Monitoring and early warning system and method for countering unmanned aerial vehicle detection interference

Abstract

The invention relates to the technical field of unmanned aerial vehicle detection, in particular to a monitoring and early warning system and method for countering unmanned aerial vehicle detection interference, and in the monitoring and early warning system, a signal receiving module independently receives electric signals of multiple unmanned aerial vehicles through array space stations; the abnormal signal detection module performs feature extraction, and detects abnormal signals by using frequency feature change and a spectrum analysis technology; the time difference of arrival detection module calculates the time difference of arrival of each space station to the abnormal signal, and the angle of arrival detection module analyzes and determines the angle of arrival through a space covariance matrix; the cross positioning module synthesizes the time difference and the angle information to determine the position of the abnormal unmanned aerial vehicle; and the adaptive interference suppression module identifies the type of an abnormal unmanned aerial vehicle by using a feature extraction result and a machine learning model, selects a suppression strategy, controls an array space station to align to the position of the abnormal unmanned aerial vehicle, realizes efficient suppression, and protects normal operation of a legal unmanned aerial vehicle and other aerial equipment at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle detection, and in particular to a monitoring and early warning system and method for countering interference in unmanned aerial vehicle detection. Background Art

[0002] Traditional monitoring and early warning systems and methods for countering drone detection interference have technical problems in many aspects. First, these systems usually rely on a single signal arrival angle or signal arrival time difference method to locate drones; this single method often greatly reduces the accuracy and reliability of positioning in complex environments, especially multipath propagation and strong noise interference. For example, relying solely on the arrival time difference method, although it can provide accurate time information, the complexity and reflection of the signal path in a multipath propagation environment will lead to uncertainty in time information, thereby affecting positioning accuracy; at the same time, relying solely on the arrival angle method, due to the presence of noise and spatial interference, the angle information may be unstable, resulting in inaccurate positioning results.

[0003] Secondly, the detection of abnormal signals in traditional systems is usually relatively simple, relying on simple monitoring of frequency feature changes or signal strength, and lacking multi-dimensional feature analysis, resulting in inefficient recognition and classification of abnormal signals; for simultaneous intrusions of multiple drones, traditional systems are often unable to quickly and effectively distinguish and locate multiple targets, further increasing system response time and processing complexity. In addition, traditional systems lack intelligent means, cannot dynamically adapt and adjust countermeasure strategies, can only rely on preset fixed modes for interference, and cannot cope with the changing types of drone threats and attack methods, which greatly limits the flexibility and response capabilities of the system.

[0004] Finally, in actual operation, traditional systems are often unable to achieve precise interference with the target and can only carry out wide-coverage interference, which can easily affect the normal operation of legitimate communication equipment and aerial equipment, causing false interference and waste of resources. Traditional methods that lack refined control means cannot flexibly adjust the power, frequency and modulation method of the interference signal, have poor adaptability to the environment, and cannot ensure the efficient operation and long-term stability of the system. Therefore, traditional monitoring and early warning systems and methods for countering drone detection interference have significant technical problems in positioning accuracy, multi-target processing, response speed and interference accuracy, and are in urgent need of improvement and optimization. Summary of the invention

[0005] The purpose of the present invention is to provide a monitoring and early warning system and method for countering UAV detection interference, so as to solve the problems raised in the above background technology.

[0006] To achieve the above-mentioned purpose, one of the purposes of the present invention is to provide a monitoring and early warning system for countering UAV detection interference, including a signal receiving module, an abnormal signal detection module, an arrival time difference detection module, an arrival angle detection module, a cross positioning module and an adaptive interference suppression module, wherein:

[0007] The signal receiving module uses an array space site composed of multiple space sites to receive electrical signals sent by multiple drones as multiple drone electrical signals; wherein each space site in the array space site independently receives multiple drone electrical signals, and each space site contains corresponding position information;

[0008] The abnormal signal detection module extracts features from the electrical signals of multiple drones, and the feature extraction results include time-frequency features, frequency features, and signal strength features. According to the changes in the frequency features in the feature extraction results, the spectrum analysis technology is used to detect abnormal signals in the electrical signals of multiple drones;

[0009] The arrival time difference detection module calculates the arrival time difference of each space station to the abnormal signal based on the abnormal signal detected by the abnormal signal detection module;

[0010] The arrival angle detection module constructs a spatial covariance matrix for the abnormal signal detected by the abnormal signal detection module; performs eigenvalue decomposition on the spatial covariance matrix, detects a spectrum peak according to the decomposition result, and determines the arrival angle of each spatial station to the abnormal signal according to the spectrum peak;

[0011] The cross-positioning module determines the drone position corresponding to the abnormal signal as the abnormal drone position according to the arrival time difference of each space station to the abnormal signal and the arrival angle of each space station to the abnormal signal;

[0012] The adaptive interference suppression module uses the feature extraction results corresponding to the abnormal signal as the input of the machine learning model to generate the type of abnormal drone; according to the type of abnormal drone, the corresponding suppression strategy is selected, and the array space site is aligned according to the position of the abnormal drone to suppress its interference with the effective signal.

[0013] As a further improvement of the present technical solution, the abnormal signal detection module includes a feature extraction unit, and the specific process of feature extraction in the feature extraction unit includes:

[0014] Time-frequency feature extraction: Use the window function to divide the electromagnetic signals of multiple drones into multiple time periods, apply the fast Fourier transform method to each time period, store the transformation results of each time period as a column of the time-frequency spectrum, and combine the transformation results of all time periods to form a two-dimensional time-frequency spectrum;

[0015] Frequency feature extraction: square the transformation result of each time period in the time-frequency feature extraction process and divide it by the frequency resolution to obtain the spectrum; divide the spectrum into multiple frequency bands, calculate the total energy in each frequency band, and determine the center of gravity of the spectrum based on the total energy to reflect the center of energy distribution; take the total energy of each frequency band and the center of gravity of the spectrum as the frequency feature;

[0016] Signal strength feature extraction: The overall energy level of the signal is obtained by calculating the root mean square value of the electrical signals of multiple drones; peak detection is performed on the electrical signals of multiple drones to find the maximum absolute value of the signal; the signal-to-noise ratio of the electrical signals of multiple drones is estimated to evaluate the signal quality and reliability; the envelope detection technology is used to extract the amplitude change trend of the electrical signals of multiple drones; and the above results are combined as signal strength features.

[0017] As a further improvement of the technical solution, the abnormal signal detection module includes an abnormality detection unit, and the specific process of the abnormality detection unit detecting abnormal signals in the electrical signals of multiple drones is as follows:

[0018] Collect the electrical signal data of multiple drones in normal operation from the public database, extract the frequency features of the collected normal electrical signal data, calculate the sum of the energy of each normal frequency band, and calculate the average value and standard deviation of the sum of the energy of each normal frequency band, as well as the average position and standard deviation of the center of gravity of the normal spectrum to determine the spectrum baseline;

[0019] Set the deviation threshold of the energy sum of each frequency band by defining the range of 3 standard deviations exceeding the average value of the normal frequency band energy sum in the spectrum baseline as the energy deviation threshold;

[0020] The spectrum center of gravity range is set by defining the average position of the normal spectrum center of gravity in the spectrum baseline ±2 standard deviations as the spectrum center of gravity deviation threshold;

[0021] Compare the relationship between the frequency feature extracted in the feature extraction unit and the energy deviation threshold and the spectrum center of gravity deviation threshold. If the total energy of any frequency band in the frequency feature exceeds the energy deviation threshold, or the center of gravity of the spectrum in the frequency feature exceeds the spectrum center of gravity deviation threshold, then the multiple UAV electromechanical signals corresponding to the frequency feature are determined to be abnormal signals. According to the spectrum position corresponding to the abnormal signal, the abnormal time period and abnormal frequency range are determined.

[0022] As a further improvement of the technical solution, the specific process of the arrival time difference detection module calculating the arrival time difference of each spatial station to the abnormal signal is as follows:

[0023] For the abnormal signal received by each space station, the first space station that detects the abnormal signal is selected as the reference space station, and the arrival time T_ref of the abnormal signal of the reference space station is determined according to the abnormal time period in the abnormal signal;

[0024] Calculate the cross-correlation function R_{ref,i}(τ) between the abnormal signal f_ref(t) of the reference space station and the abnormal signal f_i(t) of other space stations. This function represents the correlation degree of the two signals under the time difference τ. The peak position τ_max in this function corresponds to the arrival time difference ΔT_i=τ_max of the abnormal signal between other space stations and the reference space station.

[0025] For the time difference ΔT_i between each spatial station and the reference spatial station, the relative arrival time difference TDOA_i=T_i-T_ref of each spatial station is calculated as the arrival time difference of each spatial station for the abnormal signal.

[0026] As a further improvement of the technical solution, the process of the arrival angle detection module determining the arrival angle of each space station to the abnormal signal is specifically as follows:

[0027] The abnormal signal received by each spatial station is represented as a complex vector to form an M×N complex matrix X, where M is the number of spatial stations and N is the number of each time point in the abnormal segment of each station; the spatial covariance matrix R is obtained by calculating the product of X and its conjugate transpose X^H and dividing it by N, that is, R = ( / N)*X*X^H, R contains the correlation information of the signals between different spatial stations, and H represents the conjugate transpose operation of the matrix;

[0028] The spatial covariance matrix R is subjected to eigenvalue decomposition, and the vectors therein are divided into signal subspace and noise subspace. Using this decomposition, a MUSIC spectrum is constructed, and the spectrum peak is detected by searching for the peak of the MUSIC spectrum in the range of 0° to 360°. The angle corresponding to the spectrum peak is the estimated arrival angle of the abnormal signal, where the MUSIC spectrum is an angle estimation method based on subspace decomposition.

[0029] As a further improvement of the technical solution, the specific process of the cross-positioning module determining the position of the abnormal drone is as follows:

[0030] According to the arrival time difference TDOA_i of the abnormal signal received by each spatial station relative to the reference station, combined with the known spatial station location information, a nonlinear equation group is constructed; the equation group uses the unknown three-dimensional coordinates (x, y, z) of the drone as variables, the known coordinates of each spatial station and the measured TDOA_i value as known quantities, and the super-resolution technology is used to solve the nonlinear equation group; the nonlinear equation group is linearized by Taylor series expansion; the linearized equation group is iteratively solved using the least squares method or other optimization algorithms; in each iteration, the estimated drone position is updated, and the error between the theoretical arrival time difference at the new position and the actual measured value is calculated. By continuously adjusting the estimated position to minimize this error, it finally converges to an optimal solution as the preliminary drone position;

[0031] After determining the preliminary UAV position, a joint optimization problem is constructed based on the arrival angle of the abnormal signal of each spatial station in the arrival angle detection module and the preliminary UAV position;

[0032] A mathematical model containing the unknown UAV position is established. The variables in the mathematical model include the position information of each space station, as well as the arrival angle and arrival time difference of each space station to the abnormal signal. The objective function of the model contains two parts, one is the sum of squares of the arrival angle measurement error, and the other is the sum of squares of the arrival time difference measurement error. By minimizing the objective function, the positioning error caused by the time difference and angle measurement can be reduced at the same time.

[0033] During the iterative optimization process, the estimated position of the UAV is gradually adjusted. Each adjustment will calculate a new theoretical arrival angle and arrival time difference and compare them with the actual measured values ​​to correct the estimated position. After multiple iterations, an estimated position that minimizes the objective function is obtained as the abnormal UAV position.

[0034] As a further improvement of the technical solution, the adaptive interference suppression module receives the feature extraction results provided by the feature extraction unit, including time-frequency features, frequency features and signal strength features, and the features are used as inputs of a pre-trained machine learning model, which includes but is not limited to a deep neural network and a support vector machine;

[0035] The model outputs the abnormal drone type based on the input feature extraction results; according to the identified abnormal drone type, the most suitable interference suppression scheme is selected from the public suppression strategy library, including but not limited to frequency interference, signal deception and directed energy weapons;

[0036] Using the precise UAV location information provided by the cross-positioning module, the beamforming system of the array space site is controlled to align the main lobe of the antenna array with the abnormal UAV position. By adjusting the power, frequency and modulation method of the interference signal, the interference of abnormal UAVs to normal communication and navigation systems is suppressed, while protecting the normal operation of legitimate UAVs and other aerial equipment.

[0037] The second object of the present invention is to provide a method for a monitoring and early warning system to counter UAV detection interference, comprising the following method steps:

[0038] S1. Using an array space site composed of multiple space sites to receive electrical signals sent by multiple drones as multiple drone electrical signals; wherein each space site in the array space site independently receives multiple drone electrical signals, and each space site contains corresponding position information;

[0039] S2. Extract features from the electrical signals of multiple drones. The feature extraction results include time-frequency features, frequency features, and signal strength features. According to the changes in the frequency features in the feature extraction results, the spectrum analysis technology is used to detect abnormal signals in the electrical signals of multiple drones.

[0040] S3. Calculate the arrival time difference of each space station to the abnormal signal according to the detected abnormal signal;

[0041] S4. Construct a spatial covariance matrix for the detected abnormal signal; perform eigenvalue decomposition on the spatial covariance matrix, detect spectrum peaks according to the decomposition results, and determine the arrival angle of each spatial station to the abnormal signal according to the spectrum peaks;

[0042] S5. Determine the position of the UAV corresponding to the abnormal signal according to the arrival time difference of each space station to the abnormal signal and the arrival angle of each space station to the abnormal signal, as the abnormal UAV position;

[0043] S6. Use the feature extraction results corresponding to the abnormal signal as the input of the machine learning model to generate the type of abnormal drone; select the corresponding suppression strategy according to the type of abnormal drone, and align the array space sites according to the position of the abnormal drone to suppress its interference with the effective signal.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. A monitoring and early warning system and method for countering UAV detection interference determines the UAV position corresponding to the abnormal signal according to the arrival time difference of each space station to the abnormal signal and the arrival angle of each space station to the abnormal signal. As a method for abnormal UAV position, it has significant advantages over the single arrival angle and arrival time difference method; among them, by combining the two relatively independent information sources of arrival time difference and arrival angle, a more complete data set can be formed, thereby improving the accuracy and reliability of positioning; the arrival time difference correlation method provides accurate time information, but is often interfered in a multipath propagation environment, while the arrival angle correlation method is based on spatial angle information, which can effectively reduce the multipath propagation and noise caused by the noise. uncertainty; when these two methods are combined, they can make up for the defects of a single method and form more comprehensive positioning data. Through the iterative optimization process, the estimated position is gradually adjusted, the measurement errors of the time difference and the angle of arrival are continuously reduced, and finally converge to the optimal solution, thereby significantly improving the accuracy of drone positioning; this multiple information fusion method can reliably provide accurate location information of abnormal drones in various complex environments, providing a solid foundation for subsequent intervention measures and safety management; using accurate drone location information, the system can control the beamforming system of the array space station to accurately align the main lobe of the antenna array with the abnormal drone position. This precise alignment can not only maximize the interference effect, but also minimize the impact on the surrounding environment. By adjusting the power, frequency and modulation method of the interference signal, efficient suppression of abnormal drones can be achieved, while protecting the normal operation of legitimate drones and other aerial equipment.

[0046] 2. The monitoring and early warning system and method for countering UAV detection interference uses spectrum analysis technology to detect abnormal signals in the electrical signals of multiple UAVs according to the frequency feature changes in the feature extraction results, and uses the feature extraction results corresponding to the abnormal signals as the input of the machine learning model to generate the abnormal UAV type, which can greatly improve the flexibility and response ability of the system; the feature extraction unit performs comprehensive feature extraction on the electrical signals of multiple UAVs, including time-frequency features, frequency features and signal strength features, and then uses these features as input. The pre-trained machine learning model can efficiently identify different types of abnormal UAVs; the detection of frequency feature changes can quickly identify anomalies in the signal, thereby identifying and locating possible threats at an early stage; then, the adaptive interference suppression module selects the most appropriate one from a variety of interference strategies according to the identified UAV type, and uses the beamforming system of the array space site to perform high-precision interference on the target; this process enables the system to adapt to new threat types in real time through continuous updating and optimization of the machine learning model, and dynamically adjusts the interference mode and intensity, ultimately ensuring the effective suppression of the interference of abnormal signals to normal communication and navigation systems, and realizing the construction of an efficient and safe monitoring and early warning system in a comprehensive environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the overall module unit of the present invention;

[0048] Figure 2 It is a schematic diagram of the overall method flow of the present invention.

[0049] In the figure: 100, signal receiving module; 200, abnormal signal detection module; 201, feature extraction unit; 202, abnormal detection unit; 300, arrival time difference detection module; 400, arrival angle detection module; 500, cross positioning module; 600, adaptive interference suppression module. DETAILED DESCRIPTION

[0050] The following will be combined with the accompanying drawings in 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.

[0051] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0052] According to the document, the following are some definitions of terms:

[0053] Multiple drone electrical signals: refers to electromagnetic wave signals emitted by multiple drones at the same time, which are received by the array space station;

[0054] Abnormal signal: refers to a signal among multiple drone electrical signals that is identified as not conforming to the normal drone communication mode after frequency characteristic analysis. This signal may manifest as abnormal frequency band energy or spectrum center of gravity shift;

[0055] Arrival time difference: refers to the time difference between the same abnormal signal arriving at different spatial stations. By calculating the time difference between the reference station and other stations receiving the abnormal signal, it can be used to determine the location of the signal source (i.e. the abnormal drone).

[0056] Angle of arrival: refers to the incident angle of the abnormal signal arriving at each space station. It is estimated by analyzing the spatial covariance matrix of the received signal using methods such as the MUSIC algorithm. This angle information is used together with the arrival time difference to accurately locate the abnormal drone;

[0057] Abnormal drone location: refers to the precise coordinates of the drone that is identified as emitting abnormal signals in three-dimensional space. This location is calculated by comprehensively utilizing the arrival time difference and arrival angle information and using cross-positioning technology. It provides key spatial information for subsequent interference suppression and emergency response.

[0058] Next, see Figure 1-Figure 2 One of the purposes of this embodiment is to provide a monitoring and early warning system for countering UAV detection interference, including a signal receiving module 100, an abnormal signal detection module 200, an arrival time difference detection module 300, an arrival angle detection module 400, a cross positioning module 500 and an adaptive interference suppression module 600.

[0059] The signal receiving module 100 uses an array space site composed of multiple space sites to receive electrical signals sent by multiple drones as multiple drone electrical signals; each space site in the array space site independently receives multiple drone electrical signals, each space site contains corresponding position information, and each space site is arranged in a specific geometric shape, such as a linear, two-dimensional plane or a three-dimensional structure, to achieve more efficient signal reception and processing;

[0060] The feature extraction unit 201 in the abnormal signal detection module 200 extracts features from the electrical signals of multiple drones. The feature extraction results include time-frequency features, frequency features, and signal strength features. The specific process of feature extraction includes:

[0061] Time-frequency feature extraction: Use a window function (such as Hanning window) to divide the electromagnetic signals of multiple drones into multiple time periods, apply the fast Fourier transform method to each time period, store the transformation results of each time period as a column of the time-frequency spectrum, and combine the transformation results of all time periods to form a two-dimensional time-frequency spectrum;

[0062] Frequency feature extraction: square the transformation result of each time period in the time-frequency feature extraction process and divide it by the frequency resolution to obtain the spectrum; divide the spectrum into multiple frequency bands, calculate the total energy in each frequency band, and determine the center of gravity of the spectrum based on the total energy to reflect the center of energy distribution; take the total energy of each frequency band and the center of gravity of the spectrum as the frequency feature;

[0063] Signal strength feature extraction: The overall energy level of the signal is obtained by calculating the root mean square value of the electrical signals of multiple drones; peak detection is performed on the electrical signals of multiple drones to find the maximum absolute value of the signal, which helps to identify sudden or abnormally strong signals; the signal-to-noise ratio of the electrical signals of multiple drones is estimated to evaluate the signal quality and reliability; envelope detection technology (such as Hilbert transform) is used to extract the amplitude change trend of the electrical signals of multiple drones; the above results are combined as signal strength features to provide an important basis for subsequent drone classification.

[0064] The abnormality detection unit 202 in the abnormal signal detection module 200 detects abnormal signals in the electrical signals of multiple drones using spectrum analysis technology according to the frequency feature changes in the feature extraction results, specifically including:

[0065] Collect the electrical signal data of multiple drones in normal operation from the public database, extract the frequency features of the collected normal electrical signal data, calculate the sum of the energy of each normal frequency band, and calculate the average value and standard deviation of the sum of the energy of each normal frequency band, as well as the average position and standard deviation of the center of gravity of the normal spectrum to determine the spectrum baseline;

[0066] Set the deviation threshold of the energy sum of each frequency band by defining the range of 3 standard deviations exceeding the average value of the normal frequency band energy sum in the spectrum baseline as the energy deviation threshold;

[0067] The spectrum center of gravity range is set by defining the average position of the normal spectrum center of gravity in the spectrum baseline ±2 standard deviations as the spectrum center of gravity deviation threshold;

[0068] Compare the relationship between the frequency feature extracted in the feature extraction unit 201 and the energy deviation threshold and the spectrum center of gravity deviation threshold. If the total energy of any frequency band in the frequency feature exceeds the energy deviation threshold, or the center of gravity of the spectrum in the frequency feature exceeds the spectrum center of gravity deviation threshold, it is determined that the multiple drone electrical and electronic signals corresponding to the frequency feature are abnormal signals, and the abnormal time period and abnormal frequency range are determined according to the spectrum position corresponding to the abnormal signal.

[0069] The arrival time difference detection module 300 calculates the arrival time difference of each space station to the abnormal signal according to the abnormal time period in the abnormal signal detected by the abnormality detection unit 202, specifically including:

[0070] For the abnormal signal received by each space station, the first space station that detects the abnormal signal is selected as the reference space station, and the arrival time T_ref of the abnormal signal of the reference space station is determined according to the abnormal time period in the abnormal signal;

[0071] Calculate the cross-correlation function R_{ref,i}(τ) between the abnormal signal f_ref(t) of the reference space station and the abnormal signal f_i(t) of other space stations. This function represents the correlation degree of the two signals under the time difference τ. The peak position τ_max in this function corresponds to the arrival time difference ΔT_i=τ_max of the abnormal signal between other space stations and the reference space station.

[0072] For the time difference ΔT_i between each space station and the reference space station, the relative arrival time difference TDOA_i = T_i-T_ref of each space station is calculated as the arrival time difference of the abnormal signal at each space station. Through this cross-correlation method, the time difference of each space station receiving the abnormal signal can be accurately estimated in a complex multipath propagation and signal interference environment.

[0073] The arrival angle detection module 400 constructs a spatial covariance matrix for the abnormal signal detected by the abnormality detection unit 202; performs eigenvalue decomposition on the spatial covariance matrix, detects a spectrum peak according to the decomposition result, and determines the arrival angle of each spatial station to the abnormal signal according to the spectrum peak, specifically including:

[0074] The abnormal signal received by each spatial station is represented as a complex vector to form an M×N complex matrix X, where M is the number of spatial stations and N is the number of each time point in the abnormal segment of each station; the spatial covariance matrix R is obtained by calculating the product of X and its conjugate transpose X^H and dividing it by N, that is, R = (1 / N)*X*X^H. R contains the correlation information of the signals between different spatial stations, reflects the distribution characteristics of the signals in space, and provides a basis for the subsequent eigenvalue decomposition and arrival angle estimation. H represents the conjugate transpose operation of the matrix;

[0075] The spatial covariance matrix R is subjected to eigenvalue decomposition, and the vectors therein are divided into signal subspace and noise subspace. Using this decomposition, a MUSIC spectrum is constructed, and the spectrum peak is detected by searching for the peak of the MUSIC spectrum in the range of 0° to 360°. The angle corresponding to the spectrum peak is the estimated arrival angle of the abnormal signal, where the MUSIC spectrum is an angle estimation method based on subspace decomposition.

[0076] The cross positioning module 500 determines the drone position corresponding to the abnormal signal as the abnormal drone position according to the arrival time difference of each spatial station to the abnormal signal and the arrival angle of each station to the abnormal signal, which specifically includes:

[0077] According to the arrival time difference TDOA_i of the abnormal signal received by each spatial station relative to the reference station, combined with the known spatial station location information, a nonlinear equation group is constructed; the equation group uses the unknown three-dimensional coordinates (x, y, z) of the drone as variables, the known coordinates of each spatial station and the measured TDOA_i value as known quantities, and the super-resolution technology is used to solve the nonlinear equation group; the nonlinear equation group is linearized by Taylor series expansion; the linearized equation group is iteratively solved using the least squares method or other optimization algorithms; in each iteration, the estimated drone position is updated, and the error between the theoretical arrival time difference at the new position and the actual measured value is calculated. By continuously adjusting the estimated position to minimize this error, it finally converges to an optimal solution as the preliminary drone position;

[0078] After determining the preliminary UAV position, the arrival angle information provided by the arrival angle detection module 400 is further used to optimize the positioning result. Specifically, according to the arrival angle of the abnormal signal of each spatial station in the arrival angle detection module 400, combined with the preliminary UAV position obtained by the arrival time difference method, a new joint optimization problem is constructed, which considers both the arrival angle and the arrival time difference.

[0079] A mathematical model containing the unknown UAV position is established. The variables include the position information of each space station, as well as the arrival angle and arrival time difference of each space station to the abnormal signal. The objective function of the model contains two parts, one is the sum of squares of the arrival angle measurement error, and the other is the sum of squares of the arrival time difference measurement error. By minimizing the objective function, the positioning error caused by the time difference and angle measurement can be reduced at the same time.

[0080] During the iterative optimization process, the estimated position of the drone is gradually adjusted. Each time the adjustment is made, a new theoretical arrival angle and arrival time difference are calculated and compared with the actual measured values ​​to correct the estimated position. After multiple iterations, an estimated position that minimizes the objective function is finally obtained as the abnormal drone position. This process achieves more accurate positioning of the drone position by comprehensively utilizing two independent types of spatial information, the arrival angle and the arrival time difference.

[0081] Compared with the single arrival angle and arrival time difference method, this joint positioning method significantly improves the positioning accuracy and reduces the impact of factors such as noise and multipath propagation. Ultimately, the cross-positioning module 500 outputs the precise location information of the abnormal UAV. These precise coordinate data provide key basis for subsequent emergency response, intervention measures and safety management, enabling relevant departments to take necessary actions quickly and accurately to ensure regional safety.

[0082] The adaptive interference suppression module 600 receives the feature extraction results provided by the feature extraction unit 201, including time-frequency features, frequency features, and signal strength features, which are used as inputs of a pre-trained machine learning model, including but not limited to a deep neural network, a support vector machine, etc., which is specifically used to identify and classify different types of abnormal drones;

[0083] Based on the input feature extraction results, the model outputs the type of abnormal drones, such as commercial, military or homemade drones. According to the identified abnormal drone type, the most suitable interference suppression scheme is selected from the public suppression strategy library, including but not limited to frequency interference, signal deception and directed energy weapons.

[0084] By using the precise UAV position information provided by the cross-positioning module 500, the beamforming system of the array space site is controlled to align the main lobe of the antenna array with the abnormal UAV position. This precise alignment can maximize the interference effect while minimizing the impact on the surrounding environment. By adjusting the power, frequency and modulation method of the interference signal, the interference of abnormal UAVs to normal communication and navigation systems can be effectively suppressed, while protecting the normal operation of legitimate UAVs and other aerial equipment. This adaptive interference suppression method can not only quickly respond to different types of UAV threats, but also dynamically adjust the suppression strategy according to real-time conditions to ensure the optimization of interference effects and efficient use of system resources.

[0085] The second purpose of this embodiment is to provide a method for a monitoring and early warning system to counter UAV detection interference, including the following method steps:

[0086] S1. Using an array space site composed of multiple space sites to receive electrical signals sent by multiple drones as multiple drone electrical signals; wherein each space site in the array space site independently receives multiple drone electrical signals, and each space site contains corresponding position information;

[0087] S2. Extract features from the electrical signals of multiple drones. The feature extraction results include time-frequency features, frequency features, and signal strength features. According to the changes in the frequency features in the feature extraction results, the spectrum analysis technology is used to detect abnormal signals in the electrical signals of multiple drones.

[0088] S3. Calculate the arrival time difference of each space station to the abnormal signal according to the detected abnormal signal;

[0089] S4. Construct a spatial covariance matrix for the detected abnormal signal; perform eigenvalue decomposition on the spatial covariance matrix, detect spectrum peaks according to the decomposition results, and determine the arrival angle of each spatial station to the abnormal signal according to the spectrum peaks;

[0090] S5. Determine the position of the UAV corresponding to the abnormal signal according to the arrival time difference of each space station to the abnormal signal and the arrival angle of each space station to the abnormal signal, as the abnormal UAV position;

[0091] S6. Use the feature extraction results corresponding to the abnormal signal as the input of the machine learning model to generate the type of abnormal drone; select the corresponding suppression strategy according to the type of abnormal drone, and align the array space sites according to the position of the abnormal drone to suppress its interference with the effective signal.

[0092] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A monitoring and early warning system for countering UAV detection interference, characterized in that: The system comprises a signal receiving module (100), an abnormal signal detection module (200), an arrival time difference detection module (300), an arrival angle detection module (400), a cross positioning module (500) and an adaptive interference suppression module (600), wherein: The signal receiving module (100) uses an array space site composed of a plurality of space sites to receive electrical signals sent by a plurality of drones as electrical signals of a plurality of drones; wherein each space site in the array space site independently receives electrical signals of a plurality of drones, and each space site contains corresponding position information; The abnormal signal detection module (200) extracts features from the electrical signals of the plurality of drones, wherein the feature extraction results include time-frequency features, frequency features and signal strength features, and detects abnormal signals from the electrical signals of the plurality of drones using spectrum analysis technology according to changes in the frequency features in the feature extraction results; The arrival time difference detection module (300) calculates the arrival time difference of each space station to the abnormal signal based on the abnormal signal detected by the abnormal signal detection module (200); The arrival angle detection module (400) constructs a spatial covariance matrix for the abnormal signal detected by the abnormal signal detection module (200); performs eigenvalue decomposition on the spatial covariance matrix, detects spectrum peaks based on the decomposition results, and determines the arrival angle of each spatial station to the abnormal signal based on the spectrum peaks; The cross-positioning module (500) determines the drone position corresponding to the abnormal signal as the abnormal drone position according to the arrival time difference of each space station to the abnormal signal and the arrival angle of each space station to the abnormal signal; The adaptive interference suppression module (600) uses the feature extraction result corresponding to the abnormal signal as the input of the machine learning model to generate the abnormal drone type; selects the corresponding suppression strategy according to the abnormal drone type, and aligns the array space site according to the abnormal drone position to suppress its interference with the effective signal.

2. The monitoring and early warning system for countering UAV detection interference according to claim 1 is characterized in that: The abnormal signal detection module (200) comprises a feature extraction unit (201), and the specific process of feature extraction in the feature extraction unit (201) comprises: Time-frequency feature extraction: Use the window function to divide the electromagnetic signals of multiple drones into multiple time periods, apply the fast Fourier transform method to each time period, store the transformation results of each time period as a column of the time-frequency spectrum, and combine the transformation results of all time periods to form a two-dimensional time-frequency spectrum; Frequency feature extraction: square the transformation result of each time period in the time-frequency feature extraction process and divide it by the frequency resolution to obtain the spectrum; divide the spectrum into multiple frequency bands, calculate the total energy in each frequency band, and determine the center of gravity of the spectrum based on the total energy to reflect the center of energy distribution; take the total energy of each frequency band and the center of gravity of the spectrum as the frequency feature; Signal strength feature extraction: The overall energy level of the signal is obtained by calculating the root mean square value of the electrical signals of multiple drones; peak detection is performed on the electrical signals of multiple drones to find the maximum absolute value of the signal; the signal-to-noise ratio of the electrical signals of multiple drones is estimated to evaluate the signal quality and reliability; the envelope detection technology is used to extract the amplitude change trend of the electrical signals of multiple drones; and the above results are combined as signal strength features.

3. The monitoring and early warning system for countering UAV detection interference according to claim 2 is characterized in that: The abnormal signal detection module (200) comprises an abnormality detection unit (202), and the specific process of the abnormality detection unit (202) detecting abnormal signals in the electrical signals of multiple drones is as follows: Collect the electrical signal data of multiple drones in normal operation from the public database, extract the frequency features of the collected normal electrical signal data, calculate the sum of the energy of each normal frequency band, and calculate the average value and standard deviation of the sum of the energy of each normal frequency band, as well as the average position and standard deviation of the center of gravity of the normal spectrum to determine the spectrum baseline; Set the deviation threshold of the energy sum of each frequency band by defining the range of 3 standard deviations exceeding the average value of the normal frequency band energy sum in the spectrum baseline as the energy deviation threshold; The spectrum center of gravity range is set by defining the average position of the normal spectrum center of gravity in the spectrum baseline ±2 standard deviations as the spectrum center of gravity deviation threshold; Compare the relationship between the frequency feature extracted in the feature extraction unit 201 and the energy deviation threshold and the spectrum center of gravity deviation threshold. If the total energy of any frequency band in the frequency feature exceeds the energy deviation threshold, or the center of gravity of the spectrum in the frequency feature exceeds the spectrum center of gravity deviation threshold, it is determined that the multiple drone electrical and electronic signals corresponding to the frequency feature are abnormal signals, and the abnormal time period and abnormal frequency range are determined according to the spectrum position corresponding to the abnormal signal.

4. The monitoring and early warning system for countering UAV detection interference according to claim 3 is characterized in that: The specific process of the arrival time difference detection module (300) calculating the arrival time difference of each space station to the abnormal signal is as follows: For the abnormal signal received by each space station, the first space station that detects the abnormal signal is selected as the reference space station, and the arrival time T_ref of the abnormal signal of the reference space station is determined according to the abnormal time period in the abnormal signal; Calculate the cross-correlation function R_{ref,i}(τ) between the abnormal signal f_ref(t) of the reference space station and the abnormal signal f_i(t) of other space stations. This function represents the correlation degree of the two signals under the time difference τ. The peak position τ_max in this function corresponds to the arrival time difference ΔT_i=τ_max of the abnormal signal between other space stations and the reference space station. For the time difference ΔT_i between each spatial station and the reference spatial station, the relative arrival time difference TDOA_i=T_i-T_ref of each spatial station is calculated as the arrival time difference of each spatial station for the abnormal signal.

5. The monitoring and early warning system for countering UAV detection interference according to claim 4 is characterized in that: The process of the arrival angle detection module (400) determining the arrival angle of each space station to the abnormal signal is specifically as follows: The abnormal signal received by each spatial station is represented as a complex vector to form an M×N complex matrix X, where M is the number of spatial stations and N is the number of each time point in the abnormal segment of each station; the spatial covariance matrix R is obtained by calculating the product of X and its conjugate transpose X^H and dividing it by N, that is, R = (1 / N)*X*X^H, R contains the correlation information of the signals between different spatial stations, and H represents the conjugate transpose operation of the matrix; The spatial covariance matrix R is subjected to eigenvalue decomposition, and the vectors therein are divided into signal subspace and noise subspace. Using this decomposition, a MUSIC spectrum is constructed, and the spectrum peak is detected by searching for the peak of the MUSIC spectrum in the range of 0° to 360°. The angle corresponding to the spectrum peak is the estimated arrival angle of the abnormal signal, where the MUSIC spectrum is an angle estimation method based on subspace decomposition.

6. The monitoring and early warning system for countering UAV detection interference according to claim 5 is characterized in that: The specific process of the cross-location module (500) determining the position of the abnormal drone is as follows: According to the arrival time difference TDOA_i of the abnormal signal received by each space station relative to the reference station, combined with the known space station location information, a nonlinear equation group is constructed; the equation group uses the unknown three-dimensional coordinates (x, y, z) of the drone as variables, the known coordinates of each space station and the measured TDOA_i value as known quantities, and the nonlinear equation group is solved by super-resolution technology; the nonlinear equation group is linearized by Taylor series expansion; the linearized equation group is iteratively solved by the least squares method or other optimization algorithms; In each iteration, the estimated drone position is updated, and the error between the theoretical arrival time difference at the new position and the actual measured value is calculated. By continuously adjusting the estimated position to minimize this error, it eventually converges to an optimal solution as the preliminary drone position. After determining the preliminary UAV position, constructing a joint optimization problem based on the arrival angle of the abnormal signal of each spatial station in the arrival angle detection module (400) and the preliminary UAV position; A mathematical model containing the unknown UAV position is established. The variables in the mathematical model include the position information of each space station, as well as the arrival angle and arrival time difference of each space station to the abnormal signal. The objective function of the model contains two parts, one is the sum of squares of the arrival angle measurement error, and the other is the sum of squares of the arrival time difference measurement error. By minimizing the objective function, the positioning error caused by the time difference and angle measurement can be reduced at the same time. During the iterative optimization process, the estimated position of the drone is gradually adjusted. Each adjustment calculates a new theoretical arrival angle and arrival time difference and compares them with the actual measured values ​​to correct the estimated position. After multiple iterations, an estimated position that minimizes the objective function is obtained as the abnormal UAV position.

7. The monitoring and early warning system for countering UAV detection interference according to claim 6 is characterized in that: The adaptive interference suppression module (600) receives the feature extraction results provided by the feature extraction unit (201), including time-frequency features, frequency features and signal strength features, and the features are used as inputs of a pre-trained machine learning model, the model including but not limited to a deep neural network and a support vector machine; The model outputs the abnormal drone type based on the input feature extraction results; according to the identified abnormal drone type, the most suitable interference suppression scheme is selected from the public suppression strategy library, including but not limited to frequency interference, signal deception and directed energy weapons; The accurate drone position information provided by the cross-positioning module (500) is used to control the beamforming system of the array space station, aim the main lobe of the antenna array at the abnormal drone position, and suppress the interference of the abnormal drone to the normal communication and navigation system by adjusting the power, frequency and modulation mode of the interference signal, while protecting the normal operation of legal drones and other aerial equipment.

8. A method for using a monitoring and early warning system for countering UAV detection interference according to any one of claims 1 to 7, characterized in that: The method comprises the following steps: S1. Using an array space site composed of multiple space sites to receive electrical signals sent by multiple drones as multiple drone electrical signals; wherein each space site in the array space site independently receives multiple drone electrical signals, and each space site contains corresponding position information; S2. Extract features from the electrical signals of multiple drones. The feature extraction results include time-frequency features, frequency features, and signal strength features. According to the changes in the frequency features in the feature extraction results, the spectrum analysis technology is used to detect abnormal signals in the electrical signals of multiple drones. S3. Calculate the arrival time difference of each space station to the abnormal signal according to the detected abnormal signal; S4, constructing a spatial covariance matrix for the detected abnormal signal; and performing eigenvalue decomposition on the spatial covariance matrix, and detecting spectrum peaks according to the decomposition results, and determining the arrival angle of each spatial station to the abnormal signal according to the spectrum peaks; S5. Determine the position of the UAV corresponding to the abnormal signal according to the arrival time difference of each space station to the abnormal signal and the arrival angle of each space station to the abnormal signal, as the abnormal UAV position; S6. Use the feature extraction results corresponding to the abnormal signal as the input of the machine learning model to generate the type of abnormal drone; select the corresponding suppression strategy according to the type of abnormal drone, and align the array space sites according to the position of the abnormal drone to suppress its interference with the effective signal.

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