A satellite interference source positioning method

Abstract

The present invention discloses a method for locating a satellite interference source, which relates to the field of satellite navigation and wireless communication technology. First, a satellite constellation simulator generates a satellite interference signal, and a satellite receiver records the carrier-to-noise ratio and automatic gain control value under different interference powers; an output characteristic model of the receiver under different interference intensities is established; the target area is divided into a uniform grid, and ray tracing and a three-dimensional city model are used within the grid to simulate the interference signal power received by the receiver at any interference source position; a data set of carrier-to-noise ratio and automatic gain control value is obtained in combination with the output characteristic model; an interference source position predictor is trained using the data set; the position predictor runs in the cloud and performs position prediction based on the measured information received; and a signal matching or joint positioning strategy is used to ultimately determine the precise location of the interference source. The present invention has strong feasibility and can improve the accuracy of satellite interference source positioning in dense urban environments.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite navigation and wireless communication, and in particular to a method for locating a satellite interference source. Background Art

[0002] The BeiDou Navigation Satellite System, my country's independently developed global satellite navigation system, has become a vital infrastructure crucial to the nation's economy and people's livelihood. It provides round-the-clock positioning and timing services for critical infrastructure such as communications, electricity, finance, and transportation, making it a vital force in promoting economic and social development and safeguarding national security. However, due to its fragility, BeiDou satellite signals are highly susceptible to interference from natural or man-made signals. Illegal interference, in particular, poses a serious threat to the accuracy and reliability of BeiDou services. This is particularly true in core urban areas with dense populations and critical infrastructure, where it threatens information security and communication stability, posing a security risk to key areas such as urban management and emergency response. Therefore, it is particularly important to locate and locate sources of satellite interference in dense urban environments and provide law enforcement with the necessary tools.

[0003] However, the complexity of dense urban environments poses a significant challenge to satellite interference source location. The dense density of high-rise buildings and the maze of streets not only block the line-of-sight path for signals but also cause them to reflect and scatter multiple times along the way. These complex paths transform signals into highly convoluted signals by the time they reach the receiver, making them difficult to accurately interpret. Signal strength, phase, time of arrival, and angle of arrival are all severely affected, making traditional positioning methods ineffective and significantly reducing accuracy.

[0004] Therefore, how to achieve efficient and high-precision satellite interference source positioning in dense urban environments has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a satellite interference source positioning method, which can achieve efficient and high-precision positioning of the satellite interference source in a complex urban environment.

[0006] The present invention adopts the following technical solutions to solve the above technical problems:

[0007] A satellite interference source positioning method proposed in the present invention includes:

[0008] Combining the geospatial information of the target urban area with a 3D city model, a mixed signal of a satellite interference signal and a satellite signal with multipath error is generated. This mixed signal is used as the input of a satellite receiver, and the carrier-to-noise ratio and automatic gain control value of the satellite receiver under different interference intensities are recorded.

[0009] The obtained carrier-to-noise ratio and automatic gain control value are fitted to establish the output characteristic model of the satellite receiver under different interference intensities.

[0010] Multiple interference signal monitoring stations are evenly arranged in the target area, dividing the target area into uniform grids; within each grid, arbitrary interference source positions are randomly simulated using ray tracing and a three-dimensional city model; based on the interference signal power received by each interference signal monitoring station and in combination with the output characteristic model, a data set is obtained at the arbitrary interference source position within each grid, the data set including carrier-to-noise ratio and automatic gain control amount;

[0011] The obtained dataset is used as input and the location grid of the data is used as label to train the interference source location predictor;

[0012] Based on the carrier-to-noise ratio and automatic gain control value actually received by each interference signal monitoring station, the interference source location predictor obtained after training is used to make a preliminary prediction of the location grid where the interference source is located, and a predicted location grid is obtained;

[0013] Within the predicted location grid, a signal matching strategy based on a virtual interference source is used to search to ultimately determine the location of the interference source; or,

[0014] Within the predicted location grid, a search is performed based on a joint search strategy of aerial monitoring equipment and ground monitoring equipment to ultimately determine the location of the interference source.

[0015] As a further optimization scheme of the satellite interference source positioning method described in the present invention, a satellite constellation simulator is used to generate a mixed signal of a satellite interference signal and a satellite signal with multipath error; a plurality of interference signal monitoring stations are evenly arranged in an open area of ​​the target area, and an open area refers to an area where there are no obstacles that block and reflect the signal.

[0016] As a further optimization scheme of the satellite interference source positioning method described in the present invention, the interference signal monitoring station is a static satellite receiver; the interference source location predictor obtained after training is used to make a preliminary prediction of the location grid where the interference source is located, which is implemented by the central processing control unit in the cloud; specifically, as follows:

[0017] When the static satellite receiver at each interference signal monitoring station detects an interference signal, it uploads its carrier-to-noise ratio and automatic gain control value in real time to the central processing and control unit located in the cloud. Based on this carrier-to-noise ratio and automatic gain control value, the central processing and control unit uses the trained interference source location predictor to make a preliminary prediction of the location grid of the interference source.

[0018] As a further optimization scheme of the satellite interference source positioning method described in the present invention, ray tracing and a three-dimensional city model are used to randomly simulate the location of any interference source; the details are as follows:

[0019] Step 1-1, determine the signal emission position of the interference source, that is, the starting point of the ray tracing process;

[0020] Step 1-2: simulate the propagation paths of the interference signal from the starting point in the environment;

[0021] Steps 1-3: Calculate the interaction between the interference signal and the terrain and environmental objects, including the reflection angle, scattering angle, absorption coefficient, and reflection coefficient, to determine the propagation direction, path, and intensity of the interference signal;

[0022] Steps 1-4: Record all possible propagation paths of the interference signal to the receiving location, as well as the strength, arrival time, arrival angle, and arrival frequency information of the arriving signal.

[0023] As a further optimization scheme of the satellite interference source positioning method described in the present invention, the interference source position predictor obtained after training is used to make a preliminary prediction of the location grid where the interference source is located to obtain the predicted location grid; the details are as follows:

[0024] Each observation vector is composed of the carrier-to-noise ratio and automatic gain control value of the static satellite receiver of each interference signal monitoring station. The observation vectors of all interference signal monitoring stations constitute a full state space dataset. Several sub-state spaces are randomly selected from the full state space dataset, and each sub-state space is used as a dataset to train each sub-learner in the interference source position predictor. The prediction results of each sub-learner are combined to obtain the final position grid prediction result.

[0025] As a further optimization scheme of the satellite interference source positioning method described in the present invention,

[0026] Within the predicted location grid, a signal matching strategy based on a virtual interference source is used to search to ultimately determine the location of the interference source; or,

[0027] Within the predicted location grid, a search is conducted based on a joint search strategy using both aerial and ground monitoring equipment to ultimately determine the location of the interference source. The details are as follows:

[0028] When adopting the signal matching strategy of virtual interferers, multiple virtual interferers are initialized within the predicted position grid as the intelligent agents of the search method;

[0029] When using a joint search strategy based on aerial and ground-based monitoring equipment, multiple dynamic monitoring satellite receivers are initialized within the predicted location grid as intelligent agents of the search method; wherein the dynamic monitoring satellite receivers are equipped on the aerial and ground-based monitoring equipment;

[0030] Step 2-1: Check whether the position of each agent exceeds the boundary of the search space;

[0031] Step 2-2, calculate the fitness function value of each agent;

[0032] Step 2-3, determine whether the fitness function value is minimum:

[0033] If the fitness function value is not the minimum, continue to steps 2-4;

[0034] If the fitness function value is the minimum, steps 2-4 are not executed, and the positioning solution is the final position of the agent with the minimum fitness function value in the last iteration;

[0035] Steps 2-4: Calculate the quality of each agent;

[0036] Calculate the forces acting on each agent;

[0037] Each agent moves under the driving force and then goes to step 2-1 to loop.

[0038] As a further optimization scheme of the satellite interference source positioning method described in the present invention, the intelligent agent is a virtual interference source in the signal matching strategy, and the aerial monitoring equipment and the ground monitoring equipment in the joint positioning strategy are drones, monitoring vehicles, and handheld monitoring devices equipped with dynamic monitoring satellite receivers;

[0039] In step 2-2, when the signal matching strategy is adopted, the fitness function value of each agent is

[0040]

[0041] Where Q is the total number of interference signal monitoring stations, and the parameters α1 and α2 are used to normalize the values ​​of the carrier-to-noise ratio and the automatic gain control amount in two different units. and are the carrier-to-noise ratio and automatic gain control value of the qth interference signal monitoring station under real interference, and are the carrier-to-noise ratio and automatic gain control value of the qth interference signal monitoring station under the kth virtual interference source respectively.

[0042] As a further optimization scheme of the satellite interference source positioning method of the present invention, in step 2-2, when the joint positioning strategy is adopted, the fitness function value of each agent is

[0043]

[0044] Where L is the total number of dynamic monitoring receivers, parameters α1 and α2 are used to normalize the values ​​of carrier-to-noise ratio and automatic gain control amount in two different units, C / N0| l and AGC l is the carrier-to-noise ratio and automatic gain control value of the lth dynamic monitoring receiver.

[0045] As a further optimization scheme of the satellite interference source positioning method described in the present invention, in steps 2-4, the normalized formula for calculating the quality of the intelligent agent is:

[0046]

[0047] Where N is the total number of agents, M i (t) is the normalized mass of the i-th agent at cycle time t, m j (t) is the mass of the jth agent at cycle time t, m i (t) is the mass of the i-th agent at cycle time t, specifically

[0048]

[0049] Among them, fit i (t) is the fitness function value of the ith agent at cycle time t, best(t) and worst(t) are fit i (t) minimum and maximum values;

[0050] In steps 2-4, the formula for calculating the force of each agent is

[0051]

[0052] Among them, F i x (t) is the force in the x direction acting on the i-th agent at time t, F i y (t) is the force in the y direction acting on the i-th agent at time t, is the component of the force in the x direction exerted by the j-th agent on the ith agent at time t, is the component of the force in the y direction exerted by the jth agent on the ith agent at time t. The x and y directions are two mutually perpendicular directions in the two-dimensional plane coordinate system. θ is the vector R ij(t) and The angle between and are the positions of the i-th agent and the j-th agent in direction d, respectively. Direction d is the two mutually perpendicular x-directions or y-directions;

[0053] The component of the force in direction d exerted by the jth agent on the ith agent Calculated as

[0054]

[0055] Among them, M j (t) is the normalized mass of the j-th agent at cycle time t, and ε is a constant used to avoid the distance R between the i-th agent and the j-th agent. ij (t) is equal to 0, and G is the gravitational constant.

[0056] As a further optimization scheme of the satellite interference source positioning method described in the present invention, each intelligent agent moves under the driving force, and the calculation formula of movement is:

[0057]

[0058] in, is the position of the i-th agent in direction d at time t+1, is the position of the i-th agent in direction d at time t, is the speed of the i-th agent in direction d at time t+1, satisfying the following formula

[0059]

[0060] Among them, b is the side length of the search space, rand i is the random number of the i-th agent, max represents the maximum value, is the velocity of the i-th agent in direction d at time t, and the acceleration of the i-th agent in direction d Satisfy the following formula

[0061]

[0062] Among them, F i d (t) is the force acting on the i-th agent in direction d at time t. When d = x, F i d (t) is F i x (t); when d = y, F i d (t) is F iy (t).

[0063] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0064] The method of the present invention first establishes an output characteristic model of the DNR and automatic gain control (AGC) of a commercial satellite receiver under different interference intensities. Secondly, ray tracing and a three-dimensional city model are combined with the receiver output characteristic model within each position grid to obtain a data set of the DNR and AGC at any interference source location within each position grid. Finally, a two-step prediction / optimization method is used to preliminarily predict the location grid of the interference source in the target area. Then, a positioning strategy based on signal matching or a source search strategy based on joint positioning is adopted to efficiently and accurately locate the location of the satellite interference source in a dense urban environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0066] Figure 2 is a schematic diagram of a specific implementation example of an interference source position predictor;

[0067] Figure 3 A flow chart of a signal matching method;

[0068] Figure 4 The figure is a flow chart of the joint positioning method. DETAILED DESCRIPTION

[0069] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0070] like Figure 1 As shown, an embodiment of the present invention discloses a method for locating satellite interference sources in a dense urban environment based on a two-step prediction / optimization method, comprising the following steps:

[0071] S1: Combining the geospatial information of the target urban area with a 3D city model, a satellite constellation simulator is used to generate a mixed signal of satellite interference signals and satellite signals with multipath errors. This mixed signal is used as the input of the satellite receiver, and the carrier-to-noise ratio and automatic gain control value of the satellite receiver under different interference intensities are recorded.

[0072] S2: Fitting the carrier-to-noise ratio and automatic gain control value obtained in step S1 to establish an output characteristic model of the satellite receiver under different interference intensities;

[0073] S3: Multiple static satellite receivers are evenly distributed in the open area of ​​the target area as interference signal monitoring stations, dividing the target area into a uniform grid. Within each grid, ray tracing and a 3D city model are used to randomly simulate the interference signal power received by each interference signal monitoring station at any interference source location, specifically including:

[0074] S3-1: Determine the signal emission position of the interference source, that is, the starting point of the ray tracing process;

[0075] S3-2: Simulate the propagation paths of an interference signal from a starting point in an urban environment. A specific implementation example of ray tracing uses the following parameters: generate 163,842 ray paths for each starting point, with an angle of 0.5391 degrees between each ray; ignore signal scattering; allow a maximum of 5 valid reflections; discard paths with a power attenuation greater than 40dB relative to the shortest path; and set the building and terrain materials to concrete, resulting in approximately 8dB of path loss per reflection.

[0076] S3-3: Calculate the interaction between the signal and the terrain and environmental objects, including reflection angle, scattering angle, absorption coefficient, and reflection coefficient, to determine the propagation direction, path, and intensity of the signal;

[0077] S3-4: Record all possible propagation paths by which the signal can reach the receiving location, as well as the strength, arrival time, arrival angle, and arrival frequency of the arriving signal.

[0078] S4: combining the output characteristic model described in step S2, obtaining a data set of the DNR and the AGC amount at any interference source position within each grid;

[0079] S5: Using the data set obtained in step S4 as input and the location grid of the data as labels, train the interference source location predictor;

[0080] S6: In actual applications, when the static satellite receiver of each interference signal monitoring station detects an interference signal, each static satellite receiver uploads its carrier-to-noise ratio and automatic gain control value in real time to the central processing control unit located in the cloud;

[0081] S7: The central processing control unit uses the interference source position predictor obtained after training in step S5 to make a preliminary prediction of the location grid where the interference source is located, such as Figure 2 As shown, a specific implementation example of the interference source position predictor may include:

[0082] S7-1: The carrier-to-noise ratios of the 1st to mth satellites and the automatic gain control value of an interference signal monitoring station constitute an observation vector;

[0083] S7-2: The observation vectors from all interference signal monitoring stations constitute the full state space data set;

[0084] S7-3: Randomly extract several sub-state spaces from the full state space data set, each sub-state space randomly includes four data in the full space data set;

[0085] S7-4: Using each sub-state space as a data set, train each sub-space KNN (K-nearest neighbor) classifier;

[0086] S7-5: The prediction results of each subspace KNN classifier are integrated and the final position grid prediction result is obtained through a voting mechanism.

[0087] S8: Within the location grid prediction given in step S7, a signal matching strategy based on the virtual interference source is used to perform a refined search to ultimately determine the precise location of the interference source. Figure 3 As shown, specifically including:

[0088] S8-1: Initialize the positions of multiple virtual interference sources within the prediction grid;

[0089] S8-2: Check whether the position of each virtual interference source exceeds the search space boundary;

[0090] S8-3: Calculate the fitness function value of each virtual interference source. The fitness function value is

[0091]

[0092] Where Q is the total number of interference signal monitoring stations, and the parameters α1 and α2 are used to normalize the values ​​of the carrier-to-noise ratio and the automatic gain control amount in two different units. and are the carrier-to-noise ratio and automatic gain control value of the qth interference signal monitoring station under real interference, and are the carrier-to-noise ratio and automatic gain control value of the qth interference signal monitoring station under the kth virtual interference source respectively.

[0093] S8-4: Determine whether the fitness function value is the minimum. If so, the loop terminates and the positioning solution is the final position of the virtual interference source with the minimum fitness function value in the last iteration. Otherwise, continue the following loop process.

[0094] S8-5: Calculate the quality of each virtual interference source, where the normalized formula for calculating the quality of the virtual interference source is:

[0095]

[0096] Where N is the total number of agents, M i (t) is the normalized mass of the i-th agent at cycle time t, m j (t) is the mass of the jth agent at cycle time t, m i (t) is the mass of the i-th agent at cycle time t, specifically

[0097]

[0098] Among them, fit i (t) is the fitness function value of the ith agent at cycle time t, best(t) and worst(t) are fit i The minimum and maximum values ​​of (t).

[0099] S8-6: Calculate the force of each virtual interference source, where the specific formula for calculating the force of the i-th virtual interference source is:

[0100]

[0101] Among them, F i 1 (t) is the force in direction 1 acting on the i-th virtual interference source at time t, F i 2 (t) is the force in direction 2 acting on the i-th virtual interference source at time t, is the component of force in direction 1 exerted by the jth virtual interference source on the ith virtual interference source at time t, is the component force in direction 2 of the jth virtual interference source acting on the ith virtual interference source at time t. Directions 1 and 2 are the x-direction and y-direction respectively. The x-direction and the y-direction are two mutually perpendicular directions of the two-dimensional plane coordinate system. The component force in direction d of the jth virtual interference source acting on the ith virtual interference source can be calculated as

[0102]

[0103] where ε is a small constant to avoid the distance R between i and j ij (t) is equal to 0, θ is the vector R ij and The angle between and They are the positions of the i-th virtual interference source and the j-th virtual interference source in direction d, respectively. In two-dimensional positioning, the dimension d=1 and 2, that is, in two-dimensional positioning, the direction d is two mutually perpendicular x directions or y directions.

[0104] S8-7: Each virtual interference source moves under the driving force, and then goes to step (8-2) to loop, where the calculation formula for position movement is:

[0105]

[0106] in, is the position of the i-th virtual interference source in direction d at time t+1, is the corresponding speed, satisfying the following formula

[0107]

[0108] Among them, b is the side length of the search space, rand i is the random number of the i-th virtual interference source, max represents the maximum value, and the acceleration of the i-th virtual interference source in direction d Satisfy the following formula

[0109]

[0110] In addition to the signal matching strategy described in S8, within the location grid prediction given in step S7, another implementation example is to adopt a joint search strategy based on drones, monitoring vehicles, and handheld monitoring devices, specifically:

[0111] S9: Within the location grid prediction given in step S7, a refined search is performed using or based on a joint search strategy of drones, monitoring vehicles, and handheld monitoring devices to ultimately determine the precise location of the interference source. Figure 4 As shown, specifically including:

[0112] S9-1: Initialize the positions of multiple dynamic monitoring receivers within the prediction grid. These dynamic monitoring receivers are specifically drones, monitoring vehicles, and handheld monitoring devices equipped with satellite receivers.

[0113] S9-2: Check whether the position of each dynamic monitoring receiver exceeds the search space boundary;

[0114] S9-3: Calculate the fitness function value of each dynamic monitoring receiver. The fitness function value is

[0115]

[0116] Where L is the total number of dynamic monitoring receivers, α1 and α2 are used to normalize the values ​​of the carrier-to-noise ratio and the automatic gain control amount in two different units, C / N0| l and AGC l is the carrier-to-noise ratio and automatic gain control value of the lth dynamic monitoring receiver.

[0117] S9-4: Determine whether the fitness function value is minimum. If so, the loop terminates and the positioning solution is the final position of the dynamic monitoring receiver with the minimum fitness function value in the last iteration. Otherwise, continue the following loop process.

[0118] S9-5: Calculate the quality of each dynamic monitoring receiver, where the normalized formula for calculating the quality of the dynamic monitoring receiver is:

[0119]

[0120] Where N is the total number of dynamic monitoring receivers, M i (t) is the normalized quality of the i-th dynamic monitoring receiver at cycle time t, m i (t) is the quality of the i-th dynamic monitoring receiver at cycle time t, specifically:

[0121]

[0122] Among them, fit i (t) is the fitness function value of the i-th dynamic monitoring receiver at cycle time t, and best(t) and worst(t) are defined as the minimum and maximum values, respectively.

[0123] S9-6: Calculate the force of each dynamic monitoring receiver, where the force of the i-th dynamic monitoring receiver is calculated The specific formula is

[0124]

[0125] Among them, F i 1 (t) is the force in direction 1 acting on the i-th dynamic monitoring receiver at time t, F i 2 (t) is the force in direction 2 acting on the i-th dynamic monitoring receiver at time t, is the component force in direction 1 exerted by the jth dynamic monitoring receiver on the ith dynamic monitoring receiver at time t, is the component force in direction 2 exerted by the jth dynamic monitoring receiver on the ith dynamic monitoring receiver at time t. Directions 1 and 2 are the x-direction and y-direction respectively, and the x-direction and the y-direction are two mutually perpendicular directions of the two-dimensional plane coordinate system. The component force in direction d exerted by the jth dynamic monitoring receiver on the ith dynamic monitoring receiver can be calculated as

[0126]

[0127] where ε is a small constant to avoid the distance R between i and j ij(t) is equal to 0, θ is the vector R ij and The angle between and They are the positions of the i-th dynamic monitoring receiver and the j-th dynamic monitoring receiver in direction d, respectively. In two-dimensional positioning, the dimension d = 1 and 2, that is, in two-dimensional positioning, the direction d is two mutually perpendicular x directions or y directions.

[0128] S9-7: Each dynamic monitoring receiver moves under the driving force, and then goes to step (9-2) to loop, where the calculation formula for position movement is:

[0129]

[0130] in, To dynamically monitor the position of receiver i in direction d at time t+1, is the corresponding speed, satisfying the following formula

[0131]

[0132] Among them, b is the side length of the search space, rand i is the random number of the i-th dynamic monitoring receiver, max represents the maximum value, and the acceleration of the i-th dynamic monitoring receiver in direction d Satisfy the following formula

[0133]

[0134] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for locating a satellite interference source, characterized in that: include: Combining the geospatial information of the target urban area with a 3D city model, a mixed signal of a satellite interference signal and a satellite signal with multipath error is generated. This mixed signal is used as the input of a satellite receiver, and the carrier-to-noise ratio and automatic gain control value of the satellite receiver under different interference intensities are recorded. The obtained carrier-to-noise ratio and automatic gain control value are fitted to establish the output characteristic model of the satellite receiver under different interference intensities. Multiple interference signal monitoring stations are evenly arranged in the target area, dividing the target area into uniform grids; within each grid, arbitrary interference source positions are randomly simulated using ray tracing and a three-dimensional city model; based on the interference signal power received by each interference signal monitoring station and in combination with the output characteristic model, a data set is obtained at the arbitrary interference source position within each grid, the data set including carrier-to-noise ratio and automatic gain control amount; The obtained dataset is used as input and the location grid of the data is used as label to train the interference source location predictor; Based on the carrier-to-noise ratio and automatic gain control value actually received by each interference signal monitoring station, the interference source location predictor obtained after training is used to make a preliminary prediction of the location grid where the interference source is located, and a predicted location grid is obtained; In the predicted location grid, a signal matching strategy based on virtual interference sources is used to search and finally determine the location of the interference source; or, Within the predicted location grid, a search is performed based on a joint search strategy of aerial monitoring equipment and ground monitoring equipment to ultimately determine the location of the interference source.

2. A satellite interference source positioning method according to claim 1, characterized in that: A satellite constellation simulator is used to generate a mixed signal of satellite interference signals and satellite signals with multipath errors; multiple interference signal monitoring stations are evenly arranged in open areas of the target area, where there are no obstacles that block or reflect the signals.

3. The satellite interference source positioning method according to claim 1, characterized in that: The interference signal monitoring station is a static satellite receiver. The central processing control unit in the cloud uses the trained interference source location predictor to make a preliminary prediction of the interference source location grid. The details are as follows: When the static satellite receiver at each interference signal monitoring station detects an interference signal, it uploads its carrier-to-noise ratio and automatic gain control value in real time to the central processing and control unit located in the cloud. Based on this carrier-to-noise ratio and automatic gain control value, the central processing and control unit uses the trained interference source location predictor to make a preliminary prediction of the location grid of the interference source.

4. The method for locating a satellite interference source according to claim 1, wherein: Using ray tracing and a 3D city model, we randomly simulated the location of any interference source; specifically, as follows: Step 1-1, determine the signal emission position of the interference source, that is, the starting point of the ray tracing process; Step 1-2: simulate the propagation paths of the interference signal from the starting point in the environment; Steps 1-3: Calculate the interaction between the interference signal and the terrain and environmental objects, including the reflection angle, scattering angle, absorption coefficient, and reflection coefficient, to determine the propagation direction, path, and intensity of the interference signal; Steps 1-4: Record all possible propagation paths of the interference signal to the receiving location, as well as the strength, arrival time, arrival angle, and arrival frequency information of the arriving signal.

5. The method for locating a satellite interference source according to claim 1, wherein: The interference source location predictor obtained after training is used to make a preliminary prediction of the location grid where the interference source is located to obtain the predicted location grid; the details are as follows: Each observation vector is composed of the carrier-to-noise ratio and automatic gain control value of the static satellite receiver of each interference signal monitoring station. The observation vectors of all interference signal monitoring stations constitute a full state space dataset. Several sub-state spaces are randomly selected from the full state space dataset, and each sub-state space is used as a dataset to train each sub-learner in the interference source position predictor. The prediction results of each sub-learner are combined to obtain the final position grid prediction result.

6. The method for locating a satellite interference source according to claim 1, wherein: Within the predicted location grid, a signal matching strategy based on a virtual interference source is used to search to ultimately determine the location of the interference source; or, Within the predicted location grid, a search is conducted based on a joint search strategy using both aerial and ground monitoring equipment to ultimately determine the location of the interference source. The details are as follows: When adopting the signal matching strategy of virtual interferers, multiple virtual interferers are initialized within the predicted position grid as the intelligent agents of the search method; When using a joint search strategy based on aerial and ground-based monitoring equipment, multiple dynamic monitoring satellite receivers are initialized within the predicted location grid as intelligent agents of the search method; wherein the dynamic monitoring satellite receivers are equipped on the aerial and ground-based monitoring equipment; Step 2-1: Check whether the position of each agent exceeds the boundary of the search space; Step 2-2, calculate the fitness function value of each agent; Step 2-3, determine whether the fitness function value is minimum: If the fitness function value is not the minimum, continue to steps 2-4; If the fitness function value is the minimum, steps 2-4 are not executed, and the positioning solution is the final position of the agent with the minimum fitness function value in the last iteration; Steps 2-4: Calculate the quality of each agent; Calculate the forces acting on each agent; Each agent moves under the driving force and then goes to step 2-1 to loop.

7. A satellite interference source positioning method according to claim 6, characterized in that: The intelligent agent is a virtual interference source in the signal matching strategy, and the aerial monitoring equipment and ground monitoring equipment in the joint positioning strategy are drones equipped with dynamic monitoring satellite receivers, monitoring vehicles, and handheld monitoring devices; In step 2-2, when the signal matching strategy is adopted, the fitness function value of each agent is Where Q is the total number of interference signal monitoring stations, and the parameters α1 and α2 are used to normalize the values ​​of the carrier-to-noise ratio and the automatic gain control amount in two different units. and are the carrier-to-noise ratio and automatic gain control value of the qth interference signal monitoring station under real interference, and are the carrier-to-noise ratio and automatic gain control value of the qth interference signal monitoring station under the kth virtual interference source respectively.

8. The method for locating a satellite interference source according to claim 6, wherein: In step 2-2, when the joint positioning strategy is adopted, the fitness function value of each agent is Where L is the total number of dynamic monitoring receivers, parameters α1 and α2 are used to normalize the values ​​of carrier-to-noise ratio and automatic gain control amount in two different units, C / N0| l and AGC l is the carrier-to-noise ratio and automatic gain control value of the lth dynamic monitoring receiver.

9. The method for locating a satellite interference source according to claim 6, wherein: In steps 2-4, the normalized formula for calculating the quality of the agent is Where N is the total number of agents, M i (t) is the normalized mass of the i-th agent at cycle time t, m j (t) is the mass of the jth agent at cycle time t, m i (t) is the mass of the i-th agent at cycle time t, specifically Among them, fit i (t) is the fitness function value of the ith agent at cycle time t, best(t) and worst(t) are fit i (t) minimum and maximum values; In steps 2-4, the formula for calculating the force of each agent is Among them, F i x (t) is the force in the x direction acting on the i-th agent at time t, F i y (t) is the force in the y direction acting on the i-th agent at time t, is the component of the force in the x direction exerted by the j-th agent on the ith agent at time t, is the component of force in the y direction exerted by the jth agent on the ith agent at time t. The x and y directions are two mutually perpendicular directions in the two-dimensional plane coordinate system. θ is the vector R ij (t) and The angle between and are the positions of the i-th agent and the j-th agent in direction d, respectively. Direction d is the two mutually perpendicular x-directions or y-directions; The component of the force in direction d exerted by the jth agent on the ith agent Calculated as Among them, M j (t) is the normalized mass of the j-th agent at cycle time t, and ε is a constant used to avoid the distance R between the i-th agent and the j-th agent. ij (t) is equal to 0, and G is the gravitational constant.

10. A satellite interference source positioning method according to claim 9, characterized in that: Each agent moves under the driving force, and the calculation formula for movement is: in, is the position of the i-th agent in direction d at time t+1, is the position of the i-th agent in direction d at time t, is the speed of the i-th agent in direction d at time t+1, satisfying the following formula Among them, b is the side length of the search space, rand i is the random number of the i-th agent, max represents the maximum value, is the velocity of the i-th agent in direction d at time t, and the acceleration of the i-th agent in direction d Satisfy the following formula Among them, F i d (t) is the force acting on the i-th agent in direction d at time t. When d = x, F i d (t) is F i x (t); when d = y, F i d (t) is F i y (t).

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