Satellite interference source positioning method

Abstract

The invention discloses a satellite interference source positioning method, which relates to the technical field of satellite navigation and wireless communication, and comprises the following steps: firstly, generating a satellite interference signal by a satellite constellation simulator, and recording carrier-to-noise ratios and automatic gain control quantities under different interference powers by a satellite receiver; establishing an output characteristic model of the receiver under different interference intensities; dividing a target area into uniform grids, and simulating interference signal power received by a receiver at any interference source position in the grids by using ray tracing and a three-dimensional city model; obtaining a data set of the carrier-to-noise ratio and the automatic gain control quantity in combination with the output characteristic model; training an interference source position predictor by using the data set; the position predictor runs at a cloud end, and performs position prediction according to the received actual measurement information; and finally determining the accurate position of the interference source by using a signal matching or joint positioning strategy. The method is high in implementability, and can improve the positioning precision of the satellite interference source in the dense city environment.

Description

Technical Field

[0001] The 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] As a global satellite navigation system independently built by my country, the BeiDou satellite navigation system has become a major infrastructure related to the national economy and people's livelihood. It provides all-weather location and timing services for key infrastructure such as communications, electricity, finance and transportation, and has become an important force in promoting economic and social development and ensuring national security. However, due to its fragility, BeiDou satellite signals are easily interfered by natural or artificial signals. In particular, illegal human interference seriously threatens the accuracy and reliability of BeiDou services, especially in core areas such as cities with dense populations and key infrastructure. It threatens the city's information security and communication stability, and poses a safety hazard to key areas such as urban management and emergency response. Therefore, it is particularly important to locate the source of satellite interference in dense urban environments and provide law enforcement officers with corresponding law enforcement tools.

[0003] However, the complexity of dense urban environments poses a severe challenge to satellite interference source positioning. The densely packed high-rise buildings and intricate streets not only block the line-of-sight propagation path of the signal, but also cause multiple reflections and scattering of the signal during the propagation process. These signals that have traveled through complex paths have become highly mixed when they reach the receiving end, making them difficult to accurately resolve. Information such as the signal's strength, phase, arrival time, and arrival angle are all severely affected, making it difficult to effectively apply traditional positioning methods and greatly reducing positioning 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 efficiently and accurately locate the location 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] Combine the geospatial information and three-dimensional city model of the target urban area to generate a mixed signal of the satellite interference signal and the satellite signal with multipath error. Use the mixed signal as the input of the satellite receiver and record the carrier-to-noise ratio and automatic gain control amount of the satellite receiver under different interference intensities.

[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, and the target area is divided into uniform grids; in each grid, ray tracing and a three-dimensional city model are used to randomly simulate the position of any interference source, and according to the interference signal power received by each interference signal monitoring station, combined with the output characteristic model, a data set at the position of any interference source in each grid is obtained, and the data set includes a carrier-to-noise ratio and an automatic gain control amount;

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

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

[0013] In the predicted location grid, a signal matching strategy based on a virtual interference source is used to search to finally 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 a 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 errors; 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 position 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 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 and control unit located in the cloud; based on the carrier-to-noise ratio and automatic gain control value, the central processing and control unit uses the interference source position predictor obtained after training 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 position 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] Step 1-3, calculate the interaction between the interference signal and the terrain and environmental objects, which includes reflection angle, scattering angle, absorption coefficient and reflection coefficient, to determine the propagation direction, path and intensity of the interference signal;

[0022] Step 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 position grid where the interference source is located to obtain the predicted position 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 data set. Several sub-state spaces are randomly selected from the full state space data set, and each sub-state space is used as a data set 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] In the predicted location grid, a signal matching strategy based on a virtual interference source is used to search to finally determine the location of the interference source; or,

[0027] Within the predicted location grid, a search is performed based on a joint search strategy of airborne monitoring equipment and ground monitoring equipment to ultimately determine the location of the interference source; the details are as follows:

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

[0029] When a joint search strategy based on airborne monitoring equipment and ground-based monitoring equipment is adopted, multiple dynamic monitoring satellite receivers are initialized in the predicted position grid as intelligent agents of the search method; wherein the dynamic monitoring satellite receivers are equipped on the airborne monitoring equipment and the 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 the 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] Step 2-4: Calculate the quality of each agent;

[0036] Calculate the forces on each agent;

[0037] Each agent moves under the drive of the 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 equipment 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] Among them, 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 described in the present invention, in step 2-2, when the joint positioning strategy is adopted, the fitness function value of each agent is

[0043]

[0044] Among them, L is the total number of dynamic monitoring receivers, 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. 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 step 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 ith 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 ith 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 x-direction component of the force exerted by the j-th agent on the ith agent at time t, is the component force in the y direction of the jth agent acting on the ith agent at time t. The x direction and the y direction 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 ith agent and the jth agent in direction d, respectively. Direction d is 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 ith 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 the movement is:

[0057]

[0058] in, is the position of the ith agent in direction d at time t+1, is the position of the ith agent in direction d at time t, is the speed of the ith 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 ith agent, max represents the maximum value, is the speed of the ith agent in direction d at time t, and the acceleration of the ith 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 signal-to-noise ratio and the automatic gain control amount of a commercial satellite receiver under different interference intensities; secondly, ray tracing and a three-dimensional city model are used in each position grid, combined with the receiver output characteristic model, to obtain a data set of the signal-to-noise ratio and the automatic gain control amount at any interference source position in each position grid; finally, a two-step prediction / optimization method is adopted, firstly, a preliminary prediction is made of the position grid where the interference source is located in the target area, and 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 position 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 method for signal matching;

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

[0069] In order to make the purpose, 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, the embodiment of the present invention discloses a satellite interference source positioning method based on a prediction / optimization two-step method in a dense urban environment, comprising the following steps:

[0071] S1: Combine the geospatial information and 3D city model of the target urban area, use the satellite constellation simulator to generate a mixed signal of satellite interference signal and satellite signal with multipath error, use the mixed signal as the input of the satellite receiver, and record the carrier-to-noise ratio and automatic gain control amount of the satellite receiver under different interference intensities;

[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 arranged in the open area of ​​the target area as interference signal monitoring stations, and the target area is divided into uniform grids. Ray tracing and three-dimensional city models are used in each grid to randomly simulate the interference signal power received by each interference signal monitoring station at any interference source location, 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 interference signals in an urban environment starting from the starting point. A specific implementation example of ray tracing, the setting parameters of which can be: 163842 light paths are simulated for each starting point, and the angle between each light is 0.5391 degrees; ignore the scattering effect of the signal; allow a maximum of 5 effective reflections; discard paths with a power attenuation greater than 40dB relative to the shortest path; set the materials of buildings and terrain to concrete, so there is a path loss of about 8dB for each 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 information of the arriving signal.

[0078] S4: combining the output characteristic model described in step S2, obtaining a data set of the carrier-to-noise ratio and the automatic gain control amount at any interference source position in each grid;

[0079] S5: using the data set obtained in step S4 as input and the location grid where the data is located as a label, training 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 amount to a central processing control unit located in the cloud in real time;

[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 extracting a number of sub-state spaces from the full state space data set, each sub-state space randomly including four data in the full space data set;

[0085] S7-4: training each subspace KNN (K-nearest neighbor) classifier using each sub-state space as a data set;

[0086] S7-5: The prediction results of each subspace KNN classifier are integrated to obtain the final location grid prediction result through a voting mechanism.

[0087] S8: In the location grid prediction given in step S7, a signal matching strategy based on a 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] Among them, 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 ith 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 ith 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 force in direction 1 of the jth virtual interference source acting 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 respectively the positions of the ith virtual interference source and the jth virtual interference source in direction d. 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 ith virtual interference source, max represents the maximum value, and the acceleration of the ith 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 the minimum. If so, the loop is terminated 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 ith 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 ith dynamic monitoring receiver at cycle time t, and best(t) and worst(t) are defined as the minimum and maximum values ​​thereof, 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 of the jth dynamic monitoring receiver acting 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 of the jth dynamic monitoring receiver acting 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 respectively the positions of the i-th dynamic monitoring receiver and the j-th dynamic monitoring receiver in direction d. 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 implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A satellite interference source positioning method, characterized in that: include: Combine the geospatial information and three-dimensional city model of the target urban area to generate a mixed signal of the satellite interference signal and the satellite signal with multipath error. Use the mixed signal as the input of the satellite receiver and record the carrier-to-noise ratio and automatic gain control amount of the satellite receiver under different interference intensities. 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, and the target area is divided into uniform grids; in each grid, ray tracing and a three-dimensional city model are used to randomly simulate the position of any interference source, and according to the interference signal power received by each interference signal monitoring station, combined with the output characteristic model, a data set at the position of any interference source in each grid is obtained, and the data set includes a carrier-to-noise ratio and an automatic gain control amount; The obtained data set is used as input and the location grid of the data is used as a label to train the interference source location predictor; According to the carrier-to-noise ratio and automatic gain control amount actually received by each interference signal monitoring station, the interference source position predictor obtained after training is used to make a preliminary prediction of the position grid where the interference source is located, and obtain the predicted position grid; In the predicted location grid, a signal matching strategy based on a virtual interference source is used to search to 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 a satellite interference signal and a satellite signal with multipath errors; multiple interference signal monitoring stations are evenly arranged in the open area of ​​the target area, and the open area refers to an area where there are no obstacles that block or reflect the signal.

3. A satellite interference source positioning method according to claim 1, characterized in that: The interference signal monitoring station is a static satellite receiver; the initial prediction of the location grid of the interference source using the interference source location predictor obtained after training is realized by the central processing control unit in the cloud; the details are as follows: 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 and control unit located in the cloud; based on the carrier-to-noise ratio and automatic gain control value, the central processing and control unit uses the interference source position predictor obtained after training to make a preliminary prediction of the location grid of the interference source.

4. A satellite interference source positioning method according to claim 1, characterized in that: Using ray tracing and a 3D city model, we randomly simulated the location of any interference source; the details are 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; Step 1-3, calculate the interaction between the interference signal and the terrain and environmental objects, which includes reflection angle, scattering angle, absorption coefficient and reflection coefficient, to determine the propagation direction, path and intensity of the interference signal; Step 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, characterized in that: 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 data set. Several sub-state spaces are randomly selected from the full state space data set, and each sub-state space is used as a data set 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. A satellite interference source positioning method according to claim 1, characterized in that: In the predicted location grid, a signal matching strategy based on a virtual interference source is used to search to 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 airborne monitoring equipment and ground monitoring equipment to ultimately determine the location of the interference source; the details are as follows: When the signal matching strategy of virtual interferers is adopted, multiple virtual interferers are initialized in the predicted position grid as the intelligent agents of the search method; When a joint search strategy based on airborne monitoring equipment and ground-based monitoring equipment is adopted, multiple dynamic monitoring satellite receivers are initialized in the predicted position grid as intelligent agents of the search method; wherein the dynamic monitoring satellite receivers are equipped on the airborne monitoring equipment and the 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 the 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; Step 2-4: Calculate the quality of each agent; Calculate the forces on each agent; Each agent moves under the drive of the 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, monitoring vehicles and handheld monitoring equipment equipped with dynamic monitoring satellite receivers; In step 2-2, when the signal matching strategy is adopted, the fitness function value of each agent is Among them, 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. A satellite interference source positioning method according to claim 6, characterized in that: In step 2-2, when the joint positioning strategy is adopted, the fitness function value of each agent is Among them, L is the total number of dynamic monitoring receivers, 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. C / N0| l and AGC l is the carrier-to-noise ratio and automatic gain control value of the lth dynamic monitoring receiver.

9. A satellite interference source positioning method according to claim 6, characterized in that: In step 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 ith 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 ith 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 x-direction component of the force exerted by the j-th agent on the ith agent at time t, is the component force in the y direction of the jth agent acting on the ith agent at time t. The x direction and the y direction are two mutually perpendicular directions in the two-dimensional plane coordinate system. θ is the vector R ij 9t) and The angle between and are the positions of the ith agent and the jth agent in direction d, respectively. Direction d is 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 ith 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 drive of the force, and the calculation formula for movement is: in, is the position of the ith agent in direction d at time t+1, is the position of the ith agent in direction d at time t, is the speed of the ith 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 ith agent, max represents the maximum value, is the speed of the ith agent in direction d at time t, and the acceleration of the ith 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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