A method and system for driving away a drone from a no-fly zone by a pseudolite signal

Abstract

The application discloses a pseudo-satellite signal unmanned aerial vehicle no-fly zone driving-off method and system. The application continuously tracks a target and solves its motion state through a ground radar system, and plans a smooth driving-off path meeting dynamic constraints; a ground monitoring system monitors real satellite signal parameters in airspace, automatically decides a pseudo-satellite number and controls signal strength to be equivalent to the real signal; the system generates pseudo-satellite signals highly consistent with the real signal, broadcasts independent corresponding pseudo-range and carrier phase, and makes the unmanned aerial vehicle positioning result smoothly transit to the driving-off path; the closed loop process is repeated until the unmanned aerial vehicle position and velocity vector safely leave the no-fly zone. The application does not need to crack the unmanned aerial vehicle communication protocol, does not depend on the target itself, has the advantages of strong concealment, controllable flight path, high driving-off success rate and low accompanying influence.

Description

Technical Field

[0001] This invention discloses a method and system for driving away unmanned aerial vehicles (UAVs) from no-fly zones using pseudo-satellite signals, belonging to the field of navigation and positioning. Background Technology

[0002] This invention discloses a method for driving away unmanned aerial vehicles (UAVs) from no-fly zones using pseudo-satellite signals, belonging to the field of navigation and positioning. Traditional methods for handling scenarios where UAVs mistakenly enter no-fly zones often rely on communication protocol cracking or active interference, which has limitations such as difficulty in protocol adaptation, uncontrollable flight paths, and the risk of UAVs crashing out of control. Therefore, this invention proposes a refined driving-away method based on pseudo-satellite signal generation and broadcasting. A ground-based radar system continuously tracks the target UAV and plans a smooth driving-away path that satisfies the UAV's dynamic constraints in real time. The ground monitoring system uses a full-system, full-band GNSS signal monitoring receiver to monitor parameters such as ephemeris, ionospheric delay, and the number of visible satellites in the target airspace in real time. It generates pseudo-satellite signals with code phase and carrier frequency highly consistent with real satellites. The pseudo-satellite broadcasting system then broadcasts these signals to each pseudo-satellite with its independent pseudorange and carrier phase, repeating the process of status monitoring, pseudo-satellite signal generation, and broadcasting until the UAV's position is outside the no-fly zone boundary and its velocity direction is away from the no-fly zone. This invention does not require cracking the drone communication protocol, does not rely on the drone's own cooperation, and has the advantages of strong concealment, controllable trajectory, high success rate of driving away drones, and low collateral impact, providing a refined solution for the field of anti-drone operations. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for driving away drones from no-fly zones using pseudo-satellite signals, in order to solve the problems that traditional handling methods rely on communication protocol cracking or active interference, which have difficulties in protocol adaptation, uncontrollable flight paths, and are prone to causing drones to crash out of control.

[0004] The first aspect of this application provides a method for driving away unmanned aerial vehicles (UAVs) from no-fly zones using pseudo-satellite signals, the method comprising: Step 1: Continuously detect and locate the UAV using ground radar. After calculating the target's spatial position, velocity, and acceleration in real time, generate a drive-away path that follows the UAV's maneuverability and dynamic limitations. Step 2: The ground monitoring system uses full-system, full-band receivers to continuously monitor ephemeris, ionospheric delay and satellite visibility in the airspace, and automatically decides the number of pseudo-satellites based on this, while adjusting in real time to maintain the consistency between the pseudo-satellite signal strength and the real satellite signal strength.

[0005] Step 3: The ground monitoring system generates pseudo-satellite signals that are highly similar to real satellites, and provides each pseudo-range and carrier phase with independent pseudo-range and carrier phase. The pseudo-satellite signals are broadcast so that the positioning results calculated by the UAV receiver can be smoothly transitioned along the drive-off path. Step 4: Repeatedly execute the closed-loop control for UAV status detection and positioning, pseudo-satellite signal generation and broadcasting until the UAV's current position has exceeded the no-fly zone boundary and its velocity vector direction is away from the no-fly zone.

[0006] Step 1 includes: Step 1.1: The ground radar transmits electromagnetic waves and receives the target echo, calculating the UAV's position in the radar coordinate system. Epochal three-dimensional spatial position for:

[0007] In the formula, Target slant range, azimuth The elevation angle is the angle between the target and the radar's north direction in the horizontal plane. The angle between the line connecting the target and the radar and the horizontal plane; Step 1.2: Construct a UAV motion state vector containing three-dimensional position, velocity, and acceleration. Based on continuous radar observation data, use a filtering estimation algorithm to perform state prediction and measurement update, and estimate the UAV's three-dimensional spatial velocity and acceleration in real time. The motion state of the UAV in three-dimensional space is defined as a 9-dimensional vector:

[0008] In the formula, For three-dimensional space velocity, Acceleration in three-dimensional space; Based on the current state Predicting motion state over time get: In the formula, This is the state transition matrix; The measurement update was performed using the current observations, resulting in:

[0009] In the formula, Kalman gain The fusion weight matrix for prediction and measurement. Let be the radar observation vector, and the observation function be... This is the projection transformation function. It filters and predicts continuously observed data to estimate the target's velocity and acceleration in real time. Step 1.3: Set the starting state and the desired ending state for path planning, construct the objective function for path planning based on minimizing motion smoothness and terminal state deviation, and introduce the maximum speed and maximum acceleration of the UAV as dynamic constraints into the planning constraints to establish a target optimization model for generating a drive-off path that satisfies the dynamic constraints. Assume the initial state of the planned path and termination state for:

[0010] In the formula, the starting position is The initial speed is The initial acceleration is The termination position is The termination speed is The final acceleration is N is the observation epoch at the time of termination; The objective function for constructing the planned path can be expressed as follows:

[0011] In the formula, For terminal speed coefficient, For terminal acceleration coefficient, The velocity is the speed at which the vehicle departs in the desired direction, and Δt is the time interval between adjacent epochs. The following are the additional constraints on the maneuverability and dynamics of the UAV:

[0012] In the formula, The maximum speed of the drone, This represents the maximum acceleration of the drone. Step 1.4: Represent the departure trajectory and its corresponding velocity and acceleration as a high-order polynomial function, combine the start and end states and constraints to perform parameterization and matrix solving on the target optimization model to obtain polynomial coefficient estimates, and substitute the polynomial coefficient estimates back into the high-order polynomial function to obtain a smooth departure path that follows the UAV's maneuverability and dynamics constraints; The smooth departure trajectory is defined as a 5th-degree polynomial:

[0013] The corresponding velocity and acceleration polynomials are:

[0014]

[0015] In the formula, the initial coefficient Determined by the initial state, respectively , , T represents time. Initial coefficient. This can be expressed as:

[0016] Define the vector of parameters to be estimated Expanding the objective function, we get:

[0017] In the formula, , , They are represented as follows:

[0018]

[0019]

[0020] Further, we obtain the system of linear equations:

[0021] The estimated value of the parameter vector to be estimated is then obtained as follows:

[0022] The smooth departure trajectory can be obtained by substituting the polynomial coefficients back.

[0023] Step 2 includes: Step 2.1: The ground monitoring system uses a ground monitoring receiver to continuously monitor the operational status parameters, space environment delay parameters, and position accuracy factors of real satellites in the target airspace to obtain basic navigation data for evaluating the target airspace positioning geometry and signal environment; The ground monitoring system continuously monitors the actual number of satellites in the airspace using receivers across the entire system and all frequency bands. Real-time track position Satellite clock bias Ionospheric delay Position accuracy factor ; Step 2.2: Based on the basic navigation data, with the combined positioning accuracy factor including pseudo-satellites in the target area meeting a preset threshold as a constraint, calculate the minimum number of pseudo-satellites required, and combine the operating status parameters of real satellites with a spatial interpolation algorithm to determine the virtual orbital position and virtual ephemeris parameters corresponding to each pseudo-satellite. Based on real satellite data With pseudo-satellite numbers To meet the requirement of optimal positioning accuracy in the region, the objective function is set as the maximum value within the target area. Value below threshold ,but The minimum value can be expressed as:

[0024] Using real satellite orbital positions Satellite clock bias Ionospheric delay Spatial interpolation Real-time orbital position of a pseudo-satellite Satellite clock bias Ionospheric delay Where n is the pseudo-satellite count, N j The minimum value of n to satisfy the PDOP condition; Step 2.3: Based on the virtual orbital position of each pseudosatellite, the target transmission power of the pseudosatellite is calculated using the radio link transmission loss model. A closed-loop control mechanism with dynamic error zeroing is constructed by using the pseudosatellite signal reception power fed back in real time by the ground monitoring receiver. The transmission energy of the pseudosatellites is dynamically adjusted to achieve automatic decision-making on the number of pseudosatellites and real-time control to maintain a power balance state that keeps the pseudosatellite signal strength consistent with the real satellite signal strength. The ground monitoring system calculates the current real satellite signal reference power. pseudo-satellite launch power Must meet:

[0025] In the formula, For transmission distance, For frequency, For transmit and receive antenna gain, For received power, Atmospheric attenuation; Real-time transmission of pseudo-satellite signal reception power using ground monitoring receivers The error is brought to zero by using proportional-integral dynamic adjustment of numerical control attenuation.

[0026] In the formula, This is the proportional gain coefficient. Here, k represents the integral gain coefficient, and k represents the observation epoch. Step 3 includes: Step 3.1: Based on the principle of baseband signal modulation, construct a mathematical model of pseudo-satellite radio frequency transmission signal, including ranging code, navigation message, code phase delay, carrier frequency and initial phase of carrier, so as to establish a control parameter framework for generating highly realistic pseudo-satellite basic signals; The signal transmitted by the pseudo-satellite is represented as:

[0027] In the formula, For the first The amplitude of the pseudo-satellite signal, It is a pseudo-random ranging code. For navigation message data codes, For code phase delay, For carrier frequency, For the initial phase of the carrier, s (i) Let T be the pseudo-satellite signal function, and T be time. Step 3.2: Determine the mapping relationship between code phase delay and induced pseudorange, and calculate the relative geometric distance between the target UAV and each pseudosatellite according to the planned drive-away path. Then, implement decoupling control on the launch delay of each pseudosatellite to provide independent target pseudorange observation values ​​that match the drive-away path for the target UAV. Code phase delay Estimated distance between pseudo-satellites and drones The relationship between them is:

[0028] For the Given a pseudo-satellite, the pseudorange measured by the UAV receiver should be:

[0029] In the formula, Location of the drone receiver. For the first The location of the pseudo-satellite At the speed of light, For the clock bias of the drone receiver, For pseudo-satellite clock bias, This is noise in pseudorange measurements; Step 3.3: Set the initial carrier phase of the transmitted signal of each pseudo-satellite, and decompose the expected carrier phase observation value into a combination of integer ambiguity and fractional phase for precise quantization, so as to construct an independent carrier phase fine control parameter with spatial position sensitivity. The ground monitoring system sets the initial phase of the carrier wave for the signal transmitted by each pseudosatellite. The received carrier phase is The carrier phase observation can be expressed as:

[0030] In the formula, integer ambiguity, fractional phase ; Step 3.4: Based on wavelength relationship and constant offset compensation, establish and maintain physical consistency constraints between the target pseudorange observation value and the carrier phase fine control parameter, and control the pseudo-satellite broadcasting system to broadcast the pseudo-satellite signal to generate a pseudo-satellite signal that is highly simulated with the real satellite, and provide each pseudo-satellite with independent pseudorange and carrier phase, so that the positioning result calculated by the UAV receiver can smoothly transition along the drive-away path; Ground monitoring systems must ensure the physical consistency of pseudorange and carrier phase, i.e.

[0031] In the formula, For carrier wavelength, The offset is a constant. By broadcasting pseudo-satellite signals, the UAV receiver can receive both real and pseudo-satellite signals, calculate the positioning results, and smoothly transition away along the driving path.

[0032] Step 4 includes: Step 4.1: Execute the control closed loop of state detection and positioning, pseudo-satellite signal generation and broadcasting in a loop, determine that the UAV positioning trajectory after smoothing and filtering has completely deviated from the geometric boundary of the no-fly zone, determine that the projection component of the UAV velocity vector on the normal of the no-fly zone boundary is negative, and that the deviation angle remains stable in multiple consecutive epochs; Step 4.2: After determining that the UAV has completely left the geometric boundary of the no-fly zone, gradually reduce the number of pseudo-satellites broadcast to the minimum navigation requirement, and simultaneously adjust the navigation ephemeris parameters of the remaining pseudo-satellites broadcast to avoid positioning jumps in the UAV receiver due to rank deficiency in the observation equation or drastic changes in geometric configuration, until the number of broadcast pseudo-satellite signals is reduced to zero.

[0033] A second aspect of this application provides a system for driving away unmanned aerial vehicles (UAVs) from no-fly zones using pseudo-satellite signals, the system comprising: The drive-away path generation module is used to continuously detect and locate the UAV using ground radar. After calculating the target's spatial position, velocity, and acceleration in real time, it generates a drive-away path that follows the UAV's maneuverability and dynamics constraints. The control module is used by the ground monitoring system to continuously monitor the ephemeris, ionospheric delay and satellite visibility in the airspace using the full system and full frequency band receivers, and automatically decide the number of pseudo-satellites based on this, while adjusting in real time to maintain the consistency between the pseudo-satellite signal strength and the real satellite signal strength. The pseudo-satellite signal broadcasting module is used by the ground monitoring system to generate pseudo-satellite signals that are highly simulated with real satellites, and to provide each pseudo-satellite with independent pseudorange and carrier phase, and broadcast the pseudo-satellite signals so that the positioning results calculated by the UAV receiver can be smoothly transitioned along the driving path; The loop execution module is used to repeatedly execute the control closed loop of UAV status detection and positioning, pseudo-satellite signal generation and broadcasting, until the UAV's current position has exceeded the no-fly zone boundary and its velocity vector direction has deviated from the no-fly zone.

[0034] The present invention has the following beneficial effects: Traditional methods for dealing with drones entering no-fly zones due to navigational deviations or misjudgments often rely on communication protocol cracking or electromagnetic interference, which suffers from inherent drawbacks such as protocol incompatibility, uncontrollable flight paths, and a high risk of crashes. This invention proposes a refined active deterrence scheme. It uses a ground-based radar system to continuously track the target drone and plan its decoy path, generating and broadcasting pseudo-satellite signals until the drone's actual position exceeds the no-fly zone boundary and its velocity vector direction deviates from the no-fly zone. This invention requires neither cracking the drone's communication protocol nor cooperation from the drone itself, and boasts significant advantages such as high stealth, controllable flight path, high deterrence success rate, and low collateral damage. It provides a highly reliable and refined solution for the counter-drone field. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of a method for driving away drones from no-fly zones using pseudo-satellite signals, as described in this invention. Figure 2 This is a flowchart of a method for driving away unmanned aerial vehicles (UAVs) from no-fly zones using pseudo-satellite signals, as described in this invention. Figure 3 This invention provides a method for driving away drones from no-fly zones using pseudo-satellite signals, illustrating the driving-away path. Figure 4 This is a structural diagram of a pseudo-satellite signal-based drone no-fly zone deterrence system according to the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0037] A method for driving away drones from no-fly zones using pseudo-satellite signals, such as Figure 1 ,include: Step 1: Continuously detect and locate the UAV using ground radar. After calculating the target's spatial position, velocity, and acceleration in real time, generate a drive-away path that follows the UAV's maneuverability and dynamic limitations. Step 2: The ground monitoring system uses full-system, full-band receivers to continuously monitor ephemeris, ionospheric delay and satellite visibility in the airspace, and automatically decides the number of pseudo-satellites based on this, while adjusting in real time to maintain the consistency between the pseudo-satellite signal strength and the real satellite signal strength.

[0038] Step 3: The ground monitoring system generates pseudo-satellite signals that are highly similar to real satellites, and provides each pseudo-range and carrier phase with independent pseudo-range and carrier phase. The pseudo-satellite signals are broadcast so that the positioning results calculated by the UAV receiver can be smoothly transitioned along the drive-off path. Step 4: Repeatedly execute the closed-loop control for UAV status detection and positioning, pseudo-satellite signal generation and broadcasting until the UAV's current position has exceeded the no-fly zone boundary and its velocity vector direction is away from the no-fly zone.

[0039] Step 1 includes: Step 1.1: The ground radar transmits electromagnetic waves and receives the target echo, calculating the UAV's position in the radar coordinate system. Epochal three-dimensional spatial position for:

[0040] In the formula, Target slant range, azimuth The elevation angle is the angle between the target and the radar's north direction in the horizontal plane. The angle between the line connecting the target and the radar and the horizontal plane.

[0041] Step 1.2: Construct a UAV motion state vector containing three-dimensional position, velocity, and acceleration. Based on continuous radar observation data, use a filtering estimation algorithm to perform state prediction and measurement update, and estimate the UAV's three-dimensional spatial velocity and acceleration in real time. The motion state of the UAV in three-dimensional space is defined as a 9-dimensional vector:

[0042] In the formula, For three-dimensional space velocity, It refers to acceleration in three-dimensional space.

[0043] Based on the current state Predicting motion state over time get: In the formula, Let be the state transition matrix.

[0044] The measurement update was performed using the current observations, resulting in:

[0045] In the formula, Kalman gain The fusion weight matrix for prediction and measurement. Let be the radar observation vector, and the observation function be... This is the projection transformation function. It filters and predicts continuous observation data to estimate the target's velocity and acceleration in real time.

[0046] Step 1.3: Set the starting state and the desired ending state for path planning, construct the objective function for path planning based on minimizing motion smoothness and terminal state deviation, and introduce the maximum speed and maximum acceleration of the UAV as dynamic constraints into the planning constraints to establish a target optimization model for generating a drive-off path that satisfies the dynamic constraints. Assume the initial state of the planned path and termination state for:

[0047] In the formula, the starting position is The initial speed is The initial acceleration is The termination position is The termination speed is The final acceleration is N is the observation epoch at the time of termination.

[0048] The objective function for constructing the planned path can be expressed as follows:

[0049] In the formula, For terminal speed coefficient, For terminal acceleration coefficient, The velocity is the speed at which the vehicle departs in the desired direction, and Δt is the time interval between adjacent epochs.

[0050] The following are the additional constraints on the maneuverability and dynamics of the UAV:

[0051] In the formula, The maximum speed of the drone, This represents the maximum acceleration of the drone.

[0052] Step 1.4: Represent the departure trajectory and its corresponding velocity and acceleration as a high-order polynomial function, combine the start and end states and constraints to perform parameterization and matrix solving on the target optimization model to obtain polynomial coefficient estimates, and substitute the polynomial coefficient estimates back into the high-order polynomial function to obtain a smooth departure path that follows the UAV's maneuverability and dynamics constraints; The smooth departure trajectory is defined as a 5th-degree polynomial:

[0053] The corresponding velocity and acceleration polynomials are:

[0054]

[0055] In the formula, the initial coefficient Determined by the initial state, respectively , , T represents time. Initial coefficient. This can be expressed as:

[0056] Define the vector of parameters to be estimated Expanding the objective function, we get:

[0057] In the formula, , , They are represented as follows:

[0058]

[0059]

[0060] Further, we obtain the system of linear equations:

[0061] The estimated value of the parameter vector to be estimated is then obtained as follows:

[0062] The smooth departure trajectory can be obtained by substituting the polynomial coefficients back.

[0063] Step 2 includes: Step 2.1: The ground monitoring system uses a ground monitoring receiver to continuously monitor the operational status parameters, space environment delay parameters, and position accuracy factors of real satellites in the target airspace to obtain basic navigation data for evaluating the target airspace positioning geometry and signal environment; The ground monitoring system continuously monitors the actual number of satellites in the airspace using receivers across the entire system and all frequency bands. Real-time track position Satellite clock bias Ionospheric delay Position accuracy factor .

[0064] Step 2.2: Based on the basic navigation data, with the combined positioning accuracy factor including pseudo-satellites in the target area meeting a preset threshold as a constraint, calculate the minimum number of pseudo-satellites required, and combine the operating status parameters of real satellites with a spatial interpolation algorithm to determine the virtual orbital position and virtual ephemeris parameters corresponding to each pseudo-satellite. Based on real satellite data With pseudo-satellite numbers To meet the requirement of optimal positioning accuracy in the region, the objective function is set as the maximum value within the target area. Value below threshold ,but The minimum value can be expressed as:

[0065] Using real satellite orbital positions Satellite clock bias Ionospheric delay Spatial interpolation Real-time orbital position of a pseudo-satellite Satellite clock bias Ionospheric delay Where n is the pseudo-satellite count, N j The minimum value of n to satisfy the PDOP condition.

[0066] Step 2.3: Based on the virtual orbital position of each pseudosatellite, the target transmission power of the pseudosatellite is calculated using the radio link transmission loss model. A closed-loop control mechanism with dynamic error zeroing is constructed by using the pseudosatellite signal reception power fed back in real time by the ground monitoring receiver. The transmission energy of the pseudosatellites is dynamically adjusted to achieve automatic decision-making on the number of pseudosatellites and real-time control to maintain a power balance state that keeps the pseudosatellite signal strength consistent with the real satellite signal strength. The ground monitoring system calculates the current real satellite signal reference power. pseudo-satellite launch power Must meet:

[0067] In the formula, For transmission distance, For frequency, For transmit and receive antenna gain, For received power, This is due to atmospheric attenuation.

[0068] Real-time transmission of pseudo-satellite signal reception power using ground monitoring receivers The error is brought to zero by using proportional-integral dynamic adjustment of numerical control attenuation.

[0069] In the formula, This is the proportional gain coefficient. is the integral gain coefficient, and k is the observation epoch.

[0070] Step 3 includes: Step 3.1: Based on the principle of baseband signal modulation, construct a mathematical model of pseudo-satellite radio frequency transmission signal, including ranging code, navigation message, code phase delay, carrier frequency and initial phase of carrier, so as to establish a control parameter framework for generating highly realistic pseudo-satellite basic signals; The signal transmitted by the pseudo-satellite is represented as:

[0071] In the formula, For the first The amplitude of the pseudo-satellite signal, It is a pseudo-random ranging code. For navigation message data codes, For code phase delay, For carrier frequency, For the initial phase of the carrier, s (i) Let T be the pseudo-satellite signal function, and T be the time.

[0072] Step 3.2: Determine the mapping relationship between code phase delay and induced pseudorange, and calculate the relative geometric distance between the target UAV and each pseudosatellite according to the planned drive-away path. Then, implement decoupling control on the launch delay of each pseudosatellite to provide independent target pseudorange observation values ​​that match the drive-away path for the target UAV. Code phase delay Estimated distance between pseudo-satellites and drones The relationship between them is:

[0073] For the Given a pseudo-satellite, the pseudorange measured by the UAV receiver should be:

[0074] In the formula, Location of the drone receiver. For the first The location of the pseudo-satellite At the speed of light, For the clock bias of the drone receiver, For pseudo-satellite clock bias, This is noise in pseudorange measurement.

[0075] Step 3.3: Set the initial carrier phase of the transmitted signal of each pseudo-satellite, and decompose the expected carrier phase observation value into a combination of integer ambiguity and fractional phase for precise quantization, so as to construct an independent carrier phase fine control parameter with spatial position sensitivity. The ground monitoring system sets the initial phase of the carrier wave for the signal transmitted by each pseudosatellite. The received carrier phase is The carrier phase observation can be expressed as:

[0076] In the formula, integer ambiguity, fractional phase .

[0077] Step 3.4: Based on wavelength relationship and constant offset compensation, establish and maintain physical consistency constraints between the target pseudorange observation value and the carrier phase fine control parameter, and control the pseudo-satellite broadcasting system to broadcast the pseudo-satellite signal to generate a pseudo-satellite signal that is highly simulated with the real satellite, and provide each pseudo-satellite with independent pseudorange and carrier phase, so that the positioning result calculated by the UAV receiver can smoothly transition along the drive-away path; Ground monitoring systems must ensure the physical consistency of pseudorange and carrier phase, i.e.

[0078] In the formula, For carrier wavelength, This is a constant offset.

[0079] By broadcasting pseudo-satellite signals, the UAV receiver can receive both real and pseudo-satellite signals, calculate the positioning results, and smoothly transition away along the driving path.

[0080] Step 4 includes: Step 4.1: Execute the control closed loop of state detection and positioning, pseudo-satellite signal generation and broadcasting in a loop, determine that the UAV positioning trajectory after smoothing and filtering has completely deviated from the geometric boundary of the no-fly zone, determine that the projection component of the UAV velocity vector on the normal of the no-fly zone boundary is negative, and that the deviation angle remains stable in multiple consecutive epochs.

[0081] Step 4.2: After determining that the UAV has completely left the geometric boundary of the no-fly zone, gradually reduce the number of pseudo-satellites broadcast to the minimum navigation requirement, and simultaneously adjust the navigation ephemeris parameters of the remaining pseudo-satellites broadcast to avoid positioning jumps in the UAV receiver due to rank deficiency in the observation equation or drastic changes in geometric configuration, until the number of broadcast pseudo-satellite signals is reduced to zero.

[0082] An experimental analysis was conducted using a pseudo-satellite signal-based method for driving away drones from no-fly zones, such as... Figure 3 As shown, a drone mistakenly enters a no-fly zone in a straight line. If no expulsion measures are taken, it will cross the no-fly zone, seriously affecting regional security. This invention proposes a method for driving away drones from no-fly zones using pseudo-satellite signals. After identifying the drone's entry into the no-fly zone, its flight status (position, speed, acceleration, etc.) is monitored in real time. Simultaneously, nine usable real satellites are detected. After generating and broadcasting 27 pseudo-satellite signals, the drone leaves the no-fly zone along a planned path. The entire expulsion process does not require cracking the drone's communication protocol or the drone's own cooperation, and has significant advantages such as strong stealth, controllable trajectory, high success rate, and low collateral impact.

[0083] This application addresses scenarios where drones enter no-fly zones due to navigational deviations or misjudgments. Traditional methods often rely on communication protocol cracking or electromagnetic interference, which suffers from inherent drawbacks such as protocol incompatibility, uncontrollable flight paths, and a high risk of crashes. This invention proposes a refined active deterrence scheme. It uses a ground-based radar system to continuously track the target drone and plan its decoy path, generating and broadcasting pseudo-satellite signals until the drone's actual position exceeds the no-fly zone boundary and its velocity vector direction deviates from the no-fly zone. This invention requires neither cracking the drone's communication protocol nor cooperation from the drone itself, and boasts significant advantages such as high stealth, controllable flight path, high deterrence success rate, and low collateral damage. It provides a highly reliable and refined solution for the counter-drone field.

[0084] Based on the same inventive concept, as an implementation of the above-mentioned adaptive dynamic rescheduling method for UAV swarms under spatial constraints, this application also provides an adaptive dynamic rescheduling system for UAV swarms under spatial constraints. Figure 4 This is a structural diagram of the system in an embodiment of this application. See also... Figure 4 As shown, the system includes: The drive-away path generation module is used to continuously detect and locate the UAV using ground radar. After calculating the target's spatial position, velocity, and acceleration in real time, it generates a drive-away path that follows the UAV's maneuverability and dynamics constraints. The control module is used by the ground monitoring system to continuously monitor the ephemeris, ionospheric delay and satellite visibility in the airspace using the full system and full frequency band receivers, and automatically decide the number of pseudo-satellites based on this, while adjusting in real time to maintain the consistency between the pseudo-satellite signal strength and the real satellite signal strength. The pseudo-satellite signal broadcasting module is used by the ground monitoring system to generate pseudo-satellite signals that are highly simulated with real satellites, and to provide each pseudo-satellite with independent pseudorange and carrier phase, and broadcast the pseudo-satellite signals so that the positioning results calculated by the UAV receiver can be smoothly transitioned along the driving path; The loop execution module is used to repeatedly execute the control closed loop of UAV status detection and positioning, pseudo-satellite signal generation and broadcasting, until the UAV's current position has exceeded the no-fly zone boundary and its velocity vector direction has deviated from the no-fly zone.

[0085] This application addresses scenarios where drones enter no-fly zones due to navigational deviations or misjudgments. Traditional methods often rely on communication protocol cracking or electromagnetic interference, which suffers from inherent drawbacks such as protocol incompatibility, uncontrollable flight paths, and a high risk of crashes. This invention proposes a refined active deterrence scheme. It uses a ground-based radar system to continuously track the target drone and plan its decoy path, generating and broadcasting pseudo-satellite signals until the drone's actual position exceeds the no-fly zone boundary and its velocity vector direction deviates from the no-fly zone. This invention requires neither cracking the drone's communication protocol nor cooperation from the drone itself, and boasts significant advantages such as high stealth, controllable flight path, high deterrence success rate, and low collateral damage. It provides a highly reliable and refined solution for the counter-drone field.

[0086] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for driving away unmanned aerial vehicles (UAVs) from no-fly zones using pseudo-satellite signals, characterized in that, include: Step 1: Continuously detect and locate the UAV using ground radar. After calculating the target's spatial position, velocity, and acceleration in real time, generate a drive-away path that follows the UAV's maneuverability and dynamic limitations. Step 2: The ground monitoring system uses full-system, full-band receivers to continuously monitor ephemeris, ionospheric delay and satellite visibility in the airspace, and automatically decides the number of pseudo-satellites based on this, while adjusting in real time to maintain the consistency between the pseudo-satellite signal strength and the real satellite signal strength. Step 3: The ground monitoring system generates pseudo-satellite signals that are highly similar to real satellites, and provides each pseudo-range and carrier phase with independent pseudo-range and carrier phase. The pseudo-satellite signals are broadcast so that the positioning results calculated by the UAV receiver can be smoothly transitioned along the drive-off path. Step 4: Repeatedly execute the closed-loop control for UAV status detection and positioning, pseudo-satellite signal generation and broadcasting until the UAV's current position has exceeded the no-fly zone boundary and its velocity vector direction is away from the no-fly zone.

2. The method for driving away unmanned aerial vehicles (UAVs) from no-fly zones using pseudo-satellite signals according to claim 1, characterized in that, Step 1 includes: Step 1.1: The ground radar transmits electromagnetic waves and receives the target echo, calculating the UAV's position in the radar coordinate system. Epochal three-dimensional spatial position for: ； In the formula, Target slant range, azimuth The elevation angle is the angle between the target and the radar's north direction in the horizontal plane. The angle between the line connecting the target and the radar and the horizontal plane; Step 1.2: Construct a UAV motion state vector containing three-dimensional position, velocity, and acceleration. Based on continuous radar observation data, use a filtering estimation algorithm to perform state prediction and measurement update, and estimate the UAV's three-dimensional spatial velocity and acceleration in real time. The motion state of the UAV in three-dimensional space is defined as a 9-dimensional vector: ； In the formula, For three-dimensional space velocity, Acceleration in three-dimensional space; Based on the current state Predicting motion state over time get: In the formula, This is the state transition matrix; The measurement update was performed using the current observations, resulting in: ； In the formula, Kalman gain The fusion weight matrix for prediction and measurement. Let be the radar observation vector, and the observation function be... This is the projection transformation function. It filters and predicts continuously observed data to estimate the target's velocity and acceleration in real time. Step 1.3: Set the starting state and the desired ending state for path planning, construct the objective function for path planning based on minimizing motion smoothness and terminal state deviation, and introduce the maximum speed and maximum acceleration of the UAV as dynamic constraints into the planning constraints to establish a target optimization model for generating a drive-off path that satisfies the dynamic constraints. Assume the initial state of the planned path and termination state for: ； In the formula, the starting position is The initial speed is The initial acceleration is The termination position is The termination speed is The final acceleration is N is the observation epoch at the time of termination; The objective function for constructing the planned path can be expressed as follows: ； In the formula, For terminal speed coefficient, For terminal acceleration coefficient, The velocity is the speed at which the vehicle departs in the desired direction, and Δt is the time interval between adjacent epochs. The following are the additional constraints on the maneuverability and dynamics of the UAV: ； In the formula, The maximum speed of the drone, This represents the maximum acceleration of the drone. Step 1.4: Represent the departure trajectory and its corresponding velocity and acceleration as a high-order polynomial function, combine the start and end states and constraints to perform parameterization and matrix solving on the target optimization model to obtain polynomial coefficient estimates, and substitute the polynomial coefficient estimates back into the high-order polynomial function to obtain a smooth departure path that follows the UAV's maneuverability and dynamics constraints; The smooth departure trajectory is defined as a 5th-degree polynomial: ； The corresponding velocity and acceleration polynomials are: ； ； In the formula, the initial coefficient Determined by the initial state, respectively , , T is time; initial coefficient This can be expressed as: ； Define the vector of parameters to be estimated Expanding the objective function, we get: ； In the formula, , , They are represented as: ; ； ； ； Further, we obtain the system of linear equations: ； The estimated value of the parameter vector to be estimated is then obtained as follows: ； The smooth departure trajectory can be obtained by substituting the polynomial coefficients back.

3. The method for driving away unmanned aerial vehicles (UAVs) from no-fly zones using pseudo-satellite signals according to claim 1, characterized in that, Step 2 specifically involves: Step 2.1: The ground monitoring system uses a ground monitoring receiver to continuously monitor the operational status parameters, space environment delay parameters, and position accuracy factors of real satellites in the target airspace in order to obtain basic navigation data for evaluating the target airspace positioning geometry and signal environment; The ground monitoring system continuously monitors the actual number of satellites in the airspace using receivers across the entire system and all frequency bands. Real-time track position Satellite clock bias Ionospheric delay Position accuracy factor ; Step 2.2: Based on the basic navigation data, with the combined positioning accuracy factor including pseudo-satellites in the target area meeting a preset threshold as a constraint, calculate the minimum number of pseudo-satellites required, and combine the operating status parameters of real satellites with a spatial interpolation algorithm to determine the virtual orbital position and virtual ephemeris parameters corresponding to each pseudo-satellite. Based on real satellite data With pseudo-satellite numbers To meet the requirement of optimal positioning accuracy in the region, the objective function is set as the maximum value within the target area. Value below threshold ,but The minimum value can be expressed as: ； Using real satellite orbital positions Satellite clock bias Ionospheric delay Spatial interpolation Real-time orbital position of a pseudo-satellite Satellite clock bias Ionospheric delay Where n is the pseudo-satellite count, N j The minimum value of n to satisfy the PDOP condition; Step 2.3: Based on the virtual orbital position of each pseudosatellite, the target transmission power of the pseudosatellite is calculated using the radio link transmission loss model. A closed-loop control mechanism with dynamic error zeroing is constructed by using the pseudosatellite signal reception power fed back in real time by the ground monitoring receiver. The transmission energy of the pseudosatellites is dynamically adjusted to achieve automatic decision-making on the number of pseudosatellites and real-time control to maintain a power balance state that keeps the pseudosatellite signal strength consistent with the real satellite signal strength. The ground monitoring system calculates the current real satellite signal reference power. pseudo-satellite launch power Must meet: ； In the formula, For transmission distance, For frequency, For transmit and receive antenna gain, For received power, Atmospheric attenuation; Real-time transmission of pseudo-satellite signal reception power using ground monitoring receivers The error is brought to zero by using proportional-integral dynamic adjustment of numerical control attenuation. ； In the formula, This is the proportional gain coefficient. is the integral gain coefficient, and k is the observation epoch.

4. The method for driving away unmanned aerial vehicles (UAVs) from no-fly zones using pseudo-satellite signals according to claim 1, characterized in that, Step 3 specifically involves: Step 3.1: Based on the principle of baseband signal modulation, construct a mathematical model of pseudo-satellite radio frequency transmission signal, including ranging code, navigation message, code phase delay, carrier frequency and initial phase of carrier, so as to establish a control parameter framework for generating highly realistic pseudo-satellite basic signals; The signal transmitted by the pseudo-satellite is represented as: ； In the formula, For the first The amplitude of the pseudo-satellite signal, It is a pseudo-random ranging code. For navigation message data codes, For code phase delay, For carrier frequency, For the initial phase of the carrier, s (i) Let T be the pseudo-satellite signal function, and T be time. Step 3.2: Determine the mapping relationship between code phase delay and induced pseudorange, and calculate the relative geometric distance between the target UAV and each pseudosatellite according to the planned drive-away path. Then, implement decoupling control on the launch delay of each pseudosatellite to provide independent target pseudorange observation values ​​that match the drive-away path for the target UAV. Code phase delay Estimated distance between pseudo-satellites and drones The relationship between them is: ； For the Given a pseudosatellite, the pseudorange measured by the UAV receiver should be: ； In the formula, Location of the drone receiver. For the first The location of the pseudo-satellite At the speed of light, For the drone receiver clock bias, For pseudo-satellite clock bias, This is noise in pseudorange measurements; Step 3.3: Set the initial carrier phase of the transmitted signal of each pseudo-satellite, and decompose the expected carrier phase observation value into a combination of integer ambiguity and fractional phase for precise quantization, so as to construct an independent carrier phase fine control parameter with spatial position sensitivity. The ground monitoring system sets the initial phase of the carrier wave for the signal transmitted by each pseudosatellite. The received carrier phase is Carrier phase observations It can be represented as: ； In the formula, integer ambiguity, fractional phase ; Step 3.4: Based on wavelength relationship and constant offset compensation, establish and maintain physical consistency constraints between the target pseudorange observation value and the carrier phase fine control parameter, and control the pseudo-satellite broadcasting system to broadcast the pseudo-satellite signal to generate a pseudo-satellite signal that is highly simulated with the real satellite, and provide each pseudo-satellite with independent pseudorange and carrier phase, so that the positioning result calculated by the UAV receiver can smoothly transition along the drive-away path; Ground monitoring systems must ensure the physical consistency of pseudorange and carrier phase, i.e. ； In the formula, For carrier wavelength, This is a constant offset. By broadcasting pseudo-satellite signals, the UAV receiver can receive both real and pseudo-satellite signals, calculate the positioning results, and smoothly transition away along the driving path.

5. The method for driving away unmanned aerial vehicles (UAVs) from no-fly zones using pseudo-satellite signals according to claim 1, characterized in that, Step 4 specifically involves: Step 4.1: Execute the control closed loop of state detection and positioning, pseudo-satellite signal generation and broadcasting in a loop, determine that the UAV positioning trajectory after smoothing and filtering has completely deviated from the geometric boundary of the no-fly zone, determine that the projection component of the UAV velocity vector on the normal of the no-fly zone boundary is negative, and that the deviation angle remains stable in multiple consecutive epochs; Step 4.2: After determining that the UAV has completely left the geometric boundary of the no-fly zone, gradually reduce the number of pseudo-satellites broadcast to the minimum navigation requirement, and simultaneously adjust the navigation ephemeris parameters of the remaining pseudo-satellites broadcast to avoid positioning jumps in the UAV receiver due to rank deficiency in the observation equation or drastic changes in geometric configuration, until the number of broadcast pseudo-satellite signals is reduced to zero.

6. A pseudo-satellite signal-based drone no-fly zone deterrence system, characterized in that, include: The drive-away path generation module is used to continuously detect and locate the UAV using ground radar. After calculating the target's spatial position, velocity, and acceleration in real time, it generates a drive-away path that follows the UAV's maneuverability and dynamics constraints. The control module is used by the ground monitoring system to continuously monitor the ephemeris, ionospheric delay and satellite visibility in the airspace using the full system and full frequency band receivers, and automatically decide the number of pseudo-satellites based on this, while adjusting in real time to maintain the consistency between the pseudo-satellite signal strength and the real satellite signal strength. The pseudo-satellite signal broadcasting module is used by the ground monitoring system to generate pseudo-satellite signals that are highly simulated with real satellites, and to provide each pseudo-satellite with independent pseudorange and carrier phase, and broadcast the pseudo-satellite signals so that the positioning results calculated by the UAV receiver can be smoothly transitioned along the driving path; The loop execution module is used to repeatedly execute the control closed loop of UAV status detection and positioning, pseudo-satellite signal generation and broadcasting, until the UAV's current position has exceeded the no-fly zone boundary and its velocity vector direction has deviated from the no-fly zone.

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