Trapping method for unmanned aerial vehicle emitting navigation deception signal

Abstract

The invention relates to the technical field of unmanned aerial vehicle trapping, and discloses an unmanned aerial vehicle trapping method for emitting a navigation deception signal, which comprises the following steps: 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; 3, subcarrier pseudo-arbitrary frequency hopping control is carried out on the navigation deception signal with the injected parameters; and 4, collecting multi-source data of the radar and the optical sensor, and fusing the multi-source data to generate a motion track of the unmanned aerial vehicle. According to the invention, an atomic clock synchronization mechanism is adopted to generate a high-precision time reference, a technical scheme of dynamic orbit prediction and pseudo-random frequency hopping control is combined, time synchronization with a satellite system is realized through an atomic clock, satellite orbit parameters are dynamically corrected through a long and short-term memory network, and rapid hopping modulation of a subcarrier frequency band is realized. The technical effects of high space-time consistency of deception signals and dynamic disguising of spectrum characteristics are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV trapping, and specifically to a UAV trapping method for transmitting navigation deception signals. Background Technique

[0002] With the rapid development of UAV technology, the application of civilian and commercial UAVs based on satellite navigation systems has become increasingly popular in the fields of aerial photography and logistics. However, this has also brought security risks such as illegal intrusion. Among the countermeasures against UAVs, navigation signal deception has become a research hotspot in the field of active defense due to its characteristic of remotely and covertly interfering with the UAV's heading. The existing technology mainly generates false satellite navigation signals and injects incorrect positioning information into the target UAV, forcing it to deviate from the predetermined flight path or land. However, the technology has significant defects in terms of concealment, environmental adaptability, and trapping reliability, specifically manifested as the following core problems:

[0003] Traditional methods use GPS-tamed crystal oscillators to generate a time reference, and the clock synchronization accuracy is limited to the microsecond level, resulting in cumulative pseudorange errors over time. If the pseudorange deviation between the deception signal and the real satellite signal exceeds the detection threshold of the receiver autonomous integrity monitoring algorithm, it is easy to trigger the alarm mechanism and cause the deception to fail. At the same time, the existing technology relies on fixed ephemeris parameters to generate deception signals and cannot dynamically match the perturbations of the real satellite orbits, resulting in the misalignment of the spatial geometric relationship between the deception signal and the real constellation, further exacerbating the RAIM detection risk. In addition, the single-frequency continuous emission mode makes the deception signal exhibit fixed characteristics in the frequency domain and is easily detected by the enemy's spectrum sensing equipment through energy accumulation, with an exposure probability of over 80%.

[0004] In an urban scene with dense buildings, UAVs often use multipath suppression algorithms to screen navigation signals. However, due to the lack of high-precision target trajectory reconstruction capabilities in the existing technology, it is impossible to generate deception signals that match the real environment. Single-sensor positioning is affected by occlusion and reflection interference, and the position error exceeds 3m, resulting in a deviation in the spatial direction of the deception signal and an inability to cover the effective capture range of the target receiver. In addition, the omnidirectional antenna broadcast emission mode disperses the signal energy, and the signal-to-noise ratio is lower than 5dB, making it easy to be submerged by noise in a strong electromagnetic interference environment, and the trapping success rate is less than 40%.

[0005] Therefore, the present invention proposes a UAV trapping method for transmitting navigation deception signals to solve the above-mentioned problems. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the present invention provides a UAV trapping method for transmitting navigation deception signals to solve the problems raised in the above background technique.

[0007] To achieve the above object, the present invention is implemented through the following technical solutions: A method for trapping an unmanned aerial vehicle by emitting a navigation spoofing signal, comprising:

[0008] Step 1, generating a navigation spoofing signal synchronized with the time reference of the satellite navigation system through an atomic clock synchronization mechanism;

[0009] Step 2, injecting the dynamically predicted satellite orbit parameters based on the navigation spoofing signal;

[0010] Step 3, implementing subcarrier pseudo-random frequency hopping control on the navigation spoofing signal with injected parameters;

[0011] Step 4, collecting multi-source data from radar and optical sensors and fusing them to generate the motion trajectory of the unmanned aerial vehicle;

[0012] Step 5, adjusting the frequency hopping control parameters according to the motion trajectory of the unmanned aerial vehicle;

[0013] Step 6, transmitting the adjusted navigation spoofing signal through a distributed phased array antenna array;

[0014] Step 7, dynamically optimizing the orbit prediction parameters based on the feedback data of the transmitted signal.

[0015] Preferably, in step 1, generating a navigation spoofing signal synchronized with the time reference of the satellite navigation system through an atomic clock synchronization mechanism further includes:

[0016] Sub-step 1.1, receiving the 1PPS time signal broadcast by the satellite navigation system through the Precision Time Protocol and calculating the local atomic clock time deviation:

[0017]

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

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

[0020]

[0021] where f0 is the nominal frequency, f out is the actual output frequency of the atomic clock, h -1 is the noise factor, τ is the averaging time, σ y (·) is the Allan variance, h0 is the flicker noise factor of the frequency noise, and h1 is the frequency random walk factor of the frequency noise;

[0022] Sub-step 1.3, generating a baseband of a navigation spoofing signal 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-random sequence, f Ll is the GPS carrier frequency, φ0 is the initial phase, t is the time variable, s spoof (t) is the baseband navigation spoofing signal, and π is the constant of pi.

[0025] Preferably, in the said step 2, based on the satellite orbit parameters predicted by the navigation spoofing signal injection dynamics, it further includes:

[0026] Sub-step 2.1, receiving the baseband of the navigation spoofing signal generated in step 1.3, obtaining the current satellite ephemeris data and constructing an orbit parameter vector:

[0027]

[0028] where X t is the satellite orbit state vector, x sat , y sat , z sat are the satellite ECEF coordinate system positions, is the satellite motion speed;

[0029] Sub-step 2.2, predicting the satellite orbit parameters in the future time domain through a long short-term memory network:

[0030]

[0031] where X t-n:t is the sequence of orbit parameter vectors at the historical n moments, W lstm is the pre-trained network weight matrix, Δh is the predicted future time length, is the predicted satellite orbit parameters at the future moment, and t is the time variable;

[0032] Sub-step 2.3, injecting the predicted orbit parameters into the baseband of the navigation spoofing signal to correct the pseudo-range calculation value:

[0033]

[0034] where ρ true (t) is the true pseudo-range, Δρ(t) is the pseudo-range correction amount, T lnjLet \(T\) be the parameter update period, \(rect(\cdot)\) be the rectangular window function, \(t_0\) be the starting time of parameter injection, and \(\rho(t)\) be the pseudorange of the spoofing signal, where \(t\) is the time variable. spoof (t) is the pseudorange of the spoofing signal, and \(t\) 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 amount calculated in step 2.3, and determine the frequency hopping interval and frequency set:

[0037]

[0038] where \(T\) hop is the frequency hopping time slot width, \(F\) set is the frequency hopping frequency set, \(\Delta f\) max is the maximum frequency deviation, \(v\) max is the maximum movement speed of the UAV, \(c\) is the speed of light, \(f_0\) is the nominal frequency, \(\Delta f\) step is the frequency hopping step size, \(\delta f\) var is an arbitrary frequency deviation amount, and \(K\) is 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] where \(s(t)\) hop is the time-domain waveform of the navigation spoofing signal after frequency hopping modulation, \(s(t)\) spoof is the baseband navigation spoofing signal, \(f\) n is the frequency hopping carrier frequency of the \(n\)th time slot, \(rect(\cdot)\) is the rectangular window function, and \(M\) is the number of frequency hops within a single parameter injection period;

[0042] Sub-step 3.3: Optimize the frequency hopping parameters in real time according to the UAV movement trajectory in step 4:

[0043]

[0044] where \(\Delta f'\) step is the dynamically adjusted frequency hopping step size, \(v\) uav is the UAV velocity vector, \(\Delta\rho(t)\) is the pseudorange correction amount, \(\vert\vert v\vert\vert\) uav is the modulus of the UAV velocity vector, and \(\delta f'\) var is the adaptive arbitrary frequency deviation amount.

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

[0046] Sub-step 4.1: Implement spatio-temporal synchronization for radar point cloud data and optical image data, and calculate the deviation compensation amount between the sensor timestamp and the atomic clock time:

[0047]

[0048] Among them, Δt sync is the time synchronization deviation compensation amount between the radar and the optical sensor, is the timestamp of the radar sensor at the m-th sampling, is the timestamp of the optical sensor at the m-th sampling, is the target position in the radar polar coordinate system, is the target position in the optical image coordinate system, c is the speed of light, and B is the number of synchronization calibration points;

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

[0050] p WGS84 = R z (θ)·S·p radar + T,

[0051] Among them, p WGS84 is the position of the target in the WGS84 coordinate system, R z (θ) is the rotation matrix around the Z axis, θ is the heading angle, S is the radar sensor scale factor matrix, p radar is the original radar polar coordinate data, and T is the translation vector;

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

[0053]

[0054] Among them, is the UAV state estimation vector at time k, F k is the state transition matrix, K k is the Kalman gain matrix, z k is the observation vector, H k is the observation matrix, is the UAV state estimation vector at time k-1.

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

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

[0057]

[0058]

[0059] Among them, is the three-dimensional velocity component of the UAV at time k, Δz is the trajectory update period, and v k is the three-dimensional velocity vector of the UAV at time k, and v k-1 is the three-dimensional velocity vector of the UAV at time k - 1, and a uav is the three-dimensional acceleration vector of the UAV, and v uav is the velocity vector of the UAV;

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

[0061]

[0062] Among them, f0 is the nominal frequency, and e sat is the unit vector in the satellite line-of-sight direction, and v sat is the satellite motion speed, and e uav is the unit vector in the UAV motion direction, and Δf doppler is the Doppler frequency shift compensation amount;

[0063] Sub-step 5.3, dynamically adjust the frequency hopping parameter set in step 3.1:

[0064]

[0065] δf′ var = δf var + Δf doppler ,

[0066] Among them, a max is the maximum acceleration threshold of the UAV, Δf step is the initial frequency hopping step size, δf var is the initial arbitrary frequency offset, Δf′ step is the adaptive frequency hopping step size, and δf′ var is the composite arbitrary frequency offset.

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

[0068] Sub-step 6.1, calculate the beamforming weight based on the frequency hopping parameters adjusted in step 5.3 to maximize the signal-to-interference-plus-noise ratio in the target area:

[0069]

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

[0071] Sub-step 6.2, synchronize the carrier phase of the distributed nodes through the reference signal:

[0072]

[0073] where, is the synchronization phase of the k-th node, φ ref is the reference phase benchmark, f0 is the nominal frequency, is the propagation delay from the k-th node to the UAV, ∈ PLL is the PLL tracking error;

[0074] Sub-step 6.3, dynamically allocate the transmission power of each node and synthesize the radiation signal:

[0075]

[0076] where, P k is the dynamically allocated power of the k-th node, P max is the maximum allowable transmission power of a single node, η is the power allocation efficiency factor, h k is the channel response vector of the k-th node, is the noise power, I env is the environmental interference power, P total is the total transmission power budget.

[0077] Preferably, in step 7, based on the feedback data of the transmitted signal, dynamically optimize the orbit prediction parameters, further including:

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

[0079]

[0080] where, J(W lstm ) is the loss function, is the true satellite orbit parameter at the k-th sampling moment, σ pos is the position error tolerance threshold, P total is the total transmission power budget, P k is the dynamically allocated power of the k-th node, P max is the maximum allowable transmission power of a single node, λ is the weight factor of the power constraint term, is the predicted satellite orbit parameter at the k-th sampling moment;

[0081] Sub-step 7.2, update the weights of the LSTM network in step 2.2 through the backpropagation algorithm:

[0082]

[0083] Among them, W′ lstm is the updated weight matrix of the LSTM network, W lstm is the pre-trained network weight matrix, η is the power allocation efficiency factor, is 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 is the environmental feature library, X t is the true value of the satellite orbit parameters at the current moment, is the future orbit parameters predicted by the LSTM, P k is the dynamically allocated power for the k-th node, and BER is the bit error rate threshold.

[0087] A terminal device 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 method for trapping an unmanned aerial vehicle by transmitting a navigation deception signal as described above.

[0088] A storage medium stores computer-executable instructions, and when the instructions are executed by a processor, the method for trapping an unmanned aerial vehicle by transmitting a navigation deception signal as described above is implemented.

[0089] The present invention provides a method for trapping an unmanned aerial vehicle by transmitting a navigation deception signal. It has the following beneficial effects:

[0090] 1. The present invention adopts an atomic clock synchronization mechanism to generate a high-precision time reference, combines the technical solutions of dynamic orbit prediction and pseudo-random frequency hopping control, realizes time synchronization with the satellite system through the atomic clock, dynamically corrects satellite orbit parameters by a long short-term memory network, and fast frequency hopping modulation of the subcarrier frequency band, achieving the technical effects of high spatio-temporal consistency of the deception signal and dynamic camouflage of the spectral characteristics. Compared with the existing solutions that rely on fixed ephemeris parameters and single-frequency band transmission, it solves the core defects of the traditional method being easily identified by the receiver's autonomous integrity monitoring and intercepted by spectrum sensing devices due to signal time cumulative error and fixed spectral characteristics, greatly reducing the exposure risk of the deception signal and achieving a breakthrough in concealment.

[0091] 2. The present invention uses multi-source sensor data fusion to generate the UAV trajectory, combines the technical solutions of dynamic optimization of frequency hopping parameters and collaborative transmission of distributed phased arrays, and through complementary correction of radar and optical data, real-time compensation of Doppler frequency shift, and spatial energy directional focusing of beamforming, achieves the technical effects of accurate reconstruction of the UAV motion trajectory and efficient coverage of spoofing signals in complex multipath environments. Compared with the problems of insufficient single-sensor positioning accuracy and omnidirectional antenna signal dispersion in the prior art, it solves the problem of trapping failure caused by signal attenuation and target loss of lock in urban canyons and strong electromagnetic interference scenarios, and significantly improves the stable trapping ability of UAVs in dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0093] To enable those skilled in the art to understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

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

[0095] Embodiment:

[0096] Please refer to the attached Figure 1 , the embodiment of the present invention provides a method for trapping UAVs by transmitting navigation spoofing signals, including:

[0097] Step 1, generating a navigation spoofing signal synchronized with the time reference of the satellite navigation system through an atomic clock synchronization mechanism;

[0098] Sub-step 1.1, receiving the 1PPS time signal broadcast by the satellite navigation system through the Precision Time Protocol, and calculating the local atomic clock time deviation:

[0099]

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

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

[0102]

[0103]

[0104] Among them, f0 is the nominal frequency, f out is the actual output frequency of the atomic clock, h -1 is the noise coefficient, τ is the average time, σ y (·) is the Allan variance, h0 is the flicker noise coefficient of the frequency noise, and h1 is the frequency random walk coefficient of the frequency noise;

[0105] Sub-step 1.3, generate the baseband of the navigation spoofing signal 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-random sequence, f Ll is the GPS carrier frequency, φ0 is the initial phase, t is the time variable, and s spoof (t) is the baseband navigation spoofing signal, and π is the constant of pi;

[0108] Step 2, inject the dynamic predicted satellite orbit parameters based on the navigation spoofing signal;

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

[0110]

[0111] where X t is the satellite orbit state vector, x sat , y sat , z sat are the satellite ECEF coordinate system positions, is the satellite motion speed;

[0112] Sub-step 2.2, predict the satellite orbit parameters in the future time domain through the long short-term memory network:

[0113]

[0114] where X t-n:t is the sequence of orbit parameter vectors at the historical nth moment, W lstm is the pre-trained network weight matrix, Δh is the predicted future time length, is the predicted satellite orbit parameters at the future moment, and t is the time variable;

[0115] Sub-step 2.3: Inject the predicted orbit parameters into the baseband of the navigation spoofing signal to correct the calculated pseudorange value:

[0116]

[0117] where ρ true (t) is the true pseudorange, Δρ(t) is the pseudorange correction amount, T lnj is the parameter update period, rect(·) is the rectangular window function, t0 is the starting time of parameter injection, ρ spoof (t) is the pseudorange of the spoofing signal, and t is the time variable;

[0118] Step 3: Implement sub-carrier pseudo-random frequency hopping control on the navigation spoofing signal with injected parameters;

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

[0120]

[0121] where T hop is the frequency hopping time slot width, F set is the frequency hopping frequency set, Δf max is the maximum frequency deviation, v max is the maximum movement speed of the UAV, c is the speed of light, f0 is the nominal frequency, Δf step is the frequency hopping step size, δf var is an arbitrary frequency deviation amount, and K is 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] where s hop (t) is the time-domain waveform of the navigation spoofing signal after frequency hopping modulation, s spoof (t) is the baseband navigation spoofing signal, f n is the frequency hopping carrier frequency in the nth time slot, rect(·) is the rectangular window function, and M is the number of frequency hopping times within a single parameter injection period;

[0125] Sub-step 3.3: Optimize the frequency hopping parameters in real time according to the UAV movement trajectory in step 4:

[0126]

[0127] where Δf′ step is the dynamically adjusted frequency hopping step size, v uavis the UAV speed vector, Δρ(t) is the pseudorange correction amount, ||v uav || is the modulus of the UAV speed vector, δf′ var is the adaptive arbitrary frequency offset amount;

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

[0129] Sub-step 4.1: Implement spatio-temporal synchronization for the radar point cloud data and the optical image data, and calculate the deviation compensation amount between the sensor timestamp and the atomic clock time:

[0130]

[0131] where, Δt sync is the time synchronization deviation compensation amount between the radar and the optical sensor, is the timestamp of the radar sensor at the m-th sampling, is the timestamp of the optical sensor at the m-th sampling, is the target position in the radar polar coordinate system, is the target position in the optical image coordinate system, c is the speed of light, and B is the number of synchronization calibration points;

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

[0133] p WGS84 =R z (θ)·S·p radar +T,

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

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

[0136]

[0137] where, is the UAV state estimation vector at time k, F k is the state transition matrix, K k is the Kalman gain matrix, z k is the observation vector, H k is the observation matrix, is the UAV state estimation vector at time k-1;

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

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

[0140]

[0141]

[0142] where is the three-dimensional velocity component of the UAV at time k, Δz is the trajectory update period, v k is the three-dimensional velocity vector of the UAV at time k, v k-1 is the three-dimensional velocity vector of the UAV at time k - 1, a uav is the three-dimensional acceleration vector of the UAV, v uav is the velocity vector of the UAV;

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

[0144]

[0145] where f0 is the nominal frequency, e sat is the unit vector in the satellite line-of-sight direction, v sat is the satellite movement speed, e uav is the unit vector in the UAV movement direction, Δf doppler is the Doppler frequency shift compensation amount;

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

[0147]

[0148] δf′ var = δf var + Δf doppler ,

[0149] where a max is the maximum acceleration threshold of the UAV, Δf step is the initial frequency hopping step size, δf var is the initial arbitrary frequency offset, Δf′ step is the adaptive frequency hopping step size, δf′ var is the composite arbitrary frequency offset;

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

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

[0152]

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

[0154] Sub-step 6.2: Synchronize the carrier phases of distributed nodes through reference signals:

[0155]

[0156] where is the synchronization phase of the k-th node, φ ref is the reference phase benchmark, f0 is the nominal frequency, is the propagation delay from the k-th node to the UAV, and ∈ PLL is the phase-locked loop tracking error;

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

[0158]

[0159] where P k is the dynamically allocated power of the k-th node, P max is the maximum allowable transmission power of a single node, η is the power allocation efficiency factor, h k is the channel response vector of the k-th node, is the noise power, I env is the environmental interference power, and P total is the total transmission power budget;

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

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

[0162]

[0163] where J(W lstm ) is the loss function, is the true satellite orbit parameter at the k-th sampling moment, σ pos is the position error tolerance threshold, and Ptotal is the total transmit power budget, P k is the power dynamically allocated to the k-th node, P max is the maximum allowable transmit power of a single node, and λ is the weight factor of the power constraint term are the predicted satellite orbit parameters at the k-th sampling moment

[0164] Sub-step 7.2, update the weights of the LSTM network in Step 2.2 through the backpropagation algorithm

[0165]

[0166] where W′ lstm is the updated weight matrix of the LSTM network, W lstm is the pre-trained network weight matrix, and η is the power allocation efficiency factor is 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] where D env is the environmental feature library, X t is the true value of the satellite orbit parameters at the current moment are the future orbit parameters predicted by the LSTM, P k is the power dynamically allocated to the k-th node, and BER is the bit error rate threshold

[0170] Step 1 synchronizes with the time reference of the atomic clock and the satellite navigation system, and combines the dynamic calibration of the voltage-controlled crystal oscillator to generate a high-precision navigation spoofing signal baseband with a time synchronization error less than 10 ns. The core advantage is that the long-term stability of the atomic clock suppresses the clock drift of the traditional GPS-tamed crystal oscillator, avoiding the RAIM alarm of the receiver triggered by the accumulation of pseudorange errors; synchronizes the 1PPS signal of the satellite in real time through the Precision Time Protocol to ensure the code phase alignment of the spoofing signal and the real satellite signal, avoiding the receiver detecting the spoofing source through the chip deviation; the combination of the Gold code pseudorandom sequence and the atomic clock frequency source generates a baseband signal with low cross-correlation characteristics, reducing the probability of the enemy detecting the spoofing signal through the correlation peak detection

[0171] Step 2 uses an LSTM network to dynamically predict satellite orbit parameters and injects navigation spoofing signals in real time. The technical value lies in that the LSTM network learns the satellite perturbation law through historical orbit data, and the mean square error of predicting future orbit parameters is <0.3m, which is significantly better than the traditional Kalman filter; the dynamically corrected pseudorange calculation value makes the spatial geometric relationship between the spoofing signal and the real satellite consistent, avoiding the detection mechanism of abnormal multi-satellite geometric distribution by the receiver; the parameter injection uses a rectangular window function for segmented updates to avoid the exposure of signal feature regularity caused by continuous parameter adjustments.

[0172] Step 3 generates a frequency hopping sequence, synthesizes a time-domain waveform, and dynamically optimizes parameters. The frequency hopping frequency set covers a frequency offset of ±2MHz. Combined with pseudo-random sequence modulation, it disperses the coherent energy of the multipath reflected signal and reduces the code phase ambiguity caused by multipath; adjusts the frequency hopping step size in real time according to the UAV speed vector to compensate for the motion Doppler frequency shift and avoid signal loss of lock caused by the phase jump of the frequency hopping carrier.

[0173] Step 4 realizes the high-precision reconstruction of the UAV trajectory through space-time synchronization, coordinate system transformation, and Kalman filter fusion. The radar point cloud data and the feature points of the optical image are complementary. In the building occlusion scenario, the position error is reduced from 3m of a single sensor to 0.5m; the compensation amount of the sensor time synchronization deviation eliminates the time misalignment of heterogeneous sensor data and ensures the spatio-temporal continuity of trajectory prediction.

[0174] Step 5 optimizes the frequency hopping parameters in real time based on the UAV motion state and environmental interference characteristics. The technical advantage is that the frequency hopping carrier frequency is corrected by the Doppler frequency shift compensation amount to make the carrier phase of the spoofing signal continuous and avoid the loss of lock of the receiver carrier loop; the frequency hopping step size is dynamically expanded according to the UAV acceleration, and the frequency offset range is automatically increased during the sudden maneuver of the target to maintain the signal capture probability >95%.

[0175] Step 6 realizes the following performance improvement through beamforming weight optimization, phase synchronization, and power distribution. The optimal weight vector makes the main lobe gain of the beam >20dBi, and the signal energy is concentrated in the target area. Compared with the omnidirectional antenna, the signal-to-interference-plus-noise ratio is increased by 15dB; the distributed node phase synchronization compensates for the propagation delay to ensure the coherent superposition of multi-node signals at the target and suppresses the sidelobe interference.

[0176] Step 7 realizes closed-loop optimization through loss function construction, LSTM network update, and environmental feature library iteration. The loss function combines orbit error and power constraints to drive the update of LSTM weights, and the prediction error is reduced by more than 30%.

[0177] A terminal device 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 a UAV trapping method for transmitting navigation spoofing signals.

[0178] A storage medium stores computer-executable instructions, which, when executed by a processor, implement a method for trapping drones by emitting navigation spoofing signals.

[0179] The programmable radio frequency front end supports dynamic frequency hopping and multi-system signal generation. By loading different modulation waveforms in real time through an FPGA, it can adapt to the frequency band and protocol requirements of different drone navigation receivers. The phased array antenna array dynamically adjusts the signal coverage area in a complex electromagnetic environment by software-defined beam pointing and power distribution, avoiding the positioning of enemy jamming sources.

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

[0181] This storage medium realizes the algorithm solidification and dynamic upgrade of the drone trapping method by storing computer-executable instructions. The core advantages include that the instruction set adopts a modular design, supporting independent updates of the LSTM orbit prediction model, frequency hopping control logic, and distributed phased array cooperation algorithm.

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

Claims

1. A method for trapping an unmanned aerial vehicle by emitting a navigation spoofing signal, characterized in that, Including: Step 1: Generate a navigation spoofing signal synchronized with the time reference of the satellite navigation system through an atomic clock synchronization mechanism; Step 2: Inject dynamically predicted satellite orbit parameters based on the navigation spoofing signal; Step 3: Implement subcarrier pseudo-random frequency hopping control on the navigation spoofing signal with injected parameters; Step 4: Collect multi-source data from radar and optical sensors and fuse them to generate the UAV motion trajectory; Step 5: Adjust the frequency hopping control parameters according to the UAV motion trajectory; Step 6: Transmit the adjusted navigation spoofing signal through a distributed phased array antenna array; Step 7: Dynamically optimize the orbit prediction parameters based on the feedback data of the transmitted signal.

2. The method for trapping an unmanned aerial vehicle by emitting a navigation spoofing signal according to claim 1, characterized in that, In the said Step 1, generating a navigation spoofing signal synchronized with the time reference of the satellite navigation system through an atomic clock synchronization mechanism further includes: Sub-step 1.1: Receive the 1PPS time signal broadcast by the satellite navigation system through the Precision Time Protocol and calculate the local atomic clock time deviation; where Δt is the time synchronization deviation between the atomic clock and the satellite navigation system, is the 1PPS timestamp broadcast by the satellite system at the i-th sampling, is the timestamp recorded by the local atomic clock at the i-th sampling, N is the number of synchronous samplings, and σ is the standard deviation of the measurement noise; Sub-step 1.2: Drive the voltage-controlled crystal oscillator to calibrate the output frequency of the rubidium atomic clock to meet the frequency stability constraint; where f0 is the nominal frequency, f out is the actual output frequency of the atomic clock, h -1 is the noise factor, τ is the average time, σ y (·) is the Allan variance, h0 is the flicker noise factor of the frequency noise, h1 is the frequency random walk factor of the frequency noise; Sub-step 1.3: Generate the baseband of the navigation spoofing signal synchronized with the satellite system time reference; 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-random sequence, f Ll is the GPS carrier frequency, φ0 is the initial phase, t is the time variable, s spoof (t) is the baseband navigation spoofing signal, and π is the constant of the circumference ratio.

3. A method for trapping an unmanned aerial vehicle by emitting a navigation spoofing signal according to claim 1, characterized in that, In the said Step 2, injecting dynamically predicted satellite orbit parameters based on the navigation spoofing signal further includes: Sub-step 2.1: Receive the baseband of the navigation spoofing signal generated in Step 1.3, obtain the current satellite ephemeris data and construct an orbit parameter vector; Among them, X t is the satellite orbit state vector, and x sat , y sat , z sat are the positions of the satellite in the ECEF coordinate system, and is the satellite's motion speed; Sub-step 2.2: Predict the satellite orbit parameters in the future time domain through a long short-term memory network; Among them, X t-n:t is the sequence of orbital parameter vectors at historical time n, W lstm is the pre-trained network weight matrix, Δh is the predicted future time length, is the predicted satellite orbital parameter at the future moment, and t is the time variable; Sub-step 2.3: Inject the predicted orbit parameters into the baseband of the navigation spoofing signal to correct the pseudorange calculation value; where ρ true (t) is the true pseudorange, Δρ(t) is the pseudorange correction, T lnj is the parameter update period, rect(·) is the rectangular window function, t0 is the starting time of parameter injection, ρ spoof (t) is the pseudorange of the spoofing signal, and t is the time variable.

4. A method for trapping an unmanned aerial vehicle by emitting a navigation spoofing signal according to claim 1, characterized in that, In the said Step 3, implementing subcarrier pseudo-random frequency hopping control on the navigation spoofing signal with injected parameters further includes: Sub-step 3.1: Generate a frequency hopping sequence according to the pseudorange correction amount calculated in Step 2.3, and determine the frequency hopping interval and frequency set; Among them, T hop is the hopping time slot width, F set is the hopping frequency set, Δf max is the maximum frequency deviation, v max is the maximum movement speed of the UAV, c is the speed of light, f0 is the nominal frequency, Δf step is the hopping step size, δf var is an arbitrary frequency deviation amount, and K is the total number of hopping frequency points; Sub-step 3.2: Generate the time-domain waveform of the navigation spoofing signal under frequency hopping control; Among them, s hop (t) is the time-domain waveform of the navigation spoofing signal after frequency-hopping modulation, s spoof (t) is the baseband navigation spoofing signal, f n is the frequency-hopping carrier frequency of the nth time slot, rect(·) is the rectangular window function, and M is the number of frequency hops within a single parameter injection period; Sub-step 3.3: Optimize the frequency hopping parameters in real time according to the UAV motion trajectory in Step 4; Among them, Δf′ step is the hopping frequency step after dynamic adjustment, v uav is the UAV velocity vector, Δρ(t) is the pseudorange correction, ||v uav || is the modulus of the UAV velocity vector, and δf′ var is the adaptive arbitrary frequency offset.

5. A method for trapping an unmanned aerial vehicle by emitting a navigation spoofing signal according to claim 1, characterized in that, In the said Step 4, collecting multi-source data from radar and optical sensors and fusing them to generate the UAV motion trajectory further includes: Sub-step 4.1: Implement spatio-temporal synchronization on the radar point cloud data and optical image data, and calculate the deviation compensation amount between the sensor timestamp and the atomic clock time; where, Δt sync is the time synchronization deviation compensation amount between the radar and the optical sensor, is the timestamp of the radar sensor at the m-th sampling, is the timestamp of the optical sensor at the m-th sampling, is the target position in the radar polar coordinate system, is the target position in the optical image coordinate system, c is the speed of light, and B is the number of synchronization calibration points; Sub-step 4.2: Convert the radar polar coordinate data to the WGS84 geodetic coordinate system; p WGS84 = R z (θ)·S·p radar + T, Among them, p WGS84 is the position of the target in the WGS84 coordinate system, R z (θ) is the rotation matrix about the Z-axis, θ is the heading angle, S is the radar sensor scale factor matrix, p radar is the original polar coordinate data of the radar, and T is the translation vector; Sub-step 4.3: Fuse the multi-source data to generate the six-degree-of-freedom motion trajectory of the UAV; Among them, is the UAV state estimation vector at time k, F k is the state transition matrix, K k is the Kalman gain matrix, z k is the observation vector, H k is the observation matrix, is the UAV state estimation vector at time k - 1.

6. The method for trapping an unmanned aerial vehicle by emitting a navigation spoofing signal according to claim 1, wherein In the said Step 5, adjusting the frequency hopping control parameters according to the UAV motion trajectory further includes: Sub-step 5.1: Extract the velocity vector and acceleration vector based on the UAV motion trajectory generated in Step 4.3; Among them, is the three-dimensional velocity component of the UAV at time k, Δz is the trajectory update period, and v k is the three-dimensional velocity vector of the UAV at time k, and v k-1 is the three-dimensional velocity vector of the UAV at time k-1, and a uav is the three-dimensional acceleration vector of the UAV, and v uav is the velocity vector of the UAV; Sub-step 5.2: Calculate the Doppler frequency shift compensation amount caused by the motion; where f0 is the nominal frequency, e sat is the unit vector in the satellite line-of-sight direction, v sat is the satellite's velocity, e uav is the unit vector in the UAV's motion direction, Δf doppler is the Doppler frequency shift compensation amount; Sub-step 5.3: Dynamically adjust the frequency hopping parameter set in Step 3.1; δf′ var = δf var + Δf doppler , where a max is the maximum acceleration threshold of the UAV, Δf step is the initial frequency hopping step size, δf var is the initial arbitrary frequency offset, Δf′ step is the adaptive frequency hopping step size, δf′ var is the composite arbitrary frequency offset.

7. A method for trapping an unmanned aerial vehicle by emitting a navigation spoofing signal according to claim 1, characterized in that, In the said Step 6, transmitting the adjusted navigation spoofing signal through a distributed phased array antenna array further includes: Sub-step 6.1: Calculate the beamforming weights based on the frequency hopping parameters adjusted in Step 5.3 to maximize the signal-to-interference-plus-noise ratio in the target area; where, w * is the optimal beamforming weight vector, w is the beamforming weight vector, H is the target channel matrix, R n is the noise covariance matrix, H j is the interfering source channel matrix, w H is the conjugate transpose of the weight vector; Sub-step 6.2, synchronize the carrier phases of distributed nodes through reference signals: Among them, is the synchronization phase of the k-th node, φ ref is the reference phase benchmark, f0 is the nominal frequency, is the propagation delay from the k-th node to the UAV, ∈ PLL is the phase-locked loop tracking error; Sub-step 6.3, dynamically allocate the transmission power of each node and synthesize radiation signals: Among them, P k dynamically allocates power to the k-th node, P max is the maximum allowable transmission power of a single node, η is the power allocation efficiency factor, h k is the channel response vector of the k-th node, is the noise power, I env is the environmental interference power, P total is the total transmission power budget.

8. A method for trapping an unmanned aerial vehicle by emitting a navigation deception signal according to claim 1, characterized in that In the said step 7, based on the feedback data of the transmitted signal to dynamically optimize the orbit prediction parameters, it further includes: Sub-step 7.1, collect the transmission power data and the received signal bit error rate allocated in step 6.3, and calculate the orbit prediction error evaluation index: Among them, J(W lstm ) is the loss function, is the true satellite orbit parameter at the k-th sampling moment, σ pos is the position error tolerance threshold, P total is the total transmit power budget, P k is the dynamically allocated power of the k-th node, P max is the maximum allowable transmit power of a single node, λ is the weight factor of the power constraint term, is the predicted satellite orbit parameter at the k-th sampling moment; Sub-step 7.2, update the weights of the LSTM network in step 2.2 through the backpropagation algorithm: Among them, W′ lstm is the updated weight matrix of the LSTM network, W lstm is the pre-trained network weight matrix, η is the power allocation efficiency factor, is 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 is the environmental feature library, X t is the true value of the satellite orbit parameters at the current moment, is the future orbit parameters predicted by LSTM, P k is the dynamically allocated power of the k-th node, and BER is the bit error rate threshold.

9. 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 a method for trapping drones by transmitting navigation spoofing signals according to any one of claims 1-8.

10. A storage medium, characterized in that, It stores computer-executable instructions, and when the instructions are executed by a processor, a method for trapping drones by transmitting navigation spoofing signals according to any one of claims 1-8 is realized.

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