Black flight unmanned aerial vehicle navigation deception method and system for unmanned aerial vehicle countermeasure

Abstract

The present application relates to the technical field of navigation deception, in particular to a black flying UAV navigation deception method and system for UAV countermeasures. The method acquires the positions of a black flying UAV and visible satellites, and constructs a signal coming direction coordinate system with the black flying UAV as the origin; according to the angle distribution density and outlier situation among the visible satellites in the signal coming direction coordinate system, an initial central satellite is screened; based on the preset deception trajectory of the black flying UAV, a future expected position is predicted, according to the angle change situation of the visible satellites relative to the initial central satellite at the future expected position and the angle distance in the signal coming direction coordinate system, a comprehensive membership is acquired; according to the initial central satellite and the comprehensive membership, the final central satellite of a satellite cluster is acquired through a weighted fuzzy C-means clustering algorithm, and then a deception launch coordinate is acquired to deceive the navigation of the black flying UAV. The present application accurately acquires the deception launch coordinate, effectively improving the accuracy of navigation deception on the black flying UAV.

Description

Technical Field

[0001] This invention relates to the field of navigation deception technology, specifically to a navigation deception method and system for countering unmanned aerial vehicles (UAVs) flying illegally. Background Technology

[0002] With the rapid development and widespread adoption of drone technology, low-altitude security issues have become increasingly prominent. Incidents of unauthorized drones intruding into sensitive areas, disrupting aviation order, and even threatening public safety are frequent, posing a severe challenge to the existing low-altitude security system. Countermeasures against unauthorized drones mainly include physical destruction (such as laser weapons and interception networks), suppression and interference (such as communication frequency blocking), and navigation deception. Among these, navigation deception technology injects false signals synchronized with and logically consistent with real satellite signals into the navigation system of unauthorized drones, causing them to calculate incorrect position or speed information. This leads to deviations from their flight path, forced landings, or controlled return. Compared to physical destruction and suppression and interference, navigation deception has significant advantages such as less collateral damage, higher concealment, and greater controllability, and has become a research hotspot in the field of drone defense.

[0003] It is known that unauthorized drones flying illegally are generally equipped with array antennas, possessing beamforming and null filtering capabilities. They can distinguish between real and decoy signals by detecting the direction-of-arrival distribution characteristics of received signals. Existing drone navigation decoy technologies use aerial drones as decoy nodes, but lack dynamic cooperative algorithms specifically for unauthorized drones flying illegally. This means that the perspective of an unauthorized drone relative to a satellite constantly changes during its movement. If the decoy drone cannot adjust its position or select the optimal signal simulation angle in real time, the decoy signal cannot achieve continuous and covert deception throughout the entire process, making it easily recognizable by the array antenna and preventing effective navigation decoy of the unauthorized drone. Summary of the Invention

[0004] To address the technical problem that existing drone navigation deception technologies cannot accurately deceive and navigate unauthorized drones, the present invention aims to provide a method and system for deceiving and spoofing unauthorized drones used in drone countermeasures. The specific technical solution adopted is as follows:

[0005] In a first aspect, one embodiment of the present invention provides a navigation deception method for countering unmanned aerial vehicles (UAVs) flying illegally, the method comprising the following steps:

[0006] The location of the unauthorized UAV and the location of each visible satellite in the airspace are obtained, and a signal orientation coordinate system with the unauthorized UAV as the origin is constructed. The signal orientation coordinate system is composed of the pitch angle and azimuth angle of the visible satellite relative to the unauthorized UAV.

[0007] Based on the angular distribution density among visible satellites and the outlier status of visible satellites in the signal direction coordinate system, the initial center satellite corresponding to the initial center of the satellite cluster is selected.

[0008] Based on the preset deception trajectory of the black-flying drone, the expected future position is predicted. According to the angle change of each visible satellite relative to each initial center satellite in the expected future position and the angular distance of the signal in the coordinate system, the comprehensive membership degree of each visible satellite to each initial center satellite is obtained. Based on the initial center satellite and the comprehensive membership degree, the final center satellite corresponding to the final center of the satellite cluster is obtained by weighted fuzzy C-means clustering algorithm.

[0009] Based on the final location of the central satellite, the coordinates of the decoy launch are obtained to deceive the navigation of the unauthorized drone.

[0010] Furthermore, the method for obtaining the signal-oriented coordinate system is as follows:

[0011] For any given moment, take the position of the unauthorized drone at that moment as the origin and establish a northeast celestial coordinate system with due east, due north, and the celestial direction as axes;

[0012] Calculate the elevation and azimuth angles of each visible satellite in the northeast celestial coordinate system at that moment;

[0013] A two-dimensional coordinate system is constructed with elevation angle as the horizontal axis and azimuth angle as the vertical axis. The normalized values ​​of elevation angle and azimuth angle of each visible satellite are mapped onto the two-dimensional coordinate system to obtain the signal direction coordinate system.

[0014] Furthermore, the method for obtaining the initial central satellite is as follows:

[0015] For any visible satellite, based on the difference in elevation and azimuth angles between the visible satellite and each other in the coordinate system according to the signal, the degree of signal deviation between the visible satellite and each other is obtained.

[0016] The minimum deviation of the signal is used as the reference signal deviation of the visible satellite;

[0017] Based on the reference signals of all visible satellites, the degree of deviation is determined, and the deviation segmentation threshold is obtained by using the maximum inter-class variance method.

[0018] Based on the deviation segmentation threshold and the degree of signal orientation deviation, the neighborhood density and neighborhood orientation consistency of the visible satellite are obtained;

[0019] The outlier weight of each visible satellite is obtained based on the neighborhood density and bias segmentation threshold of each visible satellite.

[0020] Based on the neighborhood density and neighborhood orientation consistency of each visible satellite, the Topsis algorithm is used to obtain a comprehensive score for each visible satellite.

[0021] The product of the outlier weight of each visible satellite and the overall score is used as the center selection degree of each visible satellite;

[0022] Arrange the center selection degree in descending order to obtain a selection degree sequence;

[0023] The visible satellites corresponding to the first preset number of center selection degrees in the selection degree sequence are used as the initial center satellites; where the preset number is the number of decoy drones.

[0024] Furthermore, the formula for calculating the degree of signal direction deviation is as follows: ; ; In the formula, The degree of signal direction deviation between the a-th visible satellite and the b-th visible satellite; The elevation angle between the a-th visible satellite and the b-th visible satellite. Difference; The azimuth angle between the a-th visible satellite and the b-th visible satellite. Difference; The elevation angle of the a-th visible satellite The normalized value; The elevation angle of the b-th visible satellite The normalized value; It is an absolute value function; The azimuth angle of the a-th visible satellite The normalized value; The azimuth angle of the b-th visible satellite The normalized value; min is the function that takes the minimum value.

[0025] Furthermore, the method for obtaining the neighborhood density and the degree of consistency of neighborhood directions is as follows:

[0026] Centered on the visible satellite and with the deviation segmentation threshold as the radius, the deviation reference neighborhood of the visible satellite is determined; other visible satellites whose signal direction deviation is within the deviation reference neighborhood are all regarded as neighboring satellites of the visible satellite.

[0027] The number of neighboring satellites is used as the neighborhood density of the visible satellite;

[0028] When the neighborhood density is equal to 0, the first preset constant is used as the neighborhood consistency of the visible satellite;

[0029] When the neighborhood density is not equal to 0, the result of negatively correlating the mean of the signal direction deviation between the visible satellite and the neighboring satellites is used as the direction consistency analysis value of the visible satellite.

[0030] The sum of the first preset constant and the direction-of-arrival consistency analysis value is taken as the direction-of-arrival consistency of the neighborhood of the visible satellite.

[0031] Furthermore, the method for obtaining the outlier weights is as follows:

[0032] The mean of the neighborhood density of all visible satellites is rounded up and used as the reference neighborhood density boundary value.

[0033] For any visible satellite, when the neighborhood density of the visible satellite is greater than or equal to the reference neighborhood density boundary value, the second preset constant is used as the outlier weight of the visible satellite.

[0034] When the neighborhood density of a visible satellite is less than the reference neighborhood density threshold, determine whether the neighborhood density of the visible satellite is 0.

[0035] If the neighborhood density of the visible satellite is 0, then the degree of deviation of the reference signal of the visible satellite is used as the deviation analysis value corresponding to the visible satellite.

[0036] If the neighborhood density of the visible satellite is not 0, then the average value of the signal direction deviation between the visible satellite and each of its neighboring satellites is used as the deviation analysis value corresponding to the visible satellite.

[0037] The ratio of the deviation analysis value to the deviation segmentation threshold is used as the outlier analysis value corresponding to the visible satellite; wherein, the deviation segmentation threshold is greater than 0.

[0038] The sum of the second preset constant and the outlier analysis value is used as the outlier weight of the visible satellite.

[0039] Furthermore, the method for obtaining the desired future location is as follows:

[0040] Using the center of the pre-set drone capture area as the target deception position, and based on the current position of the black-flying drone, the target deception position, and the estimated target position, the pre-set deception trajectory of the black-flying drone is obtained using continuous drone guidance technology based on circular trajectory.

[0041] By using the current flight speed and preset duration of the unauthorized drone, the position of the unauthorized drone at the end of each preset duration is obtained on the preset deception trajectory, which is used as the expected future position of the unauthorized drone.

[0042] Furthermore, the method for obtaining the comprehensive membership degree is as follows:

[0043] For any expected future location, a signal-oriented coordinate system is constructed with the expected future location as the origin, which serves as the reference coordinate system for the expected future location.

[0044] For any visible satellite and any initial center satellite, the degree of signal direction deviation between the visible satellite and the initial center satellite in the reference coordinate system of each future expected position is obtained and used as the reference deviation analysis value between the visible satellite and the initial center satellite.

[0045] The average of all the aforementioned reference deviation analysis values ​​is taken as the first decoy offset degree of the visible satellite relative to the initial center satellite;

[0046] The degree of signal deviation between the visible satellite and the initial center satellite in the current signal-oriented coordinate system is used as the second deception offset degree of the visible satellite relative to the initial center satellite.

[0047] The product of the first preset weight and the first deception offset is used as the first analysis value; the product of the second preset weight and the second deception offset is used as the second analysis value; the sum of the first analysis value and the second analysis value is used as the overall offset of the visible satellite relative to the initial center satellite.

[0048] The overall offset of the visible satellite relative to any initial center satellite is used as the comparison offset.

[0049] The reciprocal of the sum of powers of the ratio of the overall offset to the offset of each comparison is taken as the comprehensive membership degree of the visible satellite to the initial center satellite.

[0050] Furthermore, the method for obtaining the decoy launch coordinates is as follows:

[0051] The signal of each final central satellite at the current moment is obtained to generate the normalized elevation angle and normalized azimuth angle in the coordinate system;

[0052] The normalized elevation angle and normalized azimuth angle corresponding to each final center satellite are denormalized to obtain the actual elevation angle and actual azimuth angle of each final center satellite. Combined with the preset decoy safety distance, the actual elevation angle and actual azimuth angle of each final center satellite are converted into coordinate positions in three-dimensional space, which are used as the decoy launch coordinates of the decoy drone.

[0053] Secondly, another embodiment of the present invention provides a navigation and deception system for countering unmanned aerial vehicles (UAVs) flying illegally. The system includes: a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above methods.

[0054] The present invention has the following beneficial effects:

[0055] This invention first constructs a signal direction coordinate system with the unauthorized UAV as the origin, which facilitates the subsequent geometrical quantification of the distribution characteristics of satellite signals across the entire airspace. Then, based on the angular distribution density among visible satellites and the outlier status of visible satellites within the signal direction coordinate system, it selects the initial center satellite corresponding to the initial center of the satellite cluster, improving the representativeness and comprehensiveness of the initial clustering centers. This facilitates rapid convergence of subsequent clustering algorithms and prevents the neglect of outlier satellites. Furthermore, it predicts the future desired position based on the pre-set decoy trajectory of the unauthorized UAV, laying the foundation for subsequent analysis of the signal direction stability during the dynamic decoy process. Finally, based on the angular change of each visible satellite relative to each initial center satellite at the future desired position and the angular distance in the signal direction coordinate system, it obtains the signal direction stability of each visible UAV. The comprehensive membership degree of each satellite to each initial center satellite accurately reflects the degree to which each visible satellite is adapted to a specific decoy source in the current and future spatiotemporal context. This is beneficial for finding the optimal decoy group that balances static realism and dynamic stability. Furthermore, based on the initial center satellite and the comprehensive membership degree, the final center satellite corresponding to the final center of the satellite cluster is obtained through a weighted fuzzy C-means clustering algorithm, accurately determining the optimal virtual satellite signal direction throughout the entire process. Then, based on the position of the final center satellite, the decoy launch coordinates are obtained, accurately determining the physical hovering position of the decoy drone. This allows for accurate decoy navigation of unauthorized drones, effectively improving the stealth and success rate of navigation decoy countermeasure array antenna null filtering, reducing the risk of decoy signals being identified and eliminated, and decreasing the complexity of multi-drone collaborative control. Attached Figure Description

[0056] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic flowchart of a method for navigation deception of unmanned aerial vehicles (UAVs) used for UAV countermeasures, provided in one embodiment of the present invention.

[0058] Figure 2 A flowchart illustrating a method for acquiring an initial central satellite according to an embodiment of the present invention;

[0059] Figure 3 This is a structural diagram of a navigation and deception system for countering unmanned aerial vehicles (UAVs) provided in one embodiment of the present invention.

[0060] Figure 4 This is a schematic diagram of a computer device provided according to an embodiment of the present invention. Detailed Implementation

[0061] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the navigation and deception method and system for countering unmanned aerial vehicles (UAVs) proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0063] The following description, in conjunction with the accompanying drawings, details the specific scheme of the navigation and deception method and system for countering unmanned aerial vehicles (UAVs) provided by this invention.

[0064] Example 1:

[0065] This invention proposes a navigation deception method for countering unmanned aerial vehicles (UAVs) flying illegally. Please refer to [link / reference]. Figure 1 The diagram illustrates a schematic flowchart of a method for deceiving and spoofing unmanned aerial vehicles (UAVs) used in countering unmanned aerial vehicles (UAVs) according to an embodiment of the present invention. The method includes the following steps:

[0066] Step S1: Obtain the position of the unauthorized UAV and the position of each visible satellite in the airspace, and construct a signal direction coordinate system with the unauthorized UAV as the origin; the signal direction coordinate system is composed of the pitch angle and azimuth angle of the visible satellite relative to the unauthorized UAV.

[0067] Specifically, this embodiment integrates radar detection equipment and a GPS receiver on the base station platform. Radar detection technology is used to detect, identify, locate, and continuously track unauthorized drones, acquiring their three-dimensional coordinates, flight speed, and heading in real time. Simultaneously, the GPS receiver receives satellite signals from the airspace in real time, obtaining the three-dimensional position information of each visible satellite within the airspace. Visible satellites refer to satellites within the airspace whose signals can be received by the GPS receiver. It should be noted that this embodiment uses a single unauthorized drone as an example for analysis; all instances of "unauthorized drone" in this embodiment refer to the same unauthorized drone.

[0068] For any given moment, using the location of the unmanned aerial vehicle (UAV) at that moment as the origin, establish a northeast-sky coordinate system with the east, north, and celestial (vertical to the plane) directions as axes. Calculate the elevation and azimuth angles of each visible satellite within this northeast-sky coordinate system: the angle between the line connecting the UAV and the visible satellite and the horizontal tangent plane (the plane formed by the east and north axes) is taken as the elevation angle, with a range of [value missing]. Calculate the projection of the line connecting the unmanned aerial vehicle (UAV) and the visible satellite onto the horizontal tangent plane. Use the angle between this projection and the due east direction as the azimuth angle, and define counter-clockwise rotation from due east as the positive direction. Therefore, the range of the azimuth angle is... The calculated pitch and azimuth angles were normalized using the minimum-maximum normalization method, mapping the ranges of both angles to the maximum normalization range. This yields the normalized pitch and azimuth angles. The min-max normalization method is a well-known technique and will not be elaborated upon further.

[0069] A two-dimensional coordinate system is constructed with the normalized elevation angle as the horizontal axis and the normalized azimuth angle as the vertical axis. The coordinates of each visible satellite in the airspace are mapped into this two-dimensional coordinate system, thereby obtaining the signal direction coordinate system with the position of the unauthorized UAV as the origin at that moment. This accurately reflects the distribution of the signal direction of all visible satellites in the view of the unauthorized UAV at that moment, which is beneficial for subsequent identification and simulation of the satellite's geometric configuration from the signal distribution characteristics using clustering algorithms.

[0070] Step S2: Based on the angular distribution density among visible satellites and the outlier status of visible satellites in the signal direction coordinate system, select the initial center satellite corresponding to the initial center of the satellite cluster.

[0071] Specifically, considering the characteristic that low-speed, small, unauthorized drones typically fly in a straight line along a predetermined course, this embodiment first presets the deception scenario: obtains the current real-time position, course, and flight speed of the unauthorized drone, calculates the product of the preset deception time (e.g., 3 minutes, which can be set according to actual countermeasure requirements) and the flight speed as the preset distance; and establishes the estimated target position of the unauthorized drone as the airspace point extending the preset distance along the course direction from the current position of the unauthorized drone as the origin.

[0072] Considering that the airspace where unauthorized drones fly typically contains multiple visible satellites, and each visible satellite has a unique signal direction relative to the drone, if the direction of the decoy signal differs too much from the direction of the real satellite signal, it is highly likely to trigger the null filtering mechanism of the drone's array antenna, leading to decoy failure. However, the array antenna's recognition capability has physical limits, allowing for a certain angular deviation between the decoy signal and the real signal. Based on this, this embodiment employs a strategy of multiple decoy drones working collaboratively to cluster the visible satellites in the airspace. By having the decoy drones emit decoy signals at the coordinates corresponding to the center of the satellite cluster, the geometric configuration of the real visible satellites is simulated, thereby weakening the array antenna's recognition capability.

[0073] It should be noted that the selection of the initial center of the satellite cluster is crucial in the clustering process. Randomly selecting the initial center often leads to unstable clustering results, thus affecting the deception effect. An ideal satellite cluster should satisfy the requirement of high consistency in the direction of satellite signals within the cluster, that is, the angular deviation between the visible satellite corresponding to the cluster center and other visible satellites within the cluster should be small. Therefore, this embodiment first analyzes the angular distribution density among visible satellites in the signal direction coordinate system to preliminarily assess the possibility of each visible satellite serving as a cluster center.

[0074] Furthermore, considering the highly random distribution of visible satellites in the airspace, there may be outliers with significant deviations from other satellite signals. If these outliers are ignored, the unauthorized UAV can still receive undisturbed signals from the outlier direction, maintaining correct navigation calculations and causing deception failure. Conversely, forcibly including outliers in distant satellite clusters would increase the overall angular deviation of that cluster, reducing the deception accuracy of other satellites within the cluster. Therefore, this embodiment, based on the analysis of angular distribution density, introduces in-depth analysis of outlier visible satellites, prioritizing representative outliers in the initial center candidate list. This allows for a more accurate and comprehensive selection of the initial center satellite corresponding to the initial center of the satellite cluster, ensuring omnidirectional coverage of the deception signal and maximizing the deception system's resistance to array antennas.

[0075] Preferably, in one possible implementation of this embodiment, the method for acquiring the initial center satellite is described in [reference needed]. Figure 2 The diagram illustrates a method for acquiring an initial center satellite provided in this embodiment. The method includes the following steps:

[0076] Step S201: Obtain the degree of signal direction deviation.

[0077] For any visible satellite, the more different the elevation and azimuth angles of the visible satellite are from those of another visible satellite, the more different the signal direction of the visible satellite is from that of the other visible satellite. Therefore, this embodiment obtains the degree of signal direction deviation between the visible satellite and each other visible satellite based on the difference in elevation and azimuth angles between the visible satellite and each other in the signal direction coordinate system, accurately reflecting the angular distance between the visible satellite and each other in geometric space. The greater the degree of signal direction deviation, the more significant the difference in the signal incident directions of the corresponding two visible satellites.

[0078] The formula for calculating the degree of signal direction deviation is as follows: ： ; ; In the formula, The degree of signal direction deviation between the a-th visible satellite and the b-th visible satellite; The elevation angle between the a-th visible satellite and the b-th visible satellite. Difference; The azimuth angle between the a-th visible satellite and the b-th visible satellite. Difference; The elevation angle of the a-th visible satellite The normalized value; The elevation angle of the b-th visible satellite The normalized value; It is an absolute value function; The azimuth angle of the a-th visible satellite The normalized value; The azimuth angle of the b-th visible satellite The normalized value; min is the function that takes the minimum value.

[0079] It should be noted that, considering the periodicity of azimuth angles in physical space (i.e., the normalized 0s and 1s are physically connected), in order to accurately calculate the shortest angular distance between azimuth angles and avoid distance calculation errors caused by periodic boundaries, this embodiment adopts a ring distance formula: .

[0080] For better description in the following sections, we will use this visible satellite as an example for analysis.

[0081] Step S202: Obtain the neighborhood density and the degree of consistency of neighborhood direction.

[0082] To adaptively classify the density distribution of visible satellites, the minimum signal deviation is used as the reference signal deviation for that satellite. Then, based on the reference signal deviations of all visible satellites, the deviation segmentation threshold is adaptively obtained using the maximum inter-class variance (MOV) method. This facilitates the automatic determination of the optimal neighborhood radius in cluster analysis. The MOV method is well-known and will not be elaborated further. It should be noted that if the deviation segmentation threshold is 0, it is forcibly set to a first preset minimum value to avoid denominators of 0 in subsequent calculations, ensuring algorithm stability.

[0083] In this embodiment, the first preset minimum value is set to 0.01. The implementer can set the size of the first preset minimum value according to the actual situation. There is no limitation here, but the first preset minimum value is greater than 0 to ensure that the denominator is not 0 in the subsequent calculation process.

[0084] Considering that distance alone cannot fully assess the clustering of visible satellites, in order to comprehensively consider local features, the initial central satellite selected later can not only represent the satellite group in a high-density area, but also take into account the consistency of local signals. Then, based on the deviation segmentation threshold and the degree of signal arrival deviation, the neighborhood density and neighborhood arrival consistency of the visible satellite are obtained, which accurately reflects the density and directional consistency of the visible satellite distribution in the local area around the visible satellite.

[0085] The method for obtaining neighborhood density and neighborhood direction consistency is as follows: Using the visible satellite as the center and the deviation segmentation threshold as the radius, a deviation reference neighborhood for the visible satellite is determined; other visible satellites whose signal direction deviation is within the deviation reference neighborhood are all considered as neighboring satellites of the visible satellite; the number of neighboring satellites is used as the neighborhood density of the visible satellite; the higher the neighborhood density, the more visible satellites are distributed around the visible satellite, and the stronger its representativeness as a cluster center; to quantify the compactness of the visible satellite distribution within the deviation reference neighborhood, and thus, when the neighborhood density equals 0, indicating that the visible satellite is isolated in a local area, this implementation... For example, a first preset constant is used as the neighborhood arrival direction consistency of the visible satellite; when the neighborhood density is not equal to 0, the result of negatively correlating the mean of the signal arrival direction deviation between the visible satellite and all its neighboring satellites is used as the arrival direction consistency analysis value of the visible satellite; the larger the arrival direction consistency analysis value, the more compact the distribution of satellites in the neighborhood and the more unified the signal arrival direction; finally, the sum of the first preset constant and the arrival direction consistency analysis value is used as the neighborhood arrival direction consistency of the visible satellite; in this embodiment, the first preset constant is set to 1 as a benchmark value to avoid the neighborhood arrival direction consistency being too small when the arrival direction consistency analysis value is small, thus affecting subsequent calculations;

[0086] In this embodiment, the reciprocal of the sum of the mean of the signal direction deviation and the specified constant is used as the result of negative correlation of the mean of the signal direction deviation. The specified constant is not less than 1 to avoid the denominator being 0, and to ensure that the value fluctuates within a reasonable range. In this embodiment, the specified constant is set to 1. The implementer can set the size of the specified constant according to the actual situation, and it is not limited here.

[0087] Step S203: Obtain outlier weights.

[0088] Considering the possibility of sparsely distributed outlier visible satellites in the airspace, relying solely on density clustering could easily lead to these outliers being overlooked or misclassified, resulting in decoy signals failing to cover their true origin. Therefore, this embodiment obtains the outlier weight for each visible satellite based on its neighborhood density and deviation segmentation threshold. The larger the outlier weight, the more isolated the corresponding visible satellite, and the more it should be prioritized when selecting cluster centers to prevent decoy vulnerabilities.

[0089] The outlier weight is obtained as follows: the mean of the neighborhood densities of all visible satellites is rounded up, and the result is used as a reference neighborhood density boundary value to distinguish visible satellites in dense areas from those in sparse areas. For any visible satellite, if the neighborhood density of the visible satellite is greater than or equal to the reference neighborhood density boundary value, it indicates that the visible satellite is in a dense area with a low degree of outlier, and a second preset constant is used as the outlier weight of the visible satellite. If the neighborhood density of the visible satellite is less than the reference neighborhood density boundary value, it indicates that the visible satellite is in a sparse area. In order to accurately quantify its outlier degree, it is necessary to determine whether the neighborhood density of the visible satellite is 0.

[0090] If the neighborhood density of a visible satellite is 0, it means that the visible satellite has no neighbors within the deviation segmentation threshold range and is extremely isolated. In this case, the degree of deviation of the reference signal of the visible satellite is used as the deviation analysis value corresponding to the visible satellite (the deviation analysis value is greater than the deviation segmentation threshold). If the neighborhood density of the visible satellite is not 0, it means that although the visible satellite is in a sparse region, it still has a few neighbors. In this case, the average value of the signal deviation between the visible satellite and each of its neighboring satellites is used as the deviation analysis value corresponding to the visible satellite (the deviation analysis value is less than or equal to the deviation segmentation threshold). The larger the deviation analysis value, the greater the spatial interval between the visible satellite and its surrounding satellites, and the sparser the distribution. In order to uniformly quantify the outlier significance under different sparsity levels, the ratio of the deviation analysis value to the deviation segmentation threshold is used as the outlier analysis value corresponding to the visible satellite. The larger the outlier analysis value, the farther the visible satellite is from other satellites, and the more significant the outlier feature. The deviation segmentation threshold is greater than 0. In order to ensure that satellites with more severe outliers receive greater weight compensation, the sum of the second preset constant and the outlier analysis value is used as the outlier weight of the visible satellite. In this embodiment, the second preset constant is set to 1 as the basic weight value to ensure that the basic weight of all satellites is not 0.

[0091] Step S204: Obtain the initial center satellite.

[0092] A higher neighborhood density indicates stronger representativeness of the corresponding visible satellite; a higher degree of neighborhood arrival consistency indicates higher local compactness of the corresponding visible satellite; a higher outlier weight indicates a more isolated visible satellite, requiring separate coverage. To comprehensively balance local representativeness and global coverage, this embodiment first obtains a comprehensive score for each visible satellite using the Topsis algorithm based on its neighborhood density and neighborhood arrival consistency. A higher comprehensive score indicates a smaller signal arrival deviation for the corresponding visible satellite and its deviance reference neighboring satellites. When used as decoy launch coordinates, this allows the corresponding visible satellite to meet the anti-array antenna requirements of more visible satellites, making it more suitable as the initial center of a satellite cluster. The Topsis algorithm is a well-known technique and will not be described in detail here.

[0093] Considering that relying solely on the comprehensive score might miss outlier visible satellites, this embodiment further uses the product of the outlier weight of each visible satellite and the comprehensive score as the center selection degree for each visible satellite. A higher center selection degree indicates better local representativeness and stronger outlier characteristics (requiring forced coverage). To accurately select initial centers that are both representative and cover outliers, the center selection degrees are arranged in descending order to obtain a selection degree sequence. Then, the visible satellites corresponding to the first preset number of center selection degrees in the selection degree sequence are used as initial center satellites. The preset number essentially represents the number of decoy drones, ensuring that each decoy drone is assigned an optimal initial guidance direction. This embodiment sets the preset number to 4, because common tactical decoy missions typically require 4 cooperating drones to cover the main GPS constellation quadrants. Implementers can set the preset number according to actual conditions; this is not limited here.

[0094] Thus, the initial center satellite corresponding to the initial center of the satellite cluster has been accurately selected.

[0095] Step S3: Based on the preset deception trajectory of the black-flying drone, predict the future expected position. According to the angle change of each visible satellite relative to each initial center satellite in the future expected position and the angular distance in the signal direction coordinate system, obtain the comprehensive membership degree of each visible satellite to each initial center satellite. Based on the initial center satellite and the comprehensive membership degree, obtain the final center satellite corresponding to the final center of the satellite cluster through the weighted fuzzy C-means clustering algorithm.

[0096] Specifically, known continuous guidance technology for UAVs establishes and maintains a false navigation field that is synchronized with and logically consistent with the real signal. This makes the navigation information received by the UAV change smoothly and continuously, resulting in strong concealment, a high success rate of deception, and the ability to achieve precise guidance over complex and long distances. This embodiment first uses the center position of the UAV capture area closest to the black-flying UAV as the target deception position. Continuous guidance technology is used to lure the black-flying UAV to the target deception position for subsequent landing or capture. Based on the current position of the black-flying UAV, the target deception position, and the estimated target position, this embodiment adopts the UAV continuous guidance technology based on circular trajectory disclosed in "Yi Mingjiang. Research on Deception Guidance Technology of Small UAVs Based on GNSS [D]. Academy of Military Sciences, 2024" to calculate and obtain the preset deception trajectory of the black-flying UAV during the desired navigation deception process. The circular trajectory UAV continuous guidance technology is a well-known technology and will not be described further.

[0097] To observe in real-time the dynamic changes in the direction of real satellite signals relative to the decoy source (i.e., the initial center satellite) as the unauthorized drone moves along a preset decoy trajectory, this embodiment predicts several expected future positions based on the preset decoy trajectory. Specifically, by using the unauthorized drone's current flight speed and a preset duration, the position of the unauthorized drone at the end of each preset duration on the preset decoy trajectory is obtained, and this position is taken as the expected future position of the unauthorized drone. This embodiment sets multiple different preset durations within the preset decoy time range, for example, preset durations of 1 second, 5 seconds, and 30 seconds. The implementer can set the size of the preset duration according to the actual situation, and it is not limited here.

[0098] Then, based on the angular changes of each visible satellite relative to each initial center satellite at these future expected positions (dynamic indicators), combined with the current angular distance in the signal direction coordinate system (static indicators), the comprehensive membership degree of each visible satellite to each initial center satellite is obtained. This accurately reflects the spatiotemporal adaptation stability of each visible satellite belonging to a specific satellite cluster (decoy drone) at the current moment and in the future decoy process, thereby assessing whether the corresponding decoy station can maintain consistency with the real satellite signal direction in the long term.

[0099] To enhance coverage of sparsely distributed visible satellites during clustering and prevent outliers from being ignored due to iterative convergence, this embodiment uses a weighted fuzzy C-means clustering algorithm based on the initial center satellite and comprehensive membership degree, and incorporates the outlier weight calculated in step S2, to obtain the final center satellite corresponding to the final center of the satellite cluster. This ensures that the final generated decoy launch coordinates simulate the distribution characteristics of a real satellite constellation across the entire airspace, minimizing the null-trapping anti-interference capability of the unmanned aerial vehicle (UAV) array antenna and achieving continuous and stable navigation decoy. The weighted fuzzy C-means clustering algorithm is a well-known technique and will not be described further.

[0100] Preferably, in one feasible embodiment of this invention, the method for obtaining the comprehensive membership degree is as follows: For any future expected position, taking the future expected position as the origin, a signal direction coordinate system corresponding to the future expected position is constructed using the method of obtaining the signal direction coordinate system in step S1, which serves as the reference coordinate system for the future expected position; for any visible satellite and any initial center satellite, the degree of signal direction deviation between the visible satellite and the initial center satellite in the reference coordinate system of each future expected position is obtained, and these are all used as the reference deviation analysis value between the visible satellite and the initial center satellite; the smaller the reference deviation analysis value, the more it indicates that when the black-flying drone moves to the corresponding expected position during the future deception process, the relative angle between the initial center satellite (deception source) and the visible satellite remains close, and the deception source does not need to move significantly to maintain a good signal simulation effect. It should be noted that, because the degree of signal direction deviation is essentially a generalized distance based on spherical angle projection and corrected for periodic boundaries, this embodiment directly uses the degree of signal direction deviation as a quantitative indicator to measure the spatial similarity or proximity between the visible satellite and the initial center satellite;

[0101] To accurately characterize the dynamic consistency of the incoming signal along the entire preset decoy trajectory, the average of all reference deviation analysis values ​​is used as the first decoy offset degree of the visible satellite relative to the initial center satellite. The larger the first decoy offset degree, the greater the accumulated error of the initial center satellite simulating the real signal direction of the visible satellite as the unauthorized UAV moves along the preset decoy trajectory (i.e., poor dynamic spatiotemporal adaptability). To more accurately analyze the initial static spatial proximity at the current moment, the degree of signal direction deviation between the visible satellite and the initial center satellite in the signal direction coordinate system at the current moment is further used as the second decoy offset degree of the visible satellite relative to the initial center satellite. The larger the second decoy offset degree, the greater the difference in signal direction between the visible satellite and the initial center satellite at the current moment, and the lower the initial decoy accuracy.

[0102] To balance the dual requirements of current deception accuracy and future deception stability, this embodiment sets a first preset weight (e.g., 0.4) and a second preset weight (e.g., 0.6), and the sum of the first preset weight and the second preset weight is 1. Implementers can set the magnitude of the first preset weight and the second preset weight according to actual circumstances (such as prioritizing instantaneous accuracy or long-term stability), which is not limited here. By using weighted calculations, the first and second deception offset degrees accurately reflect the comprehensive deception cost that balances static realism and dynamic stability. Specifically, the product of the first preset weight and the first deception offset degree is used as the first analysis value; the product of the second preset weight and the second deception offset degree is used as the second analysis value; the sum of the first and second analysis values ​​is used as the overall offset degree of the visible satellite relative to the initial center satellite, which initially reflects the generalized spatiotemporal cost (the smaller the cost, the better) required to classify the visible satellite into the initial center satellite. The smaller the overall offset degree, the higher the spatiotemporal matching degree between the visible satellite and the initial center satellite, and the more suitable it is to be classified into the satellite cluster. It should be noted that if the overall offset degree is 0, the overall offset degree is forced to be the second preset minimum value, wherein the second preset minimum value is greater than 0 to avoid the overall offset degree being 0. In this embodiment, the second preset minimum value is set to 0.01. Implementers can set the size of the second preset minimum value according to the actual situation, which is not limited here.

[0103] To transform the aforementioned generalized spatiotemporal cost into a normalized probability distribution that meets the requirements of clustering algorithms, the overall offset of the visible satellite relative to any initial center satellite is used as the comparison offset. Then, the reciprocal of the sum of powers of the ratios of the overall offset to each comparison offset is used as the comprehensive membership degree of the visible satellite to that initial center satellite. This accurately meets the mathematical constraint requirement of the fuzzy C-means clustering algorithm on the membership matrix (i.e., for any sample, the sum of its membership degrees to all cluster centers is 1), which is beneficial for subsequent iterative algorithms to accurately converge to the globally optimal satellite cluster center, thereby determining the best decoy station position. The formula for calculating the comprehensive membership degree is: In the formula, Let K be the overall membership degree of the a-th visible satellite to the j-th initial center satellite; K is the number of initial center satellites. The overall offset of the a-th visible satellite relative to the j-th initial center satellite; denoted as the overall offset of the a-th visible satellite relative to the k-th initial center satellite; m is the ambiguity index, which is set to 2 in this embodiment.

[0104] At this point, obtaining the comprehensive membership degree of each visible satellite to each initial central satellite is beneficial for finding the optimal decoy position that balances the current signal fidelity and future trajectory adaptability through iterative algorithms, thereby achieving continuous and stable decoy of unauthorized drones.

[0105] Based on the obtained comprehensive membership degree, iterative updates are performed using a weighted fuzzy C-means clustering algorithm until convergence conditions are met (e.g., the maximum difference between the membership degree matrices of two consecutive iterations is less than 1e-5, or the maximum number of iterations of 100 is reached). The specific steps are as follows: First, update the cluster centers. Based on the current comprehensive membership degree and the outlier weight of each visible satellite, calculate the new satellite cluster centers. The calculation formula is: In the formula, The coordinates of the updated cluster center of the j-th satellite cluster (including normalized elevation and azimuth angles); N is the total number of visible satellites; is the comprehensive membership degree of the nth visible satellite to the jth initial center satellite; m is the fuzzy index, which is set to 2 in this embodiment (the most commonly used default setting for the weighted fuzzy C-means clustering algorithm); Let be the outlier weight of the nth visible satellite; Let be the coordinates of the nth visible satellite. By explicitly introducing outlier weights into the cluster center update formula, sparsely distributed outliers with large signal direction deviations and easily ignored outliers have greater say in calculating new cluster centers. This allows for the forced shift of decoy centers towards these outliers, ensuring that the generated decoy signals can cover these special angles and effectively counteracting the null filtering mechanism of unauthorized drone array antennas.

[0106] Secondly, update the membership degree and determine convergence: replace the initial center satellite with the calculated updated cluster center, repeat the above-mentioned calculation steps of comprehensive membership degree (including calculating the dynamic / static offset degree and weighting), and obtain a new membership degree matrix; if the convergence condition is met, stop the iteration, and determine the virtual satellite position corresponding to the cluster center obtained in the final iteration as the final center satellite.

[0107] Step S4: Based on the final position of the central satellite, obtain the decoy launch coordinates to deceive the navigation of the unauthorized drone.

[0108] Specifically, the position of each final central satellite essentially represents the optimal signal direction angle for deceiving and interfering with the navigation system of unauthorized drones during the deception period. To implement physical deception, these virtual angular coordinates need to be converted into the hovering positions of the deceitful drones in physical space, i.e., the deceitful launch coordinates. This drives multiple deceitful drones to fly to their respective deceitful launch coordinates and hover, collaboratively emitting false navigation signals consistent with the ephemeris of the real satellites. This deceives the navigation of unauthorized drones, ensuring that the spatial direction distribution of the deceitful signals closely matches that of the real satellites, circumventing the directional filtering of the array antennas, and effectively improving the stealth and success rate of navigation deception.

[0109] Preferably, in one feasible manner of this embodiment, the method for obtaining the decoy launch coordinates is as follows: obtain the normalized elevation angle and normalized azimuth angle of each final center satellite at the current moment in the coordinate system; perform inverse normalization (inverse operation of the normalization rule in step S1) on the normalized elevation angle and normalized azimuth angle corresponding to each final center satellite to restore the actual elevation angle and actual azimuth angle of each final center satellite;

[0110] To ensure safe physical isolation between the decoy drone and the unauthorized drone while meeting signal coverage requirements, this embodiment introduces a preset decoy safety distance (e.g., 500 meters). Then, by combining the actual pitch and azimuth angles of each final central satellite with the preset decoy safety distance, the actual pitch and azimuth angles of each final central satellite are converted into coordinates in three-dimensional space, which are then used as the decoy drone's launch coordinates. For example, if the decoy drone's launch coordinates are... ,but ; ; Where R is the preset deception safety distance; The j-th decoy drone corresponds to the actual pitch angle of the final central satellite; Let be the actual azimuth angle of the final central satellite corresponding to the j-th decoy drone; sin is the sine function; cos is the cosine function. It should be noted that the decoy launch coordinates are obtained based on the definition of the Northeast-Heaven coordinate system with the black drone as the origin, due north as the Y-axis, due east as the X-axis, and the sky direction as the Z-axis. Implementers can adjust the trigonometric function relationships according to the actual coordinate system definition selected.

[0111] The calculated decoy launch coordinates are converted into absolute geographic coordinates (such as latitude, longitude, and altitude), driving the decoy drones on the base station platform to move to their corresponding launch coordinates and hover. All decoy drones carry decoy payloads and collaboratively launch mutually consistent (time and phase synchronized) decoy signals from the calculated optimal angle of arrival. This achieves a high-fidelity simulation of the spatial distribution of the decoy signals to a real satellite constellation, effectively weakening the null filtering capability and signal arrival direction identification capability of the unauthorized drone array antenna, successfully completing the covert intrusion into the unauthorized drone's navigation link. Furthermore, since the decoy launch coordinates are a comprehensive optimized solution for a preset decoy time period (including future trajectories), the decoy drones can remain hovered or require only minor adjustments during this period, greatly simplifying the complexity of multi-drone collaborative control.

[0112] In summary, this embodiment obtains the positions of the unauthorized UAV and visible satellites, constructing a signal-oriented coordinate system with the unauthorized UAV as the origin. Based on the angular distribution density and outlier occurrences of visible satellites in the signal-oriented coordinate system, an initial center satellite is selected. The expected future position is predicted based on the UAV's preset decoy trajectory. A comprehensive membership degree is obtained based on the angular changes of visible satellites relative to the initial center satellite at the expected future position and the angular distance in the signal-oriented coordinate system. Based on the initial center satellite and the comprehensive membership degree, a weighted fuzzy C-means clustering algorithm is used to obtain the final center satellite of the satellite cluster, thereby obtaining the decoy launch coordinates and deceiving the unauthorized UAV's navigation. This invention effectively improves the accuracy of navigation decoys for unauthorized UAVs by accurately obtaining the decoy launch coordinates.

[0113] Example 2:

[0114] This invention also proposes a navigation deception system for countering unmanned aerial vehicles (UAVs) flying illegally; please refer to [link / reference]. Figure 3 The diagram illustrates a structural diagram of a navigation deception system for countering unmanned aerial vehicles (UAVs) provided by an embodiment of the present invention. The system includes: a signal direction coordinate system acquisition module 10, an initial center satellite acquisition module 20, a final center satellite acquisition module 30, and a navigation deception module 40.

[0115] The signal direction coordinate system acquisition module 10 is used to acquire the position of the unmanned aerial vehicle (UAV) and the position of each visible satellite in the airspace, and to construct a signal direction coordinate system with the UAV as the origin; the signal direction coordinate system is composed of the pitch angle and azimuth angle of the visible satellite relative to the UAV.

[0116] The initial center satellite acquisition module 20 is used to select the initial center satellite corresponding to the initial center of the satellite cluster in the signal direction coordinate system based on the angular distribution density between visible satellites and the outlier status of visible satellites.

[0117] The final center satellite acquisition module 30 is used to predict the future expected position based on the preset deception trajectory of the black-flying drone, and to obtain the comprehensive membership degree of each visible satellite to each initial center satellite according to the angle change of each visible satellite relative to each initial center satellite in the future expected position and the angular distance in the signal-oriented coordinate system; and to obtain the final center satellite corresponding to the final center of the satellite cluster through a weighted fuzzy C-means clustering algorithm based on the initial center satellite and the comprehensive membership degree.

[0118] The navigation deception module 40 is used to obtain the deception launch coordinates based on the position of the final central satellite and to deceive the navigation of the unauthorized UAV.

[0119] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the navigation and deception system for countering unmanned aerial vehicles (UAVs) provided in the above embodiments and the navigation and deception method for countering unmanned aerial vehicles (UAVs) belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0120] Example 3:

[0121] This invention also proposes a navigation deception device for countering unmanned aerial vehicles (UAVs) flying illegally. The device includes a memory and a processor. The memory stores executable program code, and the processor calls and executes the executable program code to perform the navigation deception method for countering unmanned aerial vehicles (UAVs) flying illegally provided in the embodiments of this application. Specifically, the device may be a chip, component, or module. The chip may include a connected processor and memory; the memory stores instructions, and when the processor calls and executes the instructions, the chip can perform the navigation deception method for countering unmanned aerial vehicles (UAVs) flying illegally provided in the above embodiments.

[0122] In addition, this embodiment also protects a computer device; please refer to [link to relevant documentation]. Figure 4 The computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402. When the processor 402 executes the computer program 403, the computer device can execute any of the aforementioned methods for countering unmanned aerial vehicles (UAVs) using navigation deception.

[0123] Example 4:

[0124] The present invention also provides a computer-readable storage medium storing computer program code, which, when executed on a computer, causes the computer to perform the aforementioned method steps to implement the navigation deception method for countering unmanned aerial vehicles (UAVs) provided in the above embodiments.

[0125] Example 5:

[0126] The present invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement the navigation and deception method for countering unmanned aerial vehicles (UAVs) provided in the above embodiments.

[0127] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0128] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for deceiving and spoofing unmanned aerial vehicles (UAVs) used in counter-drone operations, characterized in that: The method includes the following steps: The location of the unauthorized UAV and the location of each visible satellite in the airspace are obtained, and a signal orientation coordinate system with the unauthorized UAV as the origin is constructed. The signal orientation coordinate system is composed of the pitch angle and azimuth angle of the visible satellite relative to the unauthorized UAV. Based on the angular distribution density among visible satellites and the outlier status of visible satellites in the signal direction coordinate system, the initial center satellite corresponding to the initial center of the satellite cluster is selected. Based on the preset deception trajectory of the black-flying drone, the expected future position is predicted. According to the angle change of each visible satellite relative to each initial center satellite in the expected future position and the angular distance of the signal in the coordinate system, the comprehensive membership degree of each visible satellite to each initial center satellite is obtained. Based on the initial center satellite and the comprehensive membership degree, the final center satellite corresponding to the final center of the satellite cluster is obtained by weighted fuzzy C-means clustering algorithm. Based on the final location of the central satellite, obtain the coordinates of the decoy launch and use them to spoof the navigation of the unauthorized drones. The method for obtaining the initial center satellite is as follows: For any visible satellite, based on the difference in elevation and azimuth angles between the visible satellite and each other in the coordinate system according to the signal, the degree of signal deviation between the visible satellite and each other is obtained. The minimum deviation of the signal is used as the reference signal deviation of the visible satellite; Based on the reference signals of all visible satellites, the degree of deviation is determined, and the deviation segmentation threshold is obtained by using the maximum inter-class variance method. Based on the deviation segmentation threshold and the degree of signal orientation deviation, the neighborhood density and neighborhood orientation consistency of the visible satellite are obtained; The outlier weight of each visible satellite is obtained based on the neighborhood density and bias segmentation threshold of each visible satellite. Based on the neighborhood density and neighborhood orientation consistency of each visible satellite, the Topsis algorithm is used to obtain a comprehensive score for each visible satellite. The product of the outlier weight of each visible satellite and the overall score is used as the center selection degree of each visible satellite; Arrange the center selection degree in descending order to obtain a selection degree sequence; The visible satellites corresponding to the first preset number of center selection degrees in the selection degree sequence are used as the initial center satellites; where the preset number is the number of decoy drones. The formula for calculating the degree of signal direction deviation is as follows: ; ; In the formula, The degree of signal direction deviation between the a-th visible satellite and the b-th visible satellite; The elevation angle between the a-th visible satellite and the b-th visible satellite. Difference; The azimuth angle between the a-th visible satellite and the b-th visible satellite. Difference; The elevation angle of the a-th visible satellite The normalized value; The elevation angle of the b-th visible satellite The normalized value; It is an absolute value function; The azimuth angle of the a-th visible satellite The normalized value; The azimuth angle of the b-th visible satellite The normalized value; min is the function that takes the minimum value.

2. The method for navigation deception of unauthorized drones for countering unmanned aerial vehicles as described in claim 1, characterized in that, The method for obtaining the signal from the coordinate system is as follows: For any given moment, take the position of the unauthorized drone at that moment as the origin and establish a northeast celestial coordinate system with due east, due north, and the celestial direction as axes; Calculate the elevation and azimuth angles of each visible satellite in the northeast celestial coordinate system at that moment; A two-dimensional coordinate system is constructed with elevation angle as the horizontal axis and azimuth angle as the vertical axis. The normalized values ​​of elevation angle and azimuth angle of each visible satellite are mapped onto the two-dimensional coordinate system to obtain the signal direction coordinate system.

3. The method for navigation deception of unauthorized drones for countering unmanned aerial vehicles as described in claim 1, characterized in that, The method for obtaining the neighborhood density and the degree of consistency of neighborhood direction is as follows: Centered on the visible satellite and with the deviation segmentation threshold as the radius, the deviation reference neighborhood of the visible satellite is determined; other visible satellites whose signal direction deviation is within the deviation reference neighborhood are all regarded as neighboring satellites of the visible satellite. The number of neighboring satellites is used as the neighborhood density of the visible satellite; When the neighborhood density is equal to 0, the first preset constant is used as the neighborhood consistency of the visible satellite; When the neighborhood density is not equal to 0, the result of negatively correlating the mean of the signal direction deviation between the visible satellite and the neighboring satellites is used as the direction consistency analysis value of the visible satellite. The sum of the first preset constant and the direction-of-arrival consistency analysis value is taken as the direction-of-arrival consistency of the neighborhood of the visible satellite.

4. The method for navigation deception of unauthorized drones for countering unmanned aerial vehicles as described in claim 3, characterized in that, The method for obtaining the outlier weights is as follows: The mean of the neighborhood density of all visible satellites is rounded up and used as the reference neighborhood density boundary value. For any visible satellite, when the neighborhood density of the visible satellite is greater than or equal to the reference neighborhood density boundary value, the second preset constant is used as the outlier weight of the visible satellite. When the neighborhood density of a visible satellite is less than the reference neighborhood density threshold, determine whether the neighborhood density of the visible satellite is 0. If the neighborhood density of the visible satellite is 0, then the degree of deviation of the reference signal of the visible satellite is used as the deviation analysis value corresponding to the visible satellite. If the neighborhood density of the visible satellite is not 0, then the average value of the signal direction deviation between the visible satellite and each of its neighboring satellites is used as the deviation analysis value corresponding to the visible satellite. The ratio of the deviation analysis value to the deviation segmentation threshold is used as the outlier analysis value corresponding to the visible satellite; wherein, the deviation segmentation threshold is greater than 0. The sum of the second preset constant and the outlier analysis value is used as the outlier weight of the visible satellite.

5. The method for navigation deception of unauthorized drones for countering unmanned aerial vehicles as described in claim 1, characterized in that, The method for obtaining the expected future location is as follows: Using the center of the pre-set drone capture area as the target deception position, and based on the current position of the black-flying drone, the target deception position, and the estimated target position, the pre-set deception trajectory of the black-flying drone is obtained using continuous drone guidance technology based on circular trajectory. By using the current flight speed and preset duration of the unauthorized drone, the position of the unauthorized drone at the end of each preset duration is obtained on the preset deception trajectory, which is used as the expected future position of the unauthorized drone.

6. The method for navigation deception of unauthorized drones for countering unmanned aerial vehicles as described in claim 1, characterized in that, The method for obtaining the comprehensive membership degree is as follows: For any expected future location, a signal-oriented coordinate system is constructed with the expected future location as the origin, which serves as the reference coordinate system for the expected future location. For any visible satellite and any initial center satellite, the degree of signal direction deviation between the visible satellite and the initial center satellite in the reference coordinate system of each future expected position is obtained and used as the reference deviation analysis value between the visible satellite and the initial center satellite. The average of all the aforementioned reference deviation analysis values ​​is taken as the first decoy offset degree of the visible satellite relative to the initial center satellite; The degree of signal deviation between the visible satellite and the initial center satellite in the current signal-oriented coordinate system is used as the second deception offset degree of the visible satellite relative to the initial center satellite. The product of the first preset weight and the first degree of deception offset is used as the first analysis value; The product of the second preset weight and the second degree of deception offset is used as the second analysis value; The sum of the first and second analysis values ​​is taken as the overall offset of the visible satellite relative to the initial center satellite. The overall offset of the visible satellite relative to any initial center satellite is used as the comparison offset. The reciprocal of the sum of powers of the ratio of the overall offset to the offset of each comparison is taken as the comprehensive membership degree of the visible satellite to the initial center satellite.

7. The method for navigation deception of unauthorized drones for countering unmanned aerial vehicles as described in claim 1, characterized in that, The method for obtaining the decoy launch coordinates is as follows: The signal of each final central satellite at the current moment is obtained to generate the normalized elevation angle and normalized azimuth angle in the coordinate system; The normalized elevation angle and normalized azimuth angle corresponding to each final center satellite are denormalized to obtain the actual elevation angle and actual azimuth angle of each final center satellite. Combined with the preset decoy safety distance, the actual elevation angle and actual azimuth angle of each final center satellite are converted into coordinate positions in three-dimensional space, which are used as the decoy launch coordinates of the decoy drone.

8. A navigation and deception system for countering unmanned aerial vehicles (UAVs), comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the navigation and deception method for countering unmanned aerial vehicles (UAVs) as described in any one of claims 1-7.

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