping behavior of the tracking UAV and determine the frequency hopping data, and analyze the frequency hopping data to determine the frequency hopping type; Generation module: Determine the communication interference signal and frequency hopping interference signal of the UAV based on the signal characteristics and frequency hopping type of the tracking UAV; Transmitter module: transmits communication interference signals and frequency hopping interference signals, and obtains interference signal data of the tracking UAV after transmitting the communication interference signals and frequency hopping interference signals; Evaluation module: Based on the real-time signal data and interference signal data of the tracking UAV, dynamically adjust the communication interference signal and frequency hopping interference signal, and evaluate the countermeasure effect.

[0014] Compared with the prior art, the present invention has the following beneficial effects: By determining the real-time signal data and signal feature vector of the UAV, determining the frequency hopping data and frequency modulation type of the UAV, determining and transmitting the communication interference signal and frequency hopping interference signal of the UAV according to the signal characteristics and frequency hopping type of the tracking UAV, dynamically adjusting the communication interference signal and frequency hopping interference signal according to the real-time signal data and interference signal data of the tracking UAV, and evaluating the countermeasure effect, the countermeasure strategy can be optimized in real time, the pertinence and accuracy of the interference effect can be improved, and the effectiveness of the countermeasures in complex environments can be ensured, providing more flexible and efficient technical means for the safety management of UAVs, significantly enhancing the defense capabilities, and significantly improving the intelligence level of UAV monitoring and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a tracking type UAV countermeasure method provided by an embodiment of the present invention.

[0017] Figure 2 It is a structural schematic diagram of a tracking type UAV countermeasure system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0019] Embodiment 1: The embodiment of the present invention provides a tracking type UAV countermeasure method, such as Figure 1 As shown, including: 101: Real-time monitoring of the communication frequency band of the tracking UAV, determining the real-time signal data, and performing feature extraction on the real-time signal data to determine the signal feature vector; 102: extract the frequency hopping behavior of the tracking UAV and determine the frequency hopping data, and analyze the frequency hopping data to determine the frequency hopping type; 103: Based on the signal characteristics and frequency hopping type of the tracking UAV, determine the communication interference signal and frequency hopping interference signal of the UAV; 104: transmitting a communication interference signal and a frequency hopping interference signal, and obtaining interference signal data of the tracking UAV after transmitting the communication interference signal and the frequency hopping interference signal; 105: Based on the real-time signal data and interference signal data of the tracking UAV, dynamically adjust the communication interference signal and frequency hopping interference signal, and evaluate the countermeasure effect.

[0020] In this embodiment, the tracking UAV is a UAV dedicated to continuously tracking a specific target, and is usually equipped with an efficient communication and control system, and is used for tasks such as surveillance and reconnaissance.

[0021] In this embodiment, the communication frequency of the drone is monitored to obtain real-time signal data, and the signal data is subjected to feature extraction to form a signal feature vector.

[0022] In this embodiment, frequency hopping is a communication technology that refers to the rapid switching of a device between multiple frequencies to enhance the signal's anti-interference capability. The system identifies and records the frequency hopping behavior of the drone, determines the corresponding frequency hopping data and type, and understands the drone's communication mode.

[0023] In this embodiment, based on the signal characteristics and frequency hopping types in the first two steps, the system generates corresponding communication interference signals and frequency hopping interference signals, which are designed to interfere with the normal communication and frequency hopping processes of the drone.

[0024] In this embodiment, the system transmits the generated communication interference signal and frequency hopping interference signal, and tracks the interference signal data of the UAV in real time to evaluate the effect of the interference.

[0025] In this embodiment, based on the real-time signal data and the interference signal data, the system dynamically adjusts the transmission parameters of the interference signal to optimize the countermeasure effect and evaluate the effectiveness of the measures taken.

[0026] In this embodiment, dynamically adjusting the communication interference signal includes: adjusting the communication interference signal frequency, the communication interference signal power, and the communication interference signal transmission position; In this embodiment, dynamically adjusting the frequency hopping interference signal includes: adjusting the frequency of the frequency hopping interference signal, the power of the frequency hopping interference signal, and the transmission position of the frequency hopping interference signal.

[0027] The beneficial effects of the above technical solution are as follows: by determining the real-time signal data and signal feature vector of the UAV, determining the frequency hopping data and frequency modulation type of the UAV, determining and transmitting the communication interference signal and frequency hopping interference signal of the UAV according to the signal characteristics and frequency hopping type of the tracking UAV, dynamically adjusting the communication interference signal and frequency hopping interference signal according to the real-time signal data and interference signal data of the tracking UAV, and evaluating the countermeasure effect, the countermeasure strategy can be optimized in real time, the pertinence and accuracy of the interference effect can be improved, and the effectiveness of the countermeasures in complex environments can be ensured, providing more flexible and efficient technical means for the safety management of UAVs, significantly enhancing the defense capabilities, and significantly improving the intelligence level of UAV monitoring and management.

[0028] Embodiment 2: The embodiment of the present invention provides a tracking drone countermeasure method, which monitors the communication frequency band of the tracking drone in real time, determines real-time signal data, and extracts features from the real-time signal data to determine a signal feature vector, including: Determine the communication frequency band of the tracking UAV, monitor all signal activities within the communication frequency band of the tracking UAV in real time based on the spectrum analyzer, and determine the real-time signal data; Preprocess the real-time signal data and eliminate interference signals, and extract the real-time sub-spectrum data and real-time sub-power data corresponding to each timestamp in the real-time signal data after preprocessing and eliminating interference signals; All real-time sub-spectrum data are time-sorted based on the timestamp to determine the time spectrum data, and at the same time, all real-time power sub-data are time-sorted based on the timestamp to determine the time power data; The signal feature vector of the tracked UAV is determined based on the time spectrum data and the time power data.

[0029] In this embodiment, a noise filtering algorithm, such as fast Fourier transform, low-pass filtering, etc., may be applied to eliminate interference signals from real-time signal data to ensure that the extracted real-time spectrum data has a high signal-to-noise ratio.

[0030] In this embodiment, a spectrum analyzer (such as a spectrum monitor) is used to monitor the communication frequency band of the drone in real time, capture all signal activities within the frequency band, and obtain real-time signal data. This is the first step of the countermeasure and is aimed at identifying the specific frequency range used by the drone.

[0031] In this embodiment, the acquired real-time signal data is preprocessed, including denoising, filtering, and eliminating the influence of interference signals. Then, the real-time sub-spectrum data and real-time sub-power data corresponding to each timestamp are extracted. The sub-spectrum data and power data respectively represent the strength and power information of the signal at different frequencies at a specific time point.

[0032] In this embodiment, all real-time sub-spectrum data are sorted according to the timestamp to form time spectrum data; similarly, the real-time power sub-data are time-sorted to form time power data. The purpose of this step is to integrate the signal data into a time series that is easy to analyze, so as to observe the change of the signal over time.

[0033] In this embodiment, the signal characteristic vector of the tracking UAV is determined by integrating the time spectrum data and the time power data. The signal characteristic vector is a mathematical representation of the signal and contains key information about the signal frequency characteristics and power distribution, which is used for subsequent analysis and interference strategy formulation.

[0034] The beneficial effects of the above technical solution are: real-time monitoring of the communication frequency band of the tracking UAV, determining the real-time signal data, and performing feature extraction on the real-time signal data to determine the signal feature vector, which can improve the accuracy and response speed of UAV communication monitoring, accurately capture signal changes, and ensure timely identification of potential threats.

[0035] Embodiment 3: An embodiment of the present invention provides a method for countering a tracking drone, which determines a signal feature vector of a tracking drone based on time spectrum data and time power data, including: Determine a window function, extract the earliest timestamp and the latest timestamp of all timestamps, and determine a sliding window of the time spectrum data based on the window function, the earliest timestamp, the latest timestamp, and the number of all timestamps; The time spectrum data is divided into a plurality of time sub-frequency domain data based on a sliding window, and a division sequence number of each time sub-frequency domain data is determined. Meanwhile, the time power data is divided into a plurality of time sub-power data based on a sliding window, and a division sequence number of each time sub-power data is determined. Applying a window function to each time sub-frequency domain data and each time sub-power data; Extracting the time sub-frequency domain data and the time sub-power data after applying the window function corresponding to the same segmentation number, and determining the first power spectrum density of each segmentation number; Performing logarithmic transformation on the first power spectrum density of each segmentation number to determine the second power spectrum density of each segmentation number; Determine the frequency range based on all the time sub-frequency domain data after the window function is applied, and at the same time, determine the power range based on all the time sub-power data after the window function is applied; Draw a power spectrum density graph based on the frequency range, power range, and second power spectrum densities of all segmentation numbers; The power spectrum density graph is analyzed to determine all signal features of the tracking UAV, and the signal feature vector is determined based on all signal features.

[0036] In this embodiment, the window function is a mathematical function used to reduce boundary effects and improve the accuracy of spectrum estimation in signal processing. After the window function is determined, the earliest and latest time stamps of all time stamps are extracted to construct a sliding window.

[0037] In this embodiment, a sliding window of the time spectrum data is determined based on the window function, the earliest timestamp, the latest timestamp and the total number of timestamps. The sliding window is used to move in a time series with a certain length and step size to extract data within each window.

[0038] In this embodiment, the time spectrum data is divided into multiple time sub-frequency domain data using a sliding window, and a division sequence number is assigned to each sub-frequency domain data; at the same time, the time power data is divided into multiple time sub-power data, and a division sequence number is also assigned to each sub-frequency domain data.

[0039] In this embodiment, a window function is applied to each time sub-frequency domain data and time sub-power data to reduce the leakage effect of the signal during the spectrum conversion process.

[0040] In this embodiment, data corresponding to the same segmentation number is extracted from the time sub-frequency domain data and the time sub-power data after applying the window function to determine the first power spectrum density of each segmentation number, and the power spectrum density reflects the power distribution of the signal at each frequency.

[0041] In this embodiment, the first power spectrum density of each segmentation sequence number is logarithmically transformed to obtain the second power spectrum density. This transformation helps to process data with a large dynamic range, making the features easier to analyze.

[0042] In this embodiment, the frequency range is determined based on all the time sub-frequency domain data after the window function is applied, and at the same time, the power range is determined from the time sub-power data, so as to perform a comprehensive spectrum analysis.

[0043] In this embodiment, a power spectrum density graph is drawn based on the frequency range, the power range and the second power spectrum densities of all segmentation numbers. This graph intuitively shows the power distribution of the signal at different frequencies.

[0044] In this embodiment, by analyzing the power spectrum density diagram, all signal characteristics of the tracking UAV are determined. These characteristics include frequency components, power distribution, etc. Based on these signal characteristics, a signal feature vector is finally generated for subsequent identification and formulation of interference strategies.

[0045] The beneficial effects of the above technical solution are as follows: determining the signal characteristic vector of the tracked UAV based on the time spectrum data and the time power data can improve the flexibility and accuracy of signal monitoring, making the real-time analysis of the UAV communication mode more accurate, thereby providing reliable data support for countermeasures and enhancing the system's response capabilities in complex environments.

[0046] Embodiment 4: An embodiment of the present invention provides a method for countering a tracking drone, which extracts the frequency hopping behavior of the tracking drone and determines the frequency hopping data, including: Extract each frequency hopping behavior and the corresponding timestamp in the real-time signal data after preprocessing and interference signal elimination, and determine the frequency hopping sub-data based on each frequency hopping behavior and all corresponding timestamps; The frequency hopping data is determined based on the frequency hopping sub-data of all the frequency hopping behaviors.

[0047] In this embodiment, all frequency hopping behaviors are identified and extracted from the real-time signal data after preprocessing and elimination of interference signals. Frequency hopping behavior refers to a mode in which the signal quickly switches between different frequencies within a specific time period. Each frequency hopping behavior is accompanied by one or more corresponding timestamps, recording the time the signal exists at each frequency.

[0048] In this embodiment, frequency hopping sub-data is constructed based on each extracted frequency hopping behavior and all corresponding time stamps. The frequency hopping sub-data is a specific set that includes all frequency and time information related to the specific frequency hopping behavior.

[0049] In this embodiment, the frequency hopping sub-data of all frequency hopping behaviors are integrated to determine the overall frequency hopping data, which is a summary of all frequency hopping behaviors and provides a complete frequency hopping pattern, including the frequency changes and time information of all frequency hopping behaviors.

[0050] The beneficial effects of the above technical solution are: extracting the frequency hopping behavior of the tracking UAV and determining the frequency hopping data, which can improve the data basis for determining the frequency hopping type.

[0051] Embodiment 5: An embodiment of the present invention provides a tracking drone countermeasure method, which analyzes frequency hopping data to determine the frequency hopping type, including: Extracting the earliest timestamp of each frequency hopping behavior, sorting all frequency modulation behaviors based on the earliest timestamp, and determining a first sequence; Determine a frequency hopping sequence of each frequency hopping behavior based on the frequency hopping sub-data of each frequency hopping behavior in the frequency hopping data; in, represents the frequency hopping sequence of the i-th frequency hopping behavior in the first sequence, , Respectively represent the 1st frequency, the jth frequency, and the iN1th frequency of the i-th frequency hopping behavior in the first sequence, and iN1 represents the number of frequencies in the frequency hopping sub-data of the i-th frequency hopping behavior in the first sequence; Determine a comprehensive frequency hopping sequence of the tracking UAV based on the frequency hopping sequences of all frequency hopping behaviors in the first sequence; in, represents the comprehensive frequency hopping sequence of the tracking UAV, Respectively indicate frequency ,frequency ,frequency , N3 represents the number of frequencies in the integrated frequency hopping sequence; Determine the time interval sequence of the tracking UAV based on the earliest timestamp of every two frequency hopping behaviors in the first sequence; in, represents the time series of the tracking UAV, Respectively represent time intervals , time interval , time interval , N2 represents the number of time intervals in the time interval sequence; A frequency hopping type is determined based on the first sequence, the frequency hopping sequence, and the time interval sequence.

[0052] In this embodiment, the earliest timestamp is extracted from each frequency hopping behavior, indicating the start time of the frequency hopping behavior.

[0053] In this embodiment, all frequency hopping behaviors are sorted based on the earliest timestamp extracted to determine a first sequence, which reflects the time sequence in which all frequency hopping behaviors occur.

[0054] In this embodiment, frequency hopping sub-data of each frequency hopping behavior is extracted from the frequency hopping data, and then a frequency hopping sequence of each frequency hopping behavior is determined. The frequency hopping sequence includes all frequencies of the corresponding frequency hopping behavior.

[0055] In this embodiment, a comprehensive frequency hopping sequence of the tracking drone is determined based on all frequencies in the frequency hopping sequence of all frequency hopping behaviors in the first sequence. This sequence includes the number of occurrences of each frequency in all frequency hopping behaviors.

[0056] In this embodiment, the earliest timestamp of every two frequency hopping behaviors in the first sequence is used to determine the time interval sequence of the tracking UAV, and the sequence includes the time intervals between all adjacent frequency hopping behaviors.

[0057] The beneficial effects of the above technical solution are as follows: analyzing the frequency hopping data to determine the frequency hopping type can improve the recognition accuracy of the frequency hopping pattern of tracking UAVs, effectively deal with potential interference and threats, provide more targeted countermeasure strategies, and significantly improve the intelligence level of UAV monitoring and management.

[0058] Embodiment 6: An embodiment of the present invention provides a tracking drone countermeasure method, which determines a frequency hopping type based on a first sequence, a frequency hopping sequence, and a time interval sequence, including: in, Indicates frequency The probability in frequency hopping data, The jth frequency of the ith frequency hopping behavior in the first sequence is equal to the frequency When the value is 1, represents the sum of the frequencies of all the frequency hopping sub-data in the frequency hopping data, N4 represents the number of frequency hopping sub-data in the frequency hopping data, Indicates time interval The probability in frequency hopping data, represents the time interval between the earliest timestamp of the ith frequency hopping behavior and the earliest timestamp of the i+1th frequency hopping behavior in the first sequence, Indicates that the time interval between the earliest timestamp of the i-th frequency hopping behavior in the first sequence and the earliest timestamp of the i+1-th frequency hopping behavior is equal to the time interval When the value is 1, represents the correlation value of all frequency hopping sequences in the first sequence, k represents the delay coefficient, represents the jth frequency of the i+kth frequency hopping behavior in the first sequence, represents the average frequency of the frequency hopping sequence of the i-th frequency hopping behavior in the first sequence, represents the average frequency of the integrated frequency hopping sequence, Indicates the frequency hopping type of the frequency hopping data. Indicates random frequency hopping, 2 means periodic frequency hopping, 3 indicates pseudo-random frequency hopping, 4 indicates other frequency hopping, A indicates condition A, , They represent the lower limit and upper limit of uniform probability respectively. , They represent the interval Poisson lower limit and interval Poisson upper limit respectively, B represents condition B, , Respectively represent the lower limit of concentration probability and the upper limit of concentration probability, FL and FU represent the fixed lower limit of interval and the fixed upper limit of interval, C represents condition C, RL and RU represent the lower limit of pseudo-random probability and the upper limit of pseudo-random probability, AL and AU represent the lower limit of interval pseudo-random probability and the upper limit of interval pseudo-random probability, Indicates frequency The probability in the frequency hopping data is within the set uniform probability range, Indicates time interval The probability in the frequency hopping data is within the set interval Poisson range, Indicates frequency The probability in the frequency hopping data is within the set concentration probability range, Indicates time interval The probability in the frequency hopping data is within a fixed range of the set interval, Indicates frequency The probability in the frequency hopping data is within the set pseudo-random probability range. Indicates time interval The probability in the frequency hopping data is within the pseudo-random range of the set interval, represents setting uniform probability quantity, represents the number of set interval Poisson, represents the number of set concentration probabilities, Indicates setting a fixed number of intervals. Indicates setting the number of pseudo-random probabilities. Indicates a pseudo-random number at a set interval.

[0059] In this embodiment, Represents the frequency in the first sequence The number of times it appears in the frequency hopping sequence of the i-th frequency hopping behavior.

[0060] In this embodiment, Represents the frequency in the first sequence The number of times it appears in all frequency hopping sequences of the frequency hopping data.

[0061] In this embodiment, the correlation value It reflects the similarity or repeatability between different frequency sequences in the sequence. The higher the correlation value, the greater the repeatability.

[0062] In this embodiment, iN1 represents the number of frequencies in the frequency hopping sub-data of the i-th frequency hopping behavior in the first sequence, and the number of frequencies in the frequency hopping sub-data of each frequency hopping behavior is different.

[0063] In this embodiment, Indicates frequency The number of frequencies in the frequency hopping data whose probability is within the set uniform probability range, Indicates time interval The number of time intervals in the frequency hopping data whose probability is within the set interval Poisson range, Indicates frequency The number of frequencies in the frequency hopping data whose probability is within the set concentration probability range, Indicates time interval The number of time intervals in the frequency hopping data whose probability is within a fixed range of the set interval, Indicates frequency The number of frequencies in the frequency hopping data whose probability is within the set pseudo-random probability range, Indicates time interval The number of time intervals in the frequency hopping data whose probability is within a set interval pseudo-random range.

[0064] The beneficial effects of the above technical solution are as follows: by determining the frequency hopping type based on the first sequence, the frequency hopping sequence and the time interval sequence, the recognition accuracy of the frequency hopping pattern of the tracking UAV can be further improved, and potential interference and threats can be more effectively dealt with.

[0065] Embodiment 7: The embodiment of the present invention provides a tracking drone countermeasure method, which transmits a communication interference signal and a frequency hopping interference signal, including: Determine a first power of the communication interference signal and a second power of the frequency modulation interference signal based on the signal strength of the tracking drone; A directional antenna is used to transmit an interference signal, and a power amplifier is used to enhance the strength of the communication interference signal based on a first power of the communication interference signal. At the same time, a power amplifier is used to enhance the strength of the FM interference signal based on a second power of the FM interference signal.

[0066] In this embodiment, based on the received signal strength of the tracking UAV, the first power of the communication interference signal is first determined, which refers to the strength level of the interference signal received by the UAV; similarly, the second power of the FM interference signal is determined, which indicates the interference intensity generated by the FM signal within the receiving range of the UAV.

[0067] In this embodiment, a directional antenna is used to transmit an interference signal to increase the directionality and transmission efficiency of the signal. The directional antenna can concentrate signal energy and enhance the interference effect.

[0068] In this embodiment, based on the first power of the communication interference signal, a power amplifier is used to enhance the strength of the signal, which helps to improve the interference effect and interfere with the normal communication of the drone; at the same time, based on the second power of the FM interference signal, another power amplifier is used to enhance the strength of the FM interference signal to ensure its effective propagation within the target range.

[0069] The beneficial effects of the above technical solution are as follows: by transmitting communication interference signals and frequency hopping interference signals, the power of communication and frequency modulation interference signals can be accurately determined, which provides a scientific basis for the effective transmission of interference signals and improves the interference effect on tracking UAVs.

[0070] Embodiment 8: An embodiment of the present invention provides a tracking type UAV countermeasure system, comprising: Monitoring module: real-time monitoring of the communication frequency band of the tracking UAV, determining the real-time signal data, and extracting the features of the real-time signal data to determine the signal feature vector; Extraction module: extract the frequency hopping behavior of the tracking UAV and determine the frequency hopping data, and analyze the frequency hopping data to determine the frequency hopping type; Generation module: Determine the communication interference signal and frequency hopping interference signal of the UAV based on the signal characteristics and frequency hopping type of the tracking UAV; Transmitter module: transmits communication interference signals and frequency hopping interference signals, and obtains interference signal data of the tracking UAV after transmitting the communication interference signals and frequency hopping interference signals; Evaluation module: Based on the real-time signal data and interference signal data of the tracking UAV, dynamically adjust the communication interference signal and frequency hopping interference signal, and evaluate the countermeasure effect.

[0071] The beneficial effects of the above technical solution are as follows: by determining the real-time signal data and signal feature vector of the UAV, determining the frequency hopping data and frequency modulation type of the UAV, determining and transmitting the communication interference signal and frequency hopping interference signal of the UAV according to the signal characteristics and frequency hopping type of the tracking UAV, dynamically adjusting the communication interference signal and frequency hopping interference signal according to the real-time signal data and interference signal data of the tracking UAV, and evaluating the countermeasure effect, the countermeasure strategy can be optimized in real time, the pertinence and accuracy of the interference effect can be improved, and the effectiveness of the countermeasures in complex environments can be ensured, providing more flexible and efficient technical means for the safety management of UAVs, significantly enhancing the defense capabilities, and significantly improving the intelligence level of UAV monitoring and management.

[0072] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0073] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A tracking drone countermeasure method, characterized in that: include: 101: Real-time monitoring of the communication frequency band of the tracking UAV, determining the real-time signal data, and performing feature extraction on the real-time signal data to determine the signal feature vector; 102: extract the frequency hopping behavior of the tracking UAV and determine the frequency hopping data, and analyze the frequency hopping data to determine the frequency hopping type; 103: Based on the signal characteristics and frequency hopping type of the tracking UAV, determine the communication interference signal and frequency hopping interference signal of the UAV; 104: transmitting a communication interference signal and a frequency hopping interference signal, and obtaining interference signal data of the tracking UAV after transmitting the communication interference signal and the frequency hopping interference signal; 105: Based on the real-time signal data and interference signal data of the tracking UAV, dynamically adjust the communication interference signal and frequency hopping interference signal, and evaluate the countermeasure effect.

2. A tracking drone countermeasure method according to claim 1, characterized in that: Real-time monitoring of the communication frequency band of the tracking UAV, determining the real-time signal data, and extracting features from the real-time signal data to determine the signal feature vector, including: Determine the communication frequency band of the tracking UAV, monitor all signal activities within the communication frequency band of the tracking UAV in real time based on the spectrum analyzer, and determine the real-time signal data; Preprocess the real-time signal data and eliminate interference signals, and extract the real-time sub-spectrum data and real-time sub-power data corresponding to each timestamp in the real-time signal data after preprocessing and eliminating interference signals; All real-time sub-spectrum data are time-sorted based on the timestamp to determine the time spectrum data, and at the same time, all real-time power sub-data are time-sorted based on the timestamp to determine the time power data; The signal feature vector of the tracked UAV is determined based on the time spectrum data and the time power data.

3. A tracking drone countermeasure method according to claim 2, characterized in that: The signal feature vector of the tracking UAV is determined based on the time spectrum data and the time power data, including: Determine a window function, extract the earliest timestamp and the latest timestamp of all timestamps, and determine a sliding window of the time spectrum data based on the window function, the earliest timestamp, the latest timestamp, and the number of all timestamps; The time spectrum data is divided into a plurality of time sub-frequency domain data based on a sliding window, and a division sequence number of each time sub-frequency domain data is determined. Meanwhile, the time power data is divided into a plurality of time sub-power data based on a sliding window, and a division sequence number of each time sub-power data is determined. Applying a window function to each time sub-frequency domain data and each time sub-power data; Extracting the time sub-frequency domain data and the time sub-power data after applying the window function corresponding to the same segmentation number, and determining the first power spectrum density of each segmentation number; Performing logarithmic transformation on the first power spectrum density of each segmentation number to determine the second power spectrum density of each segmentation number; Determine the frequency range based on all the time sub-frequency domain data after the window function is applied, and at the same time, determine the power range based on all the time sub-power data after the window function is applied; Draw a power spectrum density graph based on the frequency range, power range, and second power spectrum densities of all segmentation numbers; The power spectrum density graph is analyzed to determine all signal features of the tracking UAV, and the signal feature vector is determined based on all signal features.

4. A tracking drone countermeasure method according to claim 2, characterized in that: Extract the frequency hopping behavior of the tracked UAV and determine the frequency hopping data, including: Extract each frequency hopping behavior and the corresponding timestamp in the real-time signal data after preprocessing and interference signal elimination, and determine the frequency hopping sub-data based on each frequency hopping behavior and all corresponding timestamps; The frequency hopping data is determined based on the frequency hopping sub-data of all the frequency hopping behaviors.

5. A tracking drone countermeasure method according to claim 4, characterized in that: Analyze the frequency hopping data to determine the frequency hopping type, including: Extracting the earliest timestamp of each frequency hopping behavior, sorting all frequency modulation behaviors based on the earliest timestamp, and determining a first sequence; Determine a frequency hopping sequence of each frequency hopping behavior based on the frequency hopping sub-data of each frequency hopping behavior in the frequency hopping data; in, represents the frequency hopping sequence of the i-th frequency hopping behavior in the first sequence, , Respectively represent the 1st frequency, the jth frequency, and the iN1th frequency of the i-th frequency hopping behavior in the first sequence, and iN1 represents the number of frequencies in the frequency hopping sub-data of the i-th frequency hopping behavior in the first sequence; Determine a comprehensive frequency hopping sequence of the tracking UAV based on the frequency hopping sequences of all frequency hopping behaviors in the first sequence; in, represents the comprehensive frequency hopping sequence of the tracking UAV, Respectively indicate frequency ,frequency ,frequency , N3 represents the number of frequencies in the integrated frequency hopping sequence; Determine the time interval sequence of the tracking UAV based on the earliest timestamp of every two frequency hopping behaviors in the first sequence; in, represents the time series of the tracking UAV, Respectively represent time intervals , time interval , time interval , N2 represents the number of time intervals in the time interval sequence; A frequency hopping type is determined based on the first sequence, the frequency hopping sequence, and the time interval sequence.

6. A tracking drone countermeasure method according to claim 5, characterized in that: Determining a frequency hopping type based on the first sequence, the frequency hopping sequence, and the time interval sequence includes: in, Indicates frequency The probability in frequency hopping data, The jth frequency of the ith frequency hopping behavior in the first sequence is equal to the frequency When the value is 1, represents the sum of the frequencies of all the frequency hopping sub-data in the frequency hopping data, N4 represents the number of frequency hopping sub-data in the frequency hopping data, Indicates time interval The probability in frequency hopping data, represents the time interval between the earliest timestamp of the ith frequency hopping behavior and the earliest timestamp of the i+1th frequency hopping behavior in the first sequence, Indicates that the time interval between the earliest timestamp of the i-th frequency hopping behavior in the first sequence and the earliest timestamp of the i+1-th frequency hopping behavior is equal to the time interval When the value is 1, represents the correlation value of all frequency hopping sequences in the first sequence, k represents the delay coefficient, represents the jth frequency of the i+kth frequency hopping behavior in the first sequence, represents the average frequency of the frequency hopping sequence of the i-th frequency hopping behavior in the first sequence, represents the average frequency of the integrated frequency hopping sequence, Indicates the frequency hopping type of the frequency hopping data. Indicates random frequency hopping, 2 means periodic frequency hopping, 3 indicates pseudo-random frequency hopping, 4 indicates other frequency hopping, A indicates condition A, , They represent the lower limit and upper limit of uniform probability respectively. , They represent the interval Poisson lower limit and interval Poisson upper limit respectively, B represents condition B, , Respectively represent the lower limit of concentration probability and the upper limit of concentration probability, FL and FU represent the fixed lower limit of interval and the fixed upper limit of interval, C represents condition C, RL and RU represent the lower limit of pseudo-random probability and the upper limit of pseudo-random probability, AL and AU represent the lower limit of interval pseudo-random probability and the upper limit of interval pseudo-random probability, Indicates frequency The probability in the frequency hopping data is within the set uniform probability range, Indicates time interval The probability in the frequency hopping data is within the set interval Poisson range, Indicates frequency The probability in the frequency hopping data is within the set concentration probability range, Indicates time interval The probability in the frequency hopping data is within a fixed range of the set interval, Indicates frequency The probability in the frequency hopping data is within the set pseudo-random probability range. Indicates time interval The probability in the frequency hopping data is within the pseudo-random range of the set interval, represents setting uniform probability quantity, represents the number of set interval Poisson, represents the number of set concentration probabilities, Indicates setting a fixed number of intervals. Indicates setting the number of pseudo-random probabilities, Indicates a pseudo-random number at a set interval.

7. A tracking drone countermeasure method according to claim 1, characterized in that: Transmit communication interference signals and frequency hopping interference signals, including: Determine a first power of the communication interference signal and a second power of the frequency modulation interference signal based on the signal strength of the tracking drone; A directional antenna is used to transmit an interference signal, and a power amplifier is used to enhance the strength of the communication interference signal based on a first power of the communication interference signal. At the same time, a power amplifier is used to enhance the strength of the FM interference signal based on a second power of the FM interference signal.

8. A tracking drone countermeasure system, characterized in that: include: Monitoring module: real-time monitoring of the communication frequency band of the tracking UAV, determining the real-time signal data, and extracting the features of the real-time signal data to determine the signal feature vector; Extraction module: extract the frequency hopping behavior of the tracking UAV and determine the frequency hopping data, and analyze the frequency hopping data to determine the frequency hopping type; Generation module: Determine the communication interference signal and frequency hopping interference signal of the UAV based on the signal characteristics and frequency hopping type of the tracking UAV; Transmitter module: transmits communication interference signals and frequency hopping interference signals, and obtains interference signal data of the tracking UAV after transmitting the communication interference signals and frequency hopping interference signals; Evaluation module: Based on the real-time signal data and interference signal data of the tracking UAV, dynamically adjust the communication interference signal and frequency hopping interference signal, and evaluate the countermeasure effect.

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