A method for trapping drones by transmitting navigation deception signals

Abstract

This invention relates to the field of drone trapping technology, and discloses a method for trapping drones by transmitting navigation deception signals, comprising: step 1, generating a navigation deception signal synchronized with the time reference of a satellite navigation system through an atomic clock synchronization mechanism; step 2, injecting dynamically predicted satellite orbit parameters based on the navigation deception signal; step 3, implementing pseudo-arbitrary frequency hopping control on the subcarrier of the injected navigation deception signal; and step 4, collecting and fusing multi-source data from radar and optical sensors to generate the drone's trajectory. This invention uses an atomic clock synchronization mechanism to generate a high-precision time reference, combined with dynamic orbit prediction and pseudo-random frequency hopping control techniques. Through atomic clocks, it achieves time synchronization with the satellite system; long short-term memory networks dynamically correct satellite orbit parameters; and rapid frequency hopping modulation of the subcarrier band, achieving high spatiotemporal consistency of the deception signal and dynamic spectral camouflage.

Description

Technical Field

[0001] This invention relates to the field of drone trapping technology, specifically a method for trapping drones by transmitting navigation deception signals. Background Technology

[0002] The rapid development of drone technology has led to the increasing prevalence of civilian and commercial drones based on satellite navigation systems in aerial photography and logistics. However, this also brings security risks such as illegal intrusion. Among countermeasures against drones, navigation signal spoofing, due to its ability to remotely and covertly interfere with the drone's flight path, has become a research hotspot in the field of active defense. Existing technologies mainly generate false satellite navigation signals to inject incorrect positioning information into the target drone, forcing it to deviate from its planned route or land. However, these technologies have significant shortcomings in terms of stealth, environmental adaptability, and reliability of trapping, specifically manifested in the following core issues:

[0003] Traditional methods use GPS-disciplined crystal oscillators to generate a time reference, with clock synchronization accuracy limited to the microsecond level, resulting in accumulated pseudorange errors over time. If the pseudorange deviation between the spoofing signal and the real satellite signal exceeds the detection threshold of the receiver's autonomous integrity monitoring algorithm, an alarm mechanism is easily triggered, causing the spoofing to fail. Furthermore, existing technologies rely on fixed ephemeris parameters to generate spoofing signals, failing to dynamically match the perturbations of real satellite orbits. This leads to inaccuracies in the spatial geometric relationship between the spoofing signal and the real constellation, further exacerbating the RAIM detection risk. In addition, the continuous transmission mode in a single frequency band gives the spoofing signal fixed characteristics in the spectral domain, making it easily detectable by enemy spectrum sensing equipment through energy accumulation, with an exposure probability exceeding 80%.

[0004] In densely built-up urban environments, drones often employ multipath suppression algorithms to filter navigation signals. However, current technologies lack the capability for high-precision target trajectory reconstruction, making it impossible to generate deception signals that match the real environment. Single-sensor positioning is affected by obstruction and reflection interference, resulting in position errors exceeding 3 meters. This leads to spatial pointing deviations in the deception signal, failing to cover the effective acquisition range of the target receiver. Furthermore, the omnidirectional antenna broadcast transmission mode causes signal energy dispersion, resulting in a signal-to-noise ratio below 5 dB. In environments with strong electromagnetic interference, the signal is easily submerged by noise, leading to a decoy success rate of less than 40%.

[0005] To address these issues, this invention proposes a method for trapping unmanned aerial vehicles (UAVs) by transmitting navigation deception signals. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method for trapping unmanned aerial vehicles (UAVs) by transmitting navigation deception signals, thereby solving the problems mentioned in the background section.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for trapping unmanned aerial vehicles (UAVs) by transmitting navigation deception signals, comprising:

[0008] Step 1: Generate a navigation deception signal synchronized with the time reference of the satellite navigation system through an atomic clock synchronization mechanism;

[0009] Step 2: Inject dynamically predicted satellite orbit parameters based on navigation deception signals;

[0010] Step 3: Implement subcarrier pseudo-arbitrary frequency hopping control on the navigation deception signal with injected parameters;

[0011] Step 4: Collect multi-source data from radar and optical sensors and fuse them to generate the UAV's trajectory;

[0012] Step 5: Adjust the frequency hopping control parameters according to the drone's movement trajectory;

[0013] Step 6: Transmit the adjusted navigation deception signal through a distributed phased array antenna array;

[0014] Step 7: Dynamically optimize orbit prediction parameters based on transmitted signal feedback data.

[0015] Preferably, in step 1, generating a navigation deception signal synchronized with the satellite navigation system's time reference via an atomic clock synchronization mechanism further includes:

[0016] Sub-step 1.1: Receive the 1PPS time signal broadcast by the satellite navigation system via a precise time protocol, and calculate the time deviation of the local atomic clock:

[0017]

[0018] Where Δt is the time synchronization deviation between the atomic clock and the satellite navigation system. This is the 1PPS timestamp broadcast by the satellite system during the i-th sampling. Let be the timestamp recorded by the local atomic clock during the i-th sampling, N be the number of synchronous samplings, and σ be the standard deviation of the measurement noise.

[0019] Sub-step 1.2: Drive the voltage-controlled crystal oscillator to calibrate the output frequency of the rubidium atomic clock, satisfying the frequency stability constraint.

[0020]

[0021] Where f0 is the nominal frequency, f out h represents the actual output frequency of the atomic clock. -1 σ is the noise figure, τ is the averaging time, and σ is the noise figure. y (·) represents the Allan variance, h0 represents the flicker noise coefficient of the frequency noise, and h1 represents the frequency arbitrary wander coefficient of the frequency noise.

[0022] Sub-step 1.3: Generate a navigation spoofing signal baseband synchronized with the satellite system time reference.

[0023] s spoof (t)=A·C / A(t-Δt)·sin(2πf Ll t+φ0),

[0024] Where A is the signal amplitude, C / A(·) is the Gold code pseudo-arbitrary sequence, and f Ll φ0 is the GPS carrier frequency, φ0 is the initial phase, and t is the time variable, s. spoof (t) represents the baseband navigation deception signal, and π is the constant pi.

[0025] Preferably, in step 2, injecting dynamically predicted satellite orbit parameters based on navigation spoofing signals further includes:

[0026] Sub-step 2.1: Receive the navigation spoofing signal baseband generated in step 1.3, obtain the current satellite ephemeris data, and construct the orbital parameter vector:

[0027]

[0028] Among them, X t Let x be the satellite orbital state vector. sat y sat z sat The satellite's position in the ECEF coordinate system. The satellite's velocity;

[0029] Sub-step 2.2: Predict future satellite orbital parameters in the time domain using a long short-term memory network.

[0030]

[0031] Among them, X t-n:t W is a sequence of orbital parameter vectors at historical time n. lstm Here is the pre-trained network weight matrix, and Δh is the predicted future time length. The satellite orbital parameters are for the predicted future time, where t is the time variable;

[0032] Sub-step 2.3 involves injecting the predicted orbit parameters into the navigation spoofing signal baseband to correct the pseudorange calculation values:

[0033]

[0034] Where, ρ true (t) represents the true pseudorange, Δρ(t) represents the pseudorange correction, and T lnjThe parameter update period is denoted by ρ, rect(·) is a rectangular window function, t0 is the parameter injection start time, and ρ is the parameter update period. spoof (t) represents the pseudorange of the deception signal, where t is the time variable.

[0035] Preferably, in step 3, implementing subcarrier pseudo-arbitrary frequency hopping control on the navigation spoofing signal of the injected parameters further includes:

[0036] Sub-step 3.1: Generate a frequency hopping sequence based on the pseudorange correction calculated in step 2.3, and determine the frequency hopping interval and frequency set.

[0037]

[0038] Among them, T hop F is the frequency hopping time slot width. set For the set of frequency hopping frequencies, Δf max For maximum frequency offset, v max The maximum speed of the drone is given by c, the speed of light is given by f0, and the nominal frequency is given by Δf. step δf is the frequency hopping step size. var Let K be any frequency offset, and K be the total number of frequency hopping points;

[0039] Sub-step 3.2: Generate the time-domain waveform of the navigation spoofing signal under frequency hopping control:

[0040]

[0041] Among them, s hop (t) represents the time-domain waveform of the navigation deception signal after frequency hopping modulation, s spoof (t) represents the baseband navigation spoofing signal, f n Let be the frequency hopping carrier frequency of the nth time slot, rect(·) be the rectangular window function, and M be the number of frequency hopping cycles in a single parameter injection period;

[0042] Sub-step 3.3: Optimize frequency hopping parameters in real time based on the drone's motion trajectory from step 4.

[0043]

[0044] Where, Δf′ step v is the dynamically adjusted frequency hopping step size. uav Let ρ(t) be the velocity vector of the UAV, Δρ(t) be the pseudorange correction, and ||v uav || represents the magnitude of the UAV's velocity vector, δf′ var It is designed to adapt to arbitrary frequency offset.

[0045] Preferably, step 4, which involves collecting multi-source data from radar and optical sensors and fusing them to generate the UAV's trajectory, further includes:

[0046] Sub-step 4.1 involves performing spatiotemporal synchronization between radar point cloud data and optical image data, and calculating the compensation amount for the deviation between the sensor timestamp and the atomic clock time.

[0047]

[0048] Where, Δt sync This is the compensation amount for the time synchronization deviation between the radar and the optical sensor. This is the timestamp of the radar sensor during the m-th sampling. This is the timestamp of the optical sensor at the m-th sampling. This represents the target position in the radar polar coordinate system. Here, c represents the target position in the optical image coordinate system, and B represents the speed of light.

[0049] Sub-step 4.2: Convert the radar polar coordinate system data to the WGS84 geographic coordinate system:

[0050] p WGS84 =R z (θ)·S·p radar +T,

[0051] Where, p WGS84 R represents the position of the target in the WGS84 coordinate system. z (θ) is the rotation matrix about the Z-axis, θ is the heading angle, S is the radar sensor scale factor matrix, and p radar The data consists of the original polar coordinates of the radar, where T is the translation vector.

[0052] Sub-step 4.3: Fuse multi-source data to generate the UAV's six-degree-of-freedom motion trajectory:

[0053]

[0054] in, Let F be the UAV state estimation vector at time k. k Let K be the state transition matrix. k Let z be the Kalman gain matrix. k H is the observation vector. k For the observation matrix, This is the state estimation vector of the UAV at time k-1.

[0055] Preferably, step 5, adjusting the frequency hopping control parameters according to the UAV's motion trajectory, further includes:

[0056] Sub-step 5.1: Based on the UAV trajectory generated in step 4.3, extract the velocity vector and acceleration vector:

[0057]

[0058]

[0059] in, Let v be the three-dimensional velocity components of the UAV at time k, Δz be the trajectory update period, and v k Let v be the three-dimensional velocity vector of the UAV at time k. k-1 Let a be the three-dimensional velocity vector of the UAV at time k-1. uav Let v be the three-dimensional acceleration vector of the UAV. uav The velocity vector of the UAV;

[0060] Sub-step 5.2: Calculate the Doppler frequency shift compensation amount caused by motion:

[0061]

[0062] Where f0 is the nominal frequency, e sat v is the unit vector in the direction of the satellite's line of sight. sat e represents the satellite's velocity. uav Let Δf be the unit vector representing the direction of motion of the UAV. doppler This is the Doppler frequency shift compensation amount;

[0063] Sub-step 5.3: Dynamically adjust the frequency hopping parameter set from step 3.1:

[0064]

[0065] δf′ var =δf var +Δf doppler ,

[0066] Among them, a max Δf is the maximum acceleration threshold for the drone. step Let δf be the initial frequency hopping step size. var Let Δf′ be an initial arbitrary frequency offset. step For adaptive frequency hopping step size, δf′ var It is a composite arbitrary frequency offset.

[0067] Preferably, step 6, transmitting the adjusted navigation deception signal through a distributed phased array antenna array, further includes:

[0068] Sub-step 6.1: Calculate beamforming weights based on the frequency hopping parameters adjusted in step 5.3 to maximize the signal-to-interference-plus-noise ratio (SIR) of the target area.

[0069]

[0070] Among them, w * Let w be the optimal beamforming weight vector, H be the target channel matrix, and R be the beamforming weight vector.n H is the noise covariance matrix. j Let w be the channel matrix of the interference source. H This is the conjugate transpose of the weight vector;

[0071] Sub-step 6.2: Synchronize the carrier phase of the distributed nodes using a reference signal:

[0072]

[0073] in, For the synchronization phase of the k-th node, φ ref As the reference phase standard, f0 is the nominal frequency. Let be the propagation delay from node k to the drone, ∈ PLL This refers to the tracking error of the phase-locked loop;

[0074] Sub-step 6.3: Dynamically allocate the transmit power of each node and synthesize the radiated signal:

[0075]

[0076] Among them, P k Dynamically allocate power to the k-th node, P max Where η is the maximum allowable transmit power for a single node, h is the power allocation efficiency factor, and η is the maximum allowable transmit power for a single node. k Let k be the channel response vector of the k-th node. For noise power, I env For environmental interference power, P total Budget for total transmit power.

[0077] Preferably, step 7, which involves dynamically optimizing the orbit prediction parameters based on the transmitted signal feedback data, further includes:

[0078] Sub-step 7.1: Collect the transmit power data and received signal bit error rate allocated in step 6.3, and calculate the orbit prediction error evaluation index:

[0079]

[0080] Among them, J(W lstm ) is the loss function. Let σ be the actual satellite orbit parameters at the k-th sampling time. pos P is the position error tolerance threshold. total For the total transmit power budget, P k Dynamically allocate power to the k-th node, P max Let be the maximum allowable transmit power for a single node, and λ be the weighting factor for the power constraint term. The predicted satellite orbit parameters are given at the k-th sampling time.

[0081] Sub-step 7.2 updates the LSTM network weights from step 2.2 using the backpropagation algorithm:

[0082]

[0083] Among them, W′ lstm For the updated LSTM network weight matrix, W lstm Here, η is the weight matrix of the pre-trained network, and η is the power allocation efficiency factor. This represents the gradient of the loss function with respect to the weights.

[0084] Sub-step 7.3: Reconstruct the dynamic prediction model and update the environmental feature library:

[0085]

[0086] Among them, D env For environmental feature database, X t The true values ​​of the satellite orbital parameters at the current moment. P represents the future orbital parameters predicted by LSTM. k Power is dynamically allocated to the k-th node, and BER is the bit error rate threshold.

[0087] A terminal device comprising a programmable radio frequency front-end module, a multi-core digital signal processor, and a phased array antenna array, for executing the aforementioned method for trapping unmanned aerial vehicles by transmitting navigation deception signals.

[0088] A storage medium storing computer-executable instructions, which, when executed by a processor, implement the aforementioned method for trapping unmanned aerial vehicles (UAVs) that transmit navigation deception signals.

[0089] This invention provides a method for trapping unmanned aerial vehicles (UAVs) by transmitting navigation deception signals. It has the following beneficial effects:

[0090] 1. This invention uses an atomic clock synchronization mechanism to generate a high-precision time reference, combined with a technical solution of dynamic orbit prediction and pseudo-random frequency hopping control. Through atomic clocks, it achieves time synchronization with the satellite system, dynamic correction of satellite orbit parameters by long short-term memory networks, and rapid hopping modulation of subcarrier frequency bands. This achieves the technical effect of high spatiotemporal consistency of deception signals and dynamic spectral camouflage. Compared with the existing technology that relies on fixed ephemeris parameters and single-band transmission, this invention solves the core defects of traditional methods, which are easily detected by receiver autonomous integrity monitoring and spectrum sensing equipment due to signal time accumulation errors and fixed spectral characteristics. It significantly reduces the risk of deception signal exposure and achieves a breakthrough in concealment.

[0091] 2. This invention uses multi-source sensor data fusion to generate UAV trajectories, combined with frequency hopping parameter dynamic optimization and distributed phased array collaborative transmission technology. Through complementary correction of radar and optical data, real-time compensation of Doppler frequency shift, and spatial energy directional focusing of beamforming, it achieves the technical effect of accurate reconstruction of UAV motion trajectory and efficient coverage of deception signals in complex multipath environments. Compared with the problems of insufficient positioning accuracy of single sensors and omnidirectional antenna signal dispersion in existing technologies, it solves the problem of capture failure caused by signal attenuation and target loss in urban canyons and strong electromagnetic interference scenarios, and significantly improves the stable capture capability of UAVs in dynamic environments. Attached Figure Description

[0092] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0093] To enable those skilled in the art to understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.

[0094] The present invention will now be described in detail with reference to the accompanying drawings:

[0095] Example:

[0096] Please see the appendix Figure 1 This invention provides a method for trapping unmanned aerial vehicles (UAVs) that emit navigation deception signals, comprising:

[0097] Step 1: Generate a navigation deception signal synchronized with the time reference of the satellite navigation system through an atomic clock synchronization mechanism;

[0098] Sub-step 1.1: Receive the 1PPS time signal broadcast by the satellite navigation system via a precise time protocol, and calculate the time deviation of the local atomic clock:

[0099]

[0100] Where Δt is the time synchronization deviation between the atomic clock and the satellite navigation system. This is the 1PPS timestamp broadcast by the satellite system during the i-th sampling. Let be the timestamp recorded by the local atomic clock during the i-th sampling, N be the number of synchronous samplings, and σ be the standard deviation of the measurement noise.

[0101] Sub-step 1.2: Drive the voltage-controlled crystal oscillator to calibrate the output frequency of the rubidium atomic clock, satisfying the frequency stability constraint.

[0102]

[0103]

[0104] Where f0 is the nominal frequency, f out h represents the actual output frequency of the atomic clock. -1 σ is the noise figure, τ is the averaging time, and σ is the noise figure. y (·) represents the Allan variance, h0 represents the flicker noise coefficient of the frequency noise, and h1 represents the frequency arbitrary wander coefficient of the frequency noise.

[0105] Sub-step 1.3: Generate a navigation spoofing signal baseband synchronized with the satellite system time reference.

[0106] s spoof (t)=A·C / A(t-Δt)·sin(2πf Ll t+φ0),

[0107] Where A is the signal amplitude, C / A(·) is the Gold code pseudo-arbitrary sequence, and f Ll φ0 is the GPS carrier frequency, φ0 is the initial phase, and t is the time variable, s. spoof (t) represents the baseband navigation deception signal, and π is the constant pi.

[0108] Step 2: Inject dynamically predicted satellite orbit parameters based on navigation deception signals;

[0109] Sub-step 2.1: Receive the navigation spoofing signal baseband generated in step 1.3, obtain the current satellite ephemeris data, and construct the orbital parameter vector:

[0110]

[0111] Among them, X t Let x be the satellite orbital state vector. sat y sat z sat The satellite's position in the ECEF coordinate system. The satellite's velocity;

[0112] Sub-step 2.2: Predict future satellite orbital parameters in the time domain using a long short-term memory network.

[0113]

[0114] Among them, X t-n:t W is a sequence of orbital parameter vectors at historical time n. lstm Here is the pre-trained network weight matrix, and Δh is the predicted future time length. The satellite orbital parameters are for the predicted future time, where t is the time variable;

[0115] Sub-step 2.3 involves injecting the predicted orbit parameters into the navigation spoofing signal baseband to correct the pseudorange calculation values:

[0116]

[0117] Where, ρ true (t) represents the true pseudorange, Δρ(t) represents the pseudorange correction, and T lnj The parameter update period is denoted by ρ, rect(·) is a rectangular window function, t0 is the parameter injection start time, and ρ is the parameter update period. spoof (t) represents the pseudorange of the deception signal, where t is a time variable;

[0118] Step 3: Implement subcarrier pseudo-arbitrary frequency hopping control on the navigation deception signal with injected parameters;

[0119] Sub-step 3.1: Generate a frequency hopping sequence based on the pseudorange correction calculated in step 2.3, and determine the frequency hopping interval and frequency set.

[0120]

[0121] Among them, T hop F is the frequency hopping time slot width. set For the set of frequency hopping frequencies, Δf max For maximum frequency offset, v max The maximum speed of the drone is given by c, the speed of light is given by f0, and the nominal frequency is given by Δf. step δf is the frequency hopping step size. var Let K be any frequency offset, and K be the total number of frequency hopping points;

[0122] Sub-step 3.2: Generate the time-domain waveform of the navigation spoofing signal under frequency hopping control:

[0123]

[0124] Among them, s hop (t) represents the time-domain waveform of the navigation deception signal after frequency hopping modulation, s spoof (t) represents the baseband navigation spoofing signal, f n Let be the frequency hopping carrier frequency of the nth time slot, rect(·) be the rectangular window function, and M be the number of frequency hopping cycles in a single parameter injection period;

[0125] Sub-step 3.3: Optimize frequency hopping parameters in real time based on the drone's motion trajectory from step 4.

[0126]

[0127] Where, Δf′ step v is the dynamically adjusted frequency hopping step size. uavLet ρ(t) be the velocity vector of the UAV, Δρ(t) be the pseudorange correction, and ||v uav || represents the magnitude of the UAV's velocity vector, δf′ var To adapt to arbitrary frequency offset;

[0128] Step 4: Collect multi-source data from radar and optical sensors and fuse them to generate the UAV's trajectory;

[0129] Sub-step 4.1 involves performing spatiotemporal synchronization between radar point cloud data and optical image data, and calculating the compensation amount for the deviation between the sensor timestamp and the atomic clock time.

[0130]

[0131] Where, Δt sync This is the compensation amount for the time synchronization deviation between the radar and the optical sensor. This is the timestamp of the radar sensor during the m-th sampling. This is the timestamp of the optical sensor at the m-th sampling. This represents the target position in the radar polar coordinate system. Here, c represents the target position in the optical image coordinate system, and B represents the speed of light.

[0132] Sub-step 4.2: Convert the radar polar coordinate system data to the WGS84 geographic coordinate system:

[0133] p WGS84 =R z (θ)·S·p radar +T,

[0134] Where, p WGS84 R represents the position of the target in the WGS84 coordinate system. z (θ) is the rotation matrix about the Z-axis, θ is the heading angle, S is the radar sensor scale factor matrix, and p radar The data consists of the original polar coordinates of the radar, where T is the translation vector.

[0135] Sub-step 4.3: Fuse multi-source data to generate the UAV's six-degree-of-freedom motion trajectory:

[0136]

[0137] in, Let F be the UAV state estimation vector at time k. k Let K be the state transition matrix. k Let z be the Kalman gain matrix. k H is the observation vector. k For the observation matrix, This is the UAV state estimation vector at time k-1;

[0138] Step 5: Adjust the frequency hopping control parameters according to the drone's movement trajectory;

[0139] Sub-step 5.1: Based on the UAV trajectory generated in step 4.3, extract the velocity vector and acceleration vector:

[0140]

[0141]

[0142] in, Let v be the three-dimensional velocity components of the UAV at time k, Δz be the trajectory update period, and v k Let v be the three-dimensional velocity vector of the UAV at time k. k-1 Let a be the three-dimensional velocity vector of the UAV at time k-1. uav Let v be the three-dimensional acceleration vector of the UAV. uav The velocity vector of the UAV;

[0143] Sub-step 5.2: Calculate the Doppler frequency shift compensation amount caused by motion:

[0144]

[0145] Where f0 is the nominal frequency, e sat v is the unit vector in the direction of the satellite's line of sight. sat e represents the satellite's velocity. uav Let Δf be the unit vector representing the direction of motion of the UAV. doppler This is the Doppler frequency shift compensation amount;

[0146] Sub-step 5.3: Dynamically adjust the frequency hopping parameter set from step 3.1:

[0147]

[0148] δf′ var =δf var +Δf doppler ,

[0149] Among them, a max Δf is the maximum acceleration threshold for the drone. step Let δf be the initial frequency hopping step size. var Let Δf′ be an initial arbitrary frequency offset. step For adaptive frequency hopping step size, δf′ var It is a composite arbitrary frequency offset;

[0150] Step 6: Transmit the adjusted navigation deception signal through a distributed phased array antenna array;

[0151] Sub-step 6.1: Calculate beamforming weights based on the frequency hopping parameters adjusted in step 5.3 to maximize the signal-to-interference-plus-noise ratio (SIR) of the target area.

[0152]

[0153] Among them, w * Let w be the optimal beamforming weight vector, H be the target channel matrix, and R be the beamforming weight vector. n H is the noise covariance matrix. j Let w be the channel matrix of the interference source. H This is the conjugate transpose of the weight vector;

[0154] Sub-step 6.2: Synchronize the carrier phase of the distributed nodes using a reference signal:

[0155]

[0156] in, For the synchronization phase of the k-th node, φ ref As the reference phase standard, f0 is the nominal frequency. Let be the propagation delay from node k to the drone, ∈ PLL This refers to the tracking error of the phase-locked loop;

[0157] Sub-step 6.3: Dynamically allocate the transmit power of each node and synthesize the radiated signal:

[0158]

[0159] Among them, P k Dynamically allocate power to the k-th node, P max Where η is the maximum allowable transmit power for a single node, h is the power allocation efficiency factor, and η is the maximum allowable transmit power for a single node. k Let k be the channel response vector of the k-th node. For noise power, I env For environmental interference power, P total Budget for total transmit power;

[0160] Step 7: Dynamically optimize orbit prediction parameters based on transmitted signal feedback data;

[0161] Sub-step 7.1: Collect the transmit power data and received signal bit error rate allocated in step 6.3, and calculate the orbit prediction error evaluation index:

[0162]

[0163] Among them, J(W lstm ) is the loss function. Let σ be the actual satellite orbit parameters at the k-th sampling time. pos P is the position error tolerance threshold.total For the total transmit power budget, P k Dynamically allocate power to the k-th node, P max Let be the maximum allowable transmit power for a single node, and λ be the weighting factor for the power constraint term. The predicted satellite orbit parameters are given at the k-th sampling time.

[0164] Sub-step 7.2 updates the LSTM network weights from step 2.2 using the backpropagation algorithm:

[0165]

[0166] Among them, W′ lstm For the updated LSTM network weight matrix, W lstm Here, η is the weight matrix of the pre-trained network, and η is the power allocation efficiency factor. This represents the gradient of the loss function with respect to the weights.

[0167] Sub-step 7.3: Reconstruct the dynamic prediction model and update the environmental feature library:

[0168]

[0169] Among them, D env For environmental feature database, X t The true values ​​of the satellite orbital parameters at the current moment. P represents the future orbital parameters predicted by LSTM. k Power is dynamically allocated to the k-th node, and BER is the bit error rate threshold.

[0170] Step 1 synchronizes the atomic clock with the time reference of the satellite navigation system, and combines this with dynamic calibration of the voltage-controlled crystal oscillator to generate a high-precision navigation deception signal baseband with a time synchronization error of less than 10ns. The core advantages lie in the long-term stability of the atomic clock, which suppresses the clock drift of traditional GPS disciplined crystal oscillators and avoids triggering receiver RAIM alarms due to accumulated pseudorange errors; real-time synchronization of the satellite 1PPS signal via a precise time protocol ensures that the code phase of the deception signal is aligned with the real satellite signal, preventing the receiver from detecting the deception source through chip deviation; and the combination of the Gold code pseudo-random sequence and the atomic clock frequency source generates a baseband signal with low cross-correlation characteristics, reducing the probability that the enemy will detect and identify the deception signal through correlation peaks.

[0171] Step 2 utilizes an LSTM network to dynamically predict satellite orbital parameters and injects navigation spoofing signals in real time. The technical value lies in the fact that the LSTM network learns satellite perturbation patterns from historical orbital data, and the mean square error of predicting future orbital parameters is <0.3m, which is significantly better than traditional Kalman filtering. The dynamically corrected pseudorange calculation values ​​make the spoofing signal consistent with the spatial geometry of the real satellite, avoiding the receiver's detection mechanism based on the anomalies in the geometric distribution of multiple satellites. The parameter injection uses a rectangular window function for segmented updates, avoiding the exposure of signal characteristic regularities caused by continuous parameter adjustments.

[0172] Step 3 involves frequency hopping sequence generation, time-domain waveform synthesis, and dynamic parameter optimization. The frequency hopping frequency set covers a frequency offset of ±2MHz. Combined with pseudo-random sequence modulation, the coherent energy of multipath reflected signals is dispersed, reducing code phase ambiguity caused by multipath. The frequency hopping step size is adjusted in real time according to the UAV velocity vector to compensate for motion Doppler frequency shift and avoid signal loss due to frequency hopping carrier phase transition.

[0173] Step 4 achieves high-precision reconstruction of UAV trajectory in complex environments through spatiotemporal synchronization, coordinate system transformation, and Kalman filter fusion. Radar point cloud data and optical image feature points complement each other. In building occlusion scenarios, the position error is reduced from 3m for a single sensor to 0.5m. Sensor time synchronization deviation compensation eliminates the time misalignment of heterogeneous sensor data, ensuring the spatiotemporal continuity of trajectory prediction.

[0174] Step 5 optimizes the frequency hopping parameters in real time based on the UAV's motion state and environmental interference characteristics. The technical advantage lies in correcting the frequency hopping carrier frequency through Doppler frequency shift compensation, making the phase of the deception signal carrier continuous and avoiding the loss of carrier loop lock in the receiver; and dynamically expanding the frequency hopping step size according to the UAV's acceleration, automatically increasing the frequency offset range when the target suddenly maneuvers, and maintaining the signal acquisition probability >95%.

[0175] Step 6 achieves the following performance improvements through beamforming weight optimization, phase synchronization, and power allocation: the optimal weight vector ensures that the main lobe gain of the beam is >20dBi, and the signal energy is concentrated to cover the target area, resulting in a 15dB improvement in signal-to-interference-plus-noise ratio compared to an omnidirectional antenna; distributed node phase synchronization compensates for propagation delay, ensuring that the signals from multiple nodes are coherently superimposed at the target location and suppressing sidelobe interference.

[0176] Step 7 achieves closed-loop optimization through loss function construction, LSTM network update, and environmental feature library iteration. The loss function, combined with orbital error and power constraints, drives LSTM weight updates, reducing prediction error by more than 30%.

[0177] A terminal device comprising a programmable radio frequency front-end module, a multi-core digital signal processor, and a phased array antenna array, for executing a method for trapping unmanned aerial vehicles by transmitting navigation deception signals.

[0178] A storage medium storing computer-executable instructions, which, when executed by a processor, implement a method for trapping unmanned aerial vehicles (UAVs) that emits navigation deception signals.

[0179] The programmable RF front end supports dynamic frequency hopping and multi-mode signal generation. Different modulation waveforms are loaded in real time through FPGA to adapt to the frequency band and protocol requirements of different UAV navigation receivers. The phased array antenna array dynamically adjusts the signal coverage area in complex electromagnetic environments through software-defined beam pointing and power allocation to avoid enemy interference source location.

[0180] The multi-core DSP adopts a heterogeneous architecture to handle dynamic trajectory prediction, multi-source data fusion, and frequency hopping parameter optimization, ensuring that the end-to-end delay from signal generation to transmission is less than 10ms, meeting the real-time trapping requirements of high-speed UAVs.

[0181] This storage medium enables the solidification and dynamic upgrading of UAV trapping algorithms by storing computer-executable instructions. Its core advantages include a modular instruction set that supports independent updates of the LSTM orbit prediction model, frequency hopping control logic, and distributed phased array collaborative algorithm.

[0182] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for trapping unmanned aerial vehicles (UAVs) by transmitting navigation deception signals, characterized in that, include: Step 1: Generate a navigation deception signal synchronized with the time reference of the satellite navigation system through an atomic clock synchronization mechanism; In step 1, a navigation deception signal baseband synchronized with the satellite system time reference is generated: s spoof (t)=A·C / A(t-Δt)·sin(2πf Ll t+φ0), Where A is the signal amplitude, C / A(·) is the Gold code pseudo-arbitrary sequence, and f Ll φ0 is the GPS carrier frequency, φ0 is the initial phase, and t is the time variable, s. spoof (t) represents the baseband of the navigation deception signal, and π is the constant pi. Step 2: Inject dynamically predicted satellite orbit parameters based on navigation deception signals; Step 2, based on the dynamically predicted satellite orbit parameters injected using navigation deception signals, further includes: Sub-step 2.1: Receive the navigation spoofing signal baseband generated in step 1, obtain the current satellite ephemeris data, and construct the orbital parameter vector: Among them, X t Let x be the satellite orbital state vector. sat y sat z sat The satellite's position in the ECEF coordinate system. The satellite's velocity; Sub-step 2.2: Predict future satellite orbital parameters in the time domain using a long short-term memory network. Among them, X t-n:t W is a sequence of orbital parameter vectors at historical time n. lstm Here is the pre-trained network weight matrix, and Δh is the predicted future time length. The satellite orbital parameters are for the predicted future time, where t is the time variable; Sub-step 2.3 involves injecting the predicted orbit parameters into the navigation spoofing signal baseband to correct the pseudorange calculation values: Where, ρ true (t) represents the true pseudorange, Δρ(t) represents the pseudorange correction, and T lnj The parameter update period is denoted by ρ, rect(·) is a rectangular window function, t0 is the parameter injection start time, and ρ is the parameter update period. spoof (t) represents the pseudorange of the deception signal, where t is a time variable; Step 3: Implement subcarrier pseudo-arbitrary frequency hopping control on the navigation deception signal with injected parameters; In step 3, a frequency hopping sequence is generated based on the pseudorange correction calculated in sub-step 2.3, and the frequency hopping interval and frequency set are determined: Among them, T hop F is the frequency hopping time slot width. set For the set of frequency hopping frequencies, Δf max For maximum frequency offset, v max The maximum speed of the drone is given by c, the speed of light is given by f0, and the nominal frequency is given by Δf. step δf is the frequency hopping step size. var Let K be any frequency offset, and K be the total number of frequency hopping points; In step 3, the time-domain waveform of the navigation spoofing signal under frequency hopping control is generated: Among them, s hop (t) represents the time-domain waveform of the navigation deception signal after frequency hopping modulation, s spoof (t) represents the baseband navigation spoofing signal, f n Let be the frequency hopping carrier frequency of the nth time slot, rect(·) be the rectangular window function, M be the number of frequency hopping times in a single parameter injection period, and j be the imaginary unit; Step 4: Collect multi-source data from radar and optical sensors and fuse them to generate the UAV's trajectory; Step 4, which involves collecting multi-source data from radar and optical sensors and fusing them to generate the UAV's trajectory, further includes: Sub-step 4.1 involves performing spatiotemporal synchronization between radar point cloud data and optical image data, and calculating the compensation amount for the deviation between the sensor timestamp and the atomic clock time. Where, Δt sync This is the compensation amount for the time synchronization deviation between the radar and the optical sensor. This is the timestamp of the radar sensor during the m-th sampling. This is the timestamp of the optical sensor at the m-th sampling. This represents the target position in the radar polar coordinate system. Here, c represents the target position in the optical image coordinate system, and B represents the speed of light. Sub-step 4.2: Convert the radar polar coordinate system data to the WGS84 geographic coordinate system: p WGS84 =R z (θ)·S·p radar +T, Where, p WGS84 R represents the position of the target in the WGS84 coordinate system. z (θ) is the rotation matrix about the Z-axis, θ is the heading angle, S is the radar sensor scale factor matrix, and p radar The data consists of the original polar coordinates of the radar, where T is the translation vector. Sub-step 4.3: Fuse multi-source data to generate the UAV's six-degree-of-freedom motion trajectory: in, Let F be the UAV state estimation vector at time k. k Let K be the state transition matrix. k Let z be the Kalman gain matrix. k H is the observation vector. k For the observation matrix, This is the UAV state estimation vector at time k-1; Step 5: Adjust the frequency hopping control parameters according to the drone's movement trajectory; Step 5, adjusting the frequency hopping control parameters according to the UAV's motion trajectory, further includes: Sub-step 5.1: Based on the UAV trajectory generated in sub-step 4.3, extract the velocity vector and acceleration vector. in, Let v be the three-dimensional velocity components of the UAV at time k, Δz be the trajectory update period, and v k Let v be the three-dimensional velocity vector of the UAV at time k. k-1 Let a be the three-dimensional velocity vector of the UAV at time k-1. uav Let v be the three-dimensional acceleration vector of the UAV. uav The velocity vector of the UAV; Sub-step 5.2: Calculate the Doppler frequency shift compensation amount caused by motion: Where f0 is the nominal frequency, e sat v is the unit vector in the direction of the satellite's line of sight. sat e represents the satellite's velocity. uav Let Δf be the unit vector representing the direction of motion of the UAV. doppler This is the Doppler frequency shift compensation amount; Sub-step 5.3: Dynamically adjust the frequency hopping parameter set: δf′ var =δf var +Δf doppler , Among them, a max Δf is the maximum acceleration threshold for the drone. step Let δf be the initial frequency hopping step size. var Let Δf′ be an initial arbitrary frequency offset. step For adaptive frequency hopping step size, δf′ var It is a composite arbitrary frequency offset; Step 6: Transmit the adjusted navigation deception signal through a distributed phased array antenna array; Step 6, transmitting the adjusted navigation deception signal via a distributed phased array antenna array, further includes: Sub-step 6.1 calculates beamforming weights based on the frequency hopping parameters adjusted in sub-step 5.3 to maximize the signal-to-interference-plus-noise ratio (SINR) in the target area: Among them, w * Let w be the optimal beamforming weight vector, H be the target channel matrix, and R be the beamforming weight vector. n H is the noise covariance matrix. j Let w be the channel matrix of the interference source. H This is the conjugate transpose of the weight vector; Sub-step 6.2: Synchronize the carrier phase of the distributed nodes using a reference signal: in, For the synchronization phase of the k-th node, φ ref As the reference phase standard, f0 is the nominal frequency. Let be the propagation delay from node k to the drone, ∈ PLL This refers to the tracking error of the phase-locked loop; Sub-step 6.3: Dynamically allocate the transmit power of each node and synthesize the radiated signal: Among them, P k Dynamically allocate power to the k-th node, P max Where η is the maximum allowable transmit power for a single node, h is the power allocation efficiency factor, and η is the maximum allowable transmit power for a single node. k Let k be the channel response vector of the k-th node. For noise power, I env For environmental interference power, P total Budget for total transmit power; Step 7: Dynamically optimize orbit prediction parameters based on transmitted signal feedback data; Step 7, which involves dynamically optimizing the orbit prediction parameters based on the transmitted signal feedback data, further includes: Sub-step 7.1: Collect the transmit power data and received signal bit error rate allocated in sub-step 6.3, and calculate the orbit prediction error evaluation index. Among them, J(W lstm ) is the loss function. Let σ be the actual satellite orbital parameters at the k-th sampling time. pos P is the position error tolerance threshold. total For the total transmit power budget, P k Dynamically allocate power to the k-th node, P max Let be the maximum allowable transmit power for a single node, and λ be the weighting factor for the power constraint term. The predicted satellite orbit parameters are given at the k-th sampling time. Sub-step 7.2 updates the LSTM network weights from sub-step 2.2 using the backpropagation algorithm: Among them, W′ lstm For the updated LSTM network weight matrix, W lstm Here, η is the weight matrix of the pre-trained network, and η is the power allocation efficiency factor. This represents the gradient of the loss function with respect to the weights. Sub-step 7.3: Reconstruct the dynamic prediction model and update the environmental feature library: Among them, D env For environmental feature database, X t The true values ​​of the satellite orbital parameters at the current moment. P represents the future orbital parameters predicted by LSTM. k Power is dynamically allocated to the k-th node, and BER is the bit error rate threshold.

2. The method for trapping a drone by transmitting navigation deception signals according to claim 1, characterized in that, Step 1, which generates a navigation deception signal synchronized with the time reference of the satellite navigation system through an atomic clock synchronization mechanism, further includes: Calculate the local atomic clock time deviation by receiving the 1PPS time signal broadcast by the satellite navigation system using a precise time protocol: Where Δt is the time synchronization deviation between the atomic clock and the satellite navigation system. This is the 1PPS timestamp broadcast by the satellite system during the i-th sampling. Let be the timestamp recorded by the local atomic clock during the i-th sampling, N be the number of synchronous samplings, and σ be the standard deviation of the measurement noise. The voltage-controlled crystal oscillator is used to calibrate the output frequency of the rubidium atomic clock, satisfying frequency stability constraints. Where f0 is the nominal frequency, f out h represents the actual output frequency of the atomic clock. -1 σ is the noise figure, τ is the averaging time, and σ is the noise figure. y (·) represents the Allan variance, h0 represents the flicker noise coefficient of the frequency noise, and h1 represents the frequency arbitrary wander coefficient of the frequency noise.

3. The method for trapping a drone by transmitting navigation deception signals according to claim 1, characterized in that, In step 3, the frequency hopping parameters are optimized in real time based on the drone's motion trajectory: Where, Δf′ step v is the dynamically adjusted frequency hopping step size. uav Let ρ(t) be the velocity vector of the UAV, Δρ(t) be the pseudorange correction, and ||v uav || represents the magnitude of the UAV's velocity vector, δf′ var It is designed to adapt to arbitrary frequency offset.

4. A terminal device, characterized in that, It includes a programmable radio frequency front-end module, a multi-core digital signal processor, and a phased array antenna array, and is used to execute the drone trapping method for transmitting navigation deception signals as described in claim 1.

5. A storage medium, characterized in that, The device stores computer-executable instructions, which, when executed by a processor, implement the drone trapping method for transmitting navigation deception signals as described in claim 1.

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