A drone navigation deception method and system based on multi-mode satellite signal forgery

Abstract

The present application provides a method and system for drone navigation deception based on multi-mode satellite signal forgery. Among them, first, the original signal parameters of the multi-mode satellite signal in the drone navigation link in the target area are collected in real time, covering signal delay, carrier phase and signal strength distribution; secondly, the spatiotemporal characteristics are extracted through signal delay and carrier phase, and the time-domain synchronized forged navigation parameters are generated in combination with the dynamic change of signal strength; then the signal delay correction amount and carrier phase offset in the forged navigation parameters are dynamically adjusted; at the same time, the navigation message is dynamically compensated based on the three-dimensional coordinate increment to generate a multi-mode forged navigation signal covering the drone receiving end; finally, the forged navigation signal is superimposed on the original signal parameters to form a deceptive navigation trajectory that induces the drone to deviate from the original route. The technical solution provided by the present application can improve the multi-mode compatibility and concealment of drone navigation deception.

Description

Technical Field

[0001] The present application relates to the field of drone navigation spoofing technology, and in particular to a drone navigation spoofing method and system based on multi-mode satellite signal forgery. Background Art

[0002] The current mainstream solution uses single-mode satellite signal spoofing technology. This technology reverse-engineers the signal protocol of a single satellite navigation system that the target drone relies on, generates a fake navigation signal with matching static parameters, and transmits it in a targeted direction. This signal power is then used to suppress the real signal, forcing the drone to receive the fabricated positioning information. For example, a signal generator based on a specific frequency band of a satellite navigation system can simulate false longitude and latitude coordinates, and achieve short-term trajectory deviation through time synchronization and power control.

[0003] Existing solutions suffer from three flaws. First, they lack multi-mode compatibility and are designed only for a single satellite system, making them incapable of simultaneously counterfeiting multi-mode signals such as the Beidou and global satellite navigation systems. Modern drones generally use multi-mode joint positioning to enhance their anti-interference capabilities, making single-mode deception easily detected by redundant verification mechanisms. Second, they suffer from poor dynamic adaptability, with counterfeit signal parameters fixed and a lack of feedback and correction of the drone's real-time flight status. This makes it difficult to maintain signal spatiotemporal continuity and can easily trigger navigation system anomaly detection. Furthermore, they suffer from concealment flaws, relying on power suppression to achieve signal coverage. Abnormal energy fluctuations can easily be captured by the drone's spectrum sensing module, triggering anti-spoofing emergency mechanisms. Summary of the Invention

[0004] The present application provides a method and system for drone navigation spoofing based on multi-mode satellite signal forgery, which is used to solve the problems of low multi-mode compatibility and poor concealment of drone navigation spoofing in the prior art.

[0005] In a first aspect, the present application provides a method for deceiving drone navigation based on multi-mode satellite signal forgery, comprising:

[0006] Collecting the original signal parameters of the multi-mode satellite signal in the current navigation link of the UAV in the target area, the original signal parameters including signal delay, carrier phase and signal strength distribution;

[0007] Extracting the spatiotemporal characteristics of the multimode satellite signal based on the signal delay and carrier phase in the original signal parameters, and fusing the dynamic changes of the signal strength distribution to generate forged navigation signal parameters synchronized with the original signal parameters in the time domain;

[0008] Dynamically correcting the forged navigation signal parameters according to a real-time position deviation between a preset flight path of the UAV and a decoy target path to obtain corrected forged navigation signal parameters;

[0009] Based on the signal delay correction amount in the corrected forged navigation signal parameters and the offset of the carrier phase, and in combination with the real-time position deviation, the three-dimensional coordinate increment of the navigation message is dynamically compensated to generate a multi-mode forged navigation signal covering the receiving end of the drone;

[0010] At the receiving end of the UAV, the multi-mode forged navigation signal is superimposed on the multi-mode satellite signal corresponding to the original signal parameters to generate a decoy navigation trajectory.

[0011] Optionally, dynamically correcting the forged navigation signal parameters according to a real-time position deviation between the preset flight path of the UAV and the decoy target path to obtain corrected forged navigation signal parameters includes:

[0012] Obtaining a real-time trajectory point sequence of a preset flight path of the UAV and an expected trajectory point sequence of a decoy target path, and calculating a real-time position deviation between the real-time trajectory point sequence and the expected trajectory point sequence at corresponding timestamps as a deviation weight coefficient;

[0013] Projecting the real-time position deviation into the pseudo-range domain along the satellite signal propagation direction, and constructing the signal delay correction and carrier phase offset of the forged navigation signal parameters in combination with the deviation weight coefficient;

[0014] Based on the rate of change of the real-time position deviation, the signal delay correction amount and the carrier phase offset are reversely adjusted to obtain the adjusted signal delay correction amount and carrier phase offset;

[0015] The adjusted signal delay correction amount and carrier phase offset amount are dynamically compensated using the historical accumulation of the deviation weight coefficient to generate corrected forged navigation signal parameters.

[0016] Optionally, the reverse joint adjustment of the signal delay correction amount and the carrier phase offset based on the rate of change of the real-time position deviation to obtain the adjusted signal delay correction amount and carrier phase offset includes:

[0017] Based on the historical sequence of the real-time position deviation, a weighted combination value of the instantaneous change rate and the average change rate of the real-time position deviation is calculated as a deviation dynamic trend value;

[0018] Decomposing the direction of the deviation dynamic trend into an orthogonal component and a tangential component of the satellite signal propagation direction, and converting the orthogonal component and the tangential component into inverse adjustment factors of the signal delay correction amount and the carrier phase offset amount;

[0019] Calculating a joint adjustment weight coefficient according to the modulus of the real-time position deviation and the deviation weight coefficient, and weighting the reverse adjustment factor according to the joint adjustment weight coefficient to obtain a weighted reverse adjustment factor;

[0020] Dynamically adjusting the gain coefficient of the weighted reverse adjustment factor according to the residual of the signal delay correction amount and the carrier phase offset amount to obtain an adjusted gain coefficient;

[0021] The weighted reverse adjustment factor and the adjusted gain coefficient are added to the signal delay correction amount and the carrier phase offset to generate an adjusted signal delay correction amount and a carrier phase offset.

[0022] Optionally, dynamically adjusting the gain coefficient of the weighted reverse adjustment factor according to the residual of the signal delay correction amount and the carrier phase offset to obtain the adjusted gain coefficient includes:

[0023] Calculating the difference between the actual update amount and the expected update amount of the signal delay correction as the delay residual, and the difference between the actual update amount and the expected update amount of the carrier phase offset as the phase residual;

[0024] Calculating a delay residual trend factor and a phase residual trend factor according to the instantaneous change rate and cumulative change of the delay residual and the phase residual in consecutive iteration cycles;

[0025] The delay residual trend factor and the phase residual trend factor are integrated in a preset ratio to obtain a joint residual trend quantity, and a gain correction factor is generated according to the direction and modulus of the joint residual trend quantity;

[0026] Based on the modulus of the weighted reverse adjustment factor in the current iteration cycle, the gain correction factor is piecewise linearly scaled to obtain an adjusted gain coefficient.

[0027] Optionally, at the receiving end of the UAV, superimposing the multi-mode forged navigation signal with the multi-mode satellite signal corresponding to the original signal parameters to generate a decoy navigation trajectory includes:

[0028] Based on the carrier phase in the original signal parameters, the main polarization direction parameters of the drone receiving antenna are extracted, and the incident angle adjustment boundary is determined based on the dynamic fluctuation of the signal strength distribution;

[0029] Performing polarization orthogonal decomposition on the multi-mode forged navigation signal to generate a polarization weighting coefficient that matches the main polarization direction parameter;

[0030] Calculating a power balancing coefficient between the multi-mode forged navigation signal and the corresponding multi-mode satellite signal based on the dynamic fluctuation of the signal strength distribution, and dynamically scaling the transmit power of the multi-mode forged navigation signal based on the power balancing coefficient to obtain a balanced transmit power;

[0031] Within the incident angle adjustment boundary, the polarization weighting coefficient, the equalized transmit power and the multi-mode satellite signal are superimposed to generate a decoy navigation trajectory.

[0032] Optionally, performing polarization orthogonal decomposition on the multi-mode forged navigation signal to generate a polarization weighting coefficient matching the main polarization direction parameter includes:

[0033] Generate a polarization decomposition reference coordinate system according to the polarization angle and ellipticity of the main polarization direction parameters described in the original signal parameters;

[0034] Projecting the polarization components of the multi-mode forged navigation signal onto the polarization decomposition reference coordinate system, and decomposing the polarization components in the polarization decomposition reference coordinate system into a main polarization component and a cross-polarization component;

[0035] calculating a polarization angle error between the main polarization component and the main polarization direction parameter, generating an initial polarization adjustment amount based on the polarization angle error, and performing gain compensation on the main polarization component according to the cross-polarization component to obtain a gain compensation amount;

[0036] The initial polarization adjustment amount is superimposed on the gain compensation amount to generate a polarization weighting coefficient that matches the main polarization direction parameter.

[0037] Optionally, the dynamically compensating the three-dimensional coordinate increment of the navigation message based on the signal delay correction amount and the carrier phase offset in the corrected forged navigation signal parameters and in combination with the real-time position deviation to generate a multi-mode forged navigation signal covering the drone receiving end includes:

[0038] Projecting the signal delay correction amount along the satellite signal propagation direction into the pseudorange domain to generate a pseudorange adjustment amount, and calculating the pseudorange differential amount in combination with the carrier phase offset;

[0039] According to the ratio of the orthogonal component to the tangential component of the real-time position deviation, the pseudorange adjustment amount and the pseudorange differential amount are dynamically weighted to generate an adjustment amount of the three-dimensional coordinate increment;

[0040] Based on the satellite geometric distribution of the navigation message received by the UAV, the adjustment amount of the three-dimensional coordinate increment is decomposed into the satellite line of sight direction to generate the dynamic distribution weight of the satellite pseudorange correction amount;

[0041] Performing spatial orthogonal decomposition on the satellite pseudorange correction amount according to the dynamic allocation weight to generate a pseudorange compensation amount that matches the satellite signal propagation direction;

[0042] The pseudo-range compensation amount is converted into a three-dimensional coordinate increment of the navigation message, and the incremental direction is dynamically biased in combination with the change rate of the real-time position deviation amount to generate a multi-mode forged navigation signal covering the UAV receiving end.

[0043] In a second aspect, the present application provides a drone navigation deception system based on multi-mode satellite signal forgery, comprising:

[0044] An acquisition module collects raw signal parameters of the multi-mode satellite signal in the current navigation link of the UAV in the target area, including signal delay, carrier phase, and signal strength distribution;

[0045] an extraction module that extracts the spatiotemporal characteristics of the multimode satellite signal based on the signal delay and carrier phase in the original signal parameters, and integrates the dynamic changes of the signal strength distribution to generate forged navigation signal parameters synchronized with the original signal parameters in the time domain;

[0046] a correction module, which dynamically corrects the forged navigation signal parameters according to a real-time position deviation between the preset flight path of the UAV and the decoy target path to obtain corrected forged navigation signal parameters;

[0047] A generation module dynamically compensates for the three-dimensional coordinate increment of the navigation message based on the signal delay correction and carrier phase offset in the corrected forged navigation signal parameters and in combination with the real-time position deviation, thereby generating a multi-mode forged navigation signal covering the receiving end of the drone;

[0048] The superposition module superimposes the multi-mode forged navigation signal with the multi-mode satellite signal corresponding to the original signal parameters at the receiving end of the UAV to generate a decoy navigation trajectory.

[0049] In a third aspect, the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a drone navigation spoofing method based on multi-mode satellite signal forgery as described in the first aspect above.

[0050] In a fourth aspect, the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a drone navigation spoofing method based on multi-mode satellite signal forgery as described in the first aspect.

[0051] In the present application, original signal parameters of a multimode satellite signal in a current navigation link of a UAV in a target area are collected, the original signal parameters including signal delay, carrier phase and signal strength distribution; spatiotemporal characteristics of the multimode satellite signal are extracted based on the signal delay and carrier phase in the original signal parameters, and the dynamic changes of the signal strength distribution are integrated to generate forged navigation signal parameters synchronized with the original signal parameters in the time domain; the forged navigation signal parameters are dynamically corrected according to the real-time position deviation between the preset flight path of the UAV and the decoy target path to obtain corrected forged navigation signal parameters; based on the signal delay correction and the offset of the carrier phase in the corrected forged navigation signal parameters, and in combination with the real-time position deviation, the three-dimensional coordinate increment of the navigation message is dynamically compensated to generate a multimode forged navigation signal covering the UAV receiving end; at the UAV receiving end, the multimode forged navigation signal is superimposed with the multimode satellite signal corresponding to the original signal parameters to generate a decoy navigation trajectory.

[0052] The technical solution of this application has the following beneficial effects:

[0053] This application obtains the core parameters of the multi-mode satellite signal in the UAV navigation link in real time to provide dynamic benchmark data for subsequent signal forgery, ensuring the compatibility of the forged signal with the original signal in terms of underlying features. Through the dynamic fusion of spatiotemporal feature extraction and signal strength, forged navigation parameters synchronized in the time domain are generated to avoid the verification alarm of the UAV navigation system caused by the timestamp misalignment. The forged parameters are feedback-adjusted based on the real-time position deviation to achieve dynamic matching between the decoy path and the UAV motion state, and maintain the spatiotemporal continuity of the forged signal. Combined with the three-dimensional coordinate incremental compensation technology, the corrected parameters are converted into pseudo-range and phase offsets in the propagation direction of the multi-mode satellite signal to ensure that the spatial distribution of the forged signal is consistent with the original satellite geometry. Through polarization matching and power balancing control, the forged signal is covertly superimposed on the original signal stream, and the navigation algorithm is used to analyze the synthetic signal indiscriminately to induce the UAV to generate a deceptive trajectory that deviates from the preset path.

[0054] Furthermore, through the dynamic weight compensation mechanism, polarization matching optimization and three-dimensional spatial signal decomposition technology, the core problems such as poor spatiotemporal synchronization of multi-mode signals, easy exposure of polarization mismatch, and inaccurate pseudo-range increment allocation were collaboratively solved. Ultimately, the covert fusion of forged signals in multiple dimensions of time, frequency, space and polarization was achieved, enabling the UAV navigation system to continuously analyze the deviated coordinates in an unaware state, achieving a highly concealed and robust trajectory deception effect, avoiding triggering spectrum anomaly alarms or redundant verification mechanisms, and significantly improving the success rate of intercepting illegal UAVs.

[0055] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0057] Figure 1 A flowchart of a drone navigation spoofing method based on multi-mode satellite signal forgery provided by the present application is shown;

[0058] Figure 2 The present invention provides a schematic diagram of a UAV navigation deception system based on multi-mode satellite signal forgery;

[0059] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0060] In order to enable people skilled in the art to better understand the technical solution of this application, the technical solution of this application will be clearly and completely described below in conjunction with the drawings in this application.

[0061] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0062] Current satellite navigation spoofing techniques for drones primarily rely on single-mode signal deception. This involves reverse-engineering a single navigation system protocol to generate a static counterfeit signal, which is then overwhelmed by power suppression. However, this approach has significant limitations: First, it cannot simultaneously counterfeit multi-mode signals such as those from the Beidou and global navigation satellite systems, making it difficult to bypass the multi-mode redundancy check mechanisms of modern drones. Second, the counterfeit signal parameters are fixed, lacking dynamic feedback on the drone's real-time motion state, making it susceptible to triggering anomaly detection due to interruptions in spatiotemporal continuity. Third, the reliance on high-power signal suppression makes it susceptible to capture by spectrum sensing modules, resulting in insufficient concealment and a high rate of deception failure.

[0063] In response to the above-mentioned defects, this application proposes a UAV navigation deception method based on dynamic counterfeiting of multi-mode satellite signals. This method collects the original parameters of the multi-mode satellite signals in the target area in real time, extracts the spatiotemporal characteristics and fuses the dynamic intensity fluctuations to generate time-domain synchronized counterfeit signal parameters; further, according to the real-time position deviation between the preset path of the UAV and the decoy target, it dynamically corrects the time delay, phase offset and three-dimensional coordinate increment, and combines polarization matching and power balancing technology to covertly superimpose the counterfeit signal on the real signal stream. Compared with the existing technology, this solution systematically solves the problems of poor multi-mode compatibility, insufficient dynamic adaptability and concealment defects through three core means: synchronous generation of multi-mode signals, dynamic parameter feedback compensation and multi-dimensional signal fusion. It can achieve high-precision trajectory induction without the perception of the UAV, significantly improving the defense effectiveness of sensitive airspace.

[0064] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of this application.

[0065] Figure 1 The present invention provides a flowchart of a method for spoofing a drone navigation system based on multi-mode satellite signal forgery. Figure 1 As shown, the method includes:

[0066] 101. Collect original signal parameters of the multi-mode satellite signal in the current navigation link of the UAV in the target area, wherein the original signal parameters include signal delay, carrier phase, and signal strength distribution;

[0067] In this step, the target area refers to the key combat or defense area where the UAV needs to be deceiving its navigation.

[0068] Multimode satellite signals refer to a collection of composite signals from multiple satellite navigation systems.

[0069] The original signal parameters are the underlying characteristic data of the satellite signal obtained in real time by the drone receiver.

[0070] Signal delay refers to the time difference in the propagation of satellite signals from the transmitter to the drone receiver, reflecting the relative distance between the satellite and the drone.

[0071] Carrier phase is the phase value of the satellite signal carrier waveform at the time of reception and is used for high-precision positioning.

[0072] Signal strength distribution is the spatial distribution of the power intensity of each satellite signal at the drone receiving end, which is related to the satellite elevation angle and occlusion environment.

[0073] In this embodiment, a wideband radio frequency front-end (RFF) synchronously receives frequency bands of multimode satellite signals and uses multi-channel digital down-conversion technology to separate the individual satellite signals. Time delay estimation, carrier phase tracking, and signal strength distribution measurements are performed for each satellite signal. Signal parameters are updated in real time based on the drone's trajectory, creating a spatiotemporal feature matrix.

[0074] In a scenario where an enemy drone attempted to intrude into the airspace of a military base to conduct reconnaissance, we deployed multiple ground monitoring nodes outside the base to monitor and record the raw signal parameters of the navigation satellite signals used by the drone in real time, including signal delay, carrier phase, and signal strength distribution. This provided the basic data for generating a forged signal.

[0075] 102. Extracting spatiotemporal characteristics of the multimode satellite signal based on signal delay and carrier phase in the original signal parameters, and integrating dynamic changes in the signal strength distribution to generate forged navigation signal parameters synchronized with the original signal parameters in the time domain;

[0076] In this step, the spatiotemporal features are the satellite spatial geometry and signal propagation path characteristics calculated from time delay and phase.

[0077] Dynamic changes refer to inferring environmental occlusion changes based on signal strength fluctuations and dynamically adjusting the parameters of the forged navigation signal.

[0078] Forged navigation signal parameters refer to false signal characteristic data generated based on original signal parameters.

[0079] In this embodiment, the signal delay and carrier phase are input into an extended Kalman filter to estimate the pseudorange carrier phase observation equations for satellites and drones, outputting a satellite spatial position matrix. A wavelet transform is used to decompose the signal strength time series data and extract occlusion attenuation characteristics. Based on the spatiotemporal model and the dynamic characteristics of the signal strength distribution, a linear time-varying system is used to generate forged navigation signal parameters that are aligned with the timestamps of the original signal parameters.

[0080] In a military base defense scenario, the system synthesizes a set of fake navigation signal parameters that are highly similar to the real path but slightly offset based on the original signal parameters collected in the early stage, causing the drone to mistakenly believe that it is flying along a safe route and thus deviate from the actual attack direction.

[0081] 103. Dynamically correct the forged navigation signal parameters based on a real-time position deviation between the preset flight path of the UAV and the decoy target path to obtain corrected forged navigation signal parameters;

[0082] In this step, the preset flight path is the flight trajectory pre-planned by the drone operator and is usually stored in the flight control system in the form of a waypoint sequence.

[0083] A decoy target path refers to a false trajectory that the defender expects the drone to follow after deviating from the original path.

[0084] The real-time position deviation refers to the Euclidean distance difference between the current position of the UAV and the preset decoy path, which serves as the feedback control input.

[0085] In this embodiment, the real-time trajectory of the drone's preset flight path is timestamped and aligned with the expected trajectory of the decoy target path, and the real-time position deviation at each moment is calculated. This real-time position deviation is decomposed by satellite line of sight and converted into a pseudorange correction. The signal delay and carrier phase offset are dynamically adjusted based on the rate of change of the deviation to obtain the corrected parameters of the spoofed navigation signal.

[0086] During the base defense mission, when the enemy drone gradually approaches the core area of ​​the base, the system detects a significant deviation between its preset flight path and the expected decoy path, and then adjusts the parameters of the forged navigation signal to make the drone mistakenly believe that it has deviated from the original course, and thus automatically turns away from the sensitive area.

[0087] 104. Based on the signal delay correction and carrier phase offset in the corrected forged navigation signal parameters, and in combination with the real-time position deviation, dynamically compensate the three-dimensional coordinate increment of the navigation message to generate a multi-mode forged navigation signal covering the UAV receiving end;

[0088] In this step, the signal delay correction is an adjustment value artificially added to the original signal delay to match the decoy target path requirements.

[0089] Carrier phase offset refers to the adjustment value artificially applied to the phase of the carrier signal to precisely induce position drift.

[0090] Navigation messages are binary data frames embedded in satellite signals, containing information such as satellite orbit parameters, timestamps, and health status.

[0091] The three-dimensional coordinate increment is the three-dimensional component of the drone position offset in the geocentric coordinate system that the fake signal needs to induce.

[0092] In this embodiment, the real-time deviation is combined with the drone's motion model to predict the 3D coordinate increments within the next three seconds. The satellite's geometric dilution of precision is used to calculate the pseudorange correction weights for each satellite. The corrected pseudorange / phase parameters are input into a navigation message generator, generating a multi-mode forged navigation signal that is transmitted via the RF front end.

[0093] In a scenario involving defending the skies above a military base, the coordinates in the navigation message continuously shifted as the spoofed signal continuously updated, allowing the drone to consistently appear in the correct area. Even when its physical location approached the base's boundaries, it was unaware that it had been misled off its intended route. This continuous incremental adjustment of the coordinates ensured that the drone remained on the intended decoy path.

[0094] 105. At the receiving end of the UAV, the multi-mode forged navigation signal is superimposed with the multi-mode satellite signal corresponding to the original signal parameters to generate a decoy navigation trajectory.

[0095] In this step, the multi-mode forged navigation signal is a set of forged signals covering multiple satellite systems.

[0096] A decoy navigation trajectory refers to a false flight path calculated by the navigation module after the drone receives a forged signal.

[0097] In this embodiment, the main polarization direction of the drone's antenna pattern is determined by testing its antenna pattern. Based on this main polarization direction, the forged navigation signal is polarization-decomposed to generate a matching coefficient. The original signal strength distribution is monitored, and the transmit power of the forged navigation signal is dynamically adjusted. Based on the matching coefficient, the forged navigation signal is coupled with the original signal over the air interface. The resulting signal is then analyzed at the drone's receiving end to generate a decoy navigation trajectory.

[0098] When defending the air above a certain military base, the drone is made to continuously receive fake signals, misjudge its own position, adjust the polarization matching coefficient of the fake navigation signal, and control the transmission power of the fake navigation signal. After superimposing multi-mode satellite signals through a combiner, the drone navigation module parses the coordinates of the fake navigation signal and continuously induces it to fly away.

[0099] In summary, steps 101 to 105 collect original signal parameters to provide a baseline for forged navigation signal generation, synchronize parameters in the time domain, and ensure signal compatibility. Dynamic deviation feedback correction maintains decoy path continuity, and polarization power coordinated control conceals the superimposed signal. Ultimately, this achieves the core benefits of seamless multi-mode signal fusion, precise dynamic trajectory guidance, and zero alarm triggering. Compared to traditional single-mode deception solutions, this significantly improves the success rate of drone interception and shortens the average decoy response time.

[0100] To address the issue of traditional forged signal parameters being rigid and unable to adapt to the dynamic motion of drones, this solution obtains a sequence of trajectory points between the drone's preset path and the decoy target, calculates the real-time position deviation, and maps it to a deviation weight coefficient. This deviation is then projected into the pseudorange domain along the satellite signal propagation direction to generate an initial delay correction and phase offset. This is then combined with the deviation change rate for reverse joint adjustment, and the correction parameters are dynamically compensated using historical accumulations. In some embodiments, the method of dynamically correcting the forged navigation signal parameters based on the real-time position deviation between the drone's preset flight path and the decoy target path in step 103 to obtain the corrected forged navigation signal parameters includes:

[0101] 201. Obtain a real-time trajectory point sequence of a preset flight path of the UAV and an expected trajectory point sequence of a decoy target path, and calculate a real-time position deviation between the real-time trajectory point sequence and the expected trajectory point sequence at corresponding timestamps as a deviation weight coefficient;

[0102] In step 201, the real-time trajectory point sequence is a set of continuous position points along the drone's actual flight path, acquired in real time via radar or Automatic Dependent Surveillance-Broadcast (ADS) signals. The desired trajectory point sequence is a set of preset coordinate points along the decoy target path, aligned with the real-time trajectory by timestamp. The real-time position deviation is the Euclidean distance difference between the real-time position and the desired position at the same timestamp. The deviation weight coefficient is a normalized deviation value used to control the magnitude of parameter corrections.

[0103] In this embodiment, a set of continuous position points along the drone's current flight path is first acquired in real time using a global positioning system or radar tracking device, recorded as a real-time trajectory point sequence. Simultaneously, a set of coordinate points along a pre-set decoy target path is read, recorded as a desired trajectory point sequence. An interpolation algorithm is used to synchronize the real-time and desired trajectories, ensuring a consistent time base for deviation calculations. Subsequently, a three-dimensional Euclidean distance is used to calculate the precise real-time position deviation. Finally, a nonlinear function mapping is used to generate a deviation weight coefficient.

[0104] 202. Project the real-time position deviation into a pseudo-range domain along the satellite signal propagation direction, and construct a signal delay correction and a carrier phase offset of the forged navigation signal parameters in combination with the deviation weight coefficient;

[0105] In step 202, signal propagation direction projection involves decomposing the position deviation into the direction of the satellite-UAV connection and calculating the pseudorange correction component along that direction. The pseudorange domain is the computational domain that converts spatial range deviations into satellite signal propagation delay variations.

[0106] In an embodiment of the present application, first, the spatial position deviation is converted into an equivalent offset of the signal propagation path by projecting the satellite line of sight direction. Subsequently, the initial signal delay correction and the initial carrier phase offset are generated based on the relationship between the speed of light and the carrier frequency. Finally, dynamic scaling of the signal delay correction and the carrier phase offset is achieved by multiplying the delay correction and the phase offset by the deviation weight coefficient respectively.

[0107] 203. Based on the rate of change of the real-time position deviation, perform reverse joint adjustment on the signal delay correction and the carrier phase offset to obtain adjusted signal delay correction and carrier phase offset;

[0108] In step 203, the rate of change refers to the speed at which the position deviation changes over time, reflecting the drone's motion trend. Reverse joint adjustment dynamically reduces the correction amount based on the rate of change to prevent signal jumps caused by excessive correction.

[0109] In the embodiment of the present application, first, the rate of change of the real-time position deviation is obtained by differential calculation, and secondly, the proportional-integral-differential controller is used to generate a reverse adjustment factor. Subsequently, the signal delay correction amount and the carrier phase offset are respectively multiplied by the reverse joint adjustment factor to obtain the adjusted signal delay correction amount and carrier phase offset; finally, the rationality of the output parameters is ensured by dynamic limiting, so as to realize adaptive and stable adjustment of the correction amount.

[0110] 204. Dynamically compensate the adjusted signal delay correction amount and carrier phase offset amount using the historical accumulation of the deviation weight coefficient to generate corrected forged navigation signal parameters;

[0111] In step 204, the historical accumulation refers to the moving average of the deviation weight coefficients within the past time window, which is used to smooth transient disturbances. Dynamic compensation is a real-time feedback adjustment mechanism that analyzes the correlation between historical data and the current state to adaptively adjust system parameters to offset the impact of external interference or internal error accumulation on system stability. The fake navigation signal parameters are artificially generated datasets of false satellite signal characteristics used to simulate the spatial propagation characteristics of real satellite signals, inducing the drone navigation system to calculate erroneous position information.

[0112] In an embodiment of the present application, first, the historical cumulative amount is extracted through a moving average algorithm to reflect the long-term trend of the deviation weight. Secondly, the historical cumulative amount is weighted and fused with the deviation weight coefficient at the current moment to generate a dynamic compensation factor. Subsequently, the compensation factor is applied to the signal delay and carrier phase correction amount, and the compensated signal delay correction amount and the carrier phase offset amount are threshold-checked. The maximum allowable range of the delay correction amount and the circular period constraint of the phase offset amount are set. If the parameter exceeds the threshold, it is truncated according to the boundary value, and finally the corrected forged navigation signal parameters that comply with the satellite navigation protocol specifications are output.

[0113] Here's a specific example:

[0114] To defend against drones flying over a military base, they must be induced to deviate westward to a safe area. The drone's actual flight trajectory is collected in real time and compared with the hypothetical path, calculating the deviation at each moment. This deviation is then mapped to the pseudorange domain along the propagation direction of the visible satellites, generating the delay and phase offset parameters of the forged signal. To address sudden trajectory drift, the system also performs dynamic reverse adjustments based on the deviation trend to avoid obvious trajectory jumps. At the same time, considering that long-term operation may lead to accumulated deviations, the system introduces a weighted cumulative amount of historical deviations to re-compensate the parameters, ensuring that the forged signal remains highly realistic. The drone continuously analyzes the westward deviation coordinates and eventually deviates from the original path without triggering any abnormal alarms.

[0115] In summary, steps 201 to 204 utilize precise feedback control, based on dual adjustments of real-time position deviation and its rate of change, to avoid signal jumps caused by overshoot. Historical accumulation compensation smooths transient interference, improves parameter stability, and optimizes stealth. Reverse regulation suppresses power surges, reducing fluctuations in forged signal strength and circumventing spectrum monitoring. Ultimately, this achieves the core benefits of centimeter-level deception accuracy, second-level dynamic response, and imperceptible trajectory takeover, significantly extending the duration of drone deception compared to traditional static deception schemes.

[0116] In order to solve the problems of dynamic response hysteresis and poor parameter stability in traditional reverse adjustment methods, the scheme introduces a deviation dynamic trend calculation and spatial decomposition mechanism. Based on the historical sequence of real-time position deviation, the weighted combination value of the instantaneous change rate and the average change rate is extracted as the trend quantity, which is decomposed into the orthogonal and tangential components of the satellite signal propagation direction, and converted into a reverse adjustment factor for the delay and phase offset. The weight coefficient and the residual dynamic adjustment gain coefficient are further adjusted to achieve refined control of the correction amount. In some embodiments, the signal delay correction amount and the carrier phase offset amount are reversely jointly adjusted based on the rate of change of the real-time position deviation amount in step 203 to obtain the adjusted signal delay correction amount and carrier phase offset, including:

[0117] 301. Based on the historical sequence of the real-time position deviation, calculate a weighted combination value of the instantaneous change rate and the average change rate of the real-time position deviation as a deviation dynamic trend value;

[0118] In step 301, the historical sequence of real-time position deviations refers to a collection of real-time position deviations recorded in chronological order for multiple consecutive moments. The instantaneous rate of change is the rate of change of the current position deviation relative to the previous moment. The average rate of change is the average rate of change of the position deviation within a preset time window. The deviation dynamic trend is a comprehensive trend indicator that is a combination of the instantaneous rate of change and the average rate of change according to a preset weight ratio.

[0119] In this embodiment, a sliding window is first used to extract a historical sequence of real-time position deviations. The central difference method is then used to differentiate the real-time position deviations at adjacent moments to obtain the instantaneous rate of change. Linear regression analysis is then performed on the deviations at all moments in the historical sequence to extract the average rate of change. Finally, a linear weighted summation is performed according to a pre-defined weight ratio to generate a dynamic trend value for the deviations, which serves as the basic input for subsequent signal conditioning.

[0120] 302. Decompose the direction of the deviation dynamic trend into an orthogonal component and a tangential component of the satellite signal propagation direction, and convert the orthogonal component and the tangential component into inverse adjustment factors of the signal delay correction and the carrier phase offset;

[0121] In step 302, the direction of the deviation dynamic trend refers to its vector direction in three-dimensional space. The satellite signal propagation direction is the spatial direction of the straight line from the satellite to the receiver. The orthogonal component is the projection of the deviation dynamic trend onto the satellite signal propagation direction and directly affects pseudorange correction. The tangential component is the component of the deviation dynamic trend perpendicular to the satellite signal propagation direction and affects carrier phase continuity. The reverse adjustment factor is a delay and phase correction suppression coefficient generated based on the orthogonal and tangential components.

[0122] In this embodiment, vector projection technology is used to decompose the deviation dynamic trend into orthogonal and tangential components based on the satellite line-of-sight direction cosine matrix. The tangential component influences the signal delay correction, while the quadrature component influences the carrier phase offset. These two components are input into corresponding functions. The quadrature component is input into an exponential decay function and mapped into an inverse adjustment factor for the delay correction. The tangential component is input into a sinusoidal modulation function and mapped into an inverse adjustment factor for the phase offset. This is converted into the corresponding inverse adjustment factor, ensuring that the signal error caused by the deviation can be effectively offset.

[0123] 303. Calculate a joint adjustment weight coefficient based on the modulus of the real-time position deviation and the deviation weight coefficient, and perform weighted processing on the reverse adjustment factor based on the joint adjustment weight coefficient to obtain a weighted reverse adjustment factor.

[0124] In step 303, the modulus of the real-time position deviation refers to the absolute value of the position deviation at the current moment, indicating the degree of deviation. The deviation weight coefficient is the weighting factor assigned based on the deviation magnitude in the previous step. The combined adjustment weight coefficient is a dynamic adjustment proportional factor combining the modulus and the deviation weight coefficient, used to dynamically weight the inverse adjustment factor.

[0125] In the embodiment of the present application, the modulus of the real-time position deviation is first divided by a preset maximum deviation threshold to obtain a normalized modulus coefficient. The normalized modulus coefficient is then multiplied by the deviation weight coefficient and divided by the sum of the two maximum possible values ​​to obtain a joint adjustment weight coefficient. The obtained reverse adjustment factor is then weighted according to the joint adjustment weight coefficient to obtain a weighted reverse adjustment factor.

[0126] 304. Dynamically adjust the gain coefficient of the weighted reverse adjustment factor according to the residual of the signal delay correction amount and the carrier phase offset amount to obtain an adjusted gain coefficient;

[0127] In step 304, the residual of the signal delay correction and carrier phase offset is the difference between the actual and theoretically expected values ​​of the signal delay correction and carrier phase offset. The gain factor is a proportional coefficient used to amplify or reduce the strength of the negative adjustment factor. It is dynamically adjusted based on the residual to improve system robustness and convergence speed.

[0128] In this embodiment, the system continuously monitors and compares the current delay correction and phase offset with the expected values ​​from the theoretical model, calculating the residuals. Large residuals indicate insufficient adjustment and require increasing the gain coefficient. Conversely, the gain coefficient is reduced to prevent overshoot. In a specific implementation, a sliding window variance analysis is performed on the residual sequence to extract a trend factor for the residual variation. Based on the direction and magnitude of the trend factor, the gain coefficient is dynamically adjusted to ensure rapid response without oscillation.

[0129] 305. Add the weighted reverse adjustment factor and the adjusted gain coefficient to the signal delay correction value and the carrier phase offset to generate an adjusted signal delay correction value and carrier phase offset.

[0130] In step 305, the weighted reverse adjustment factor refers to the signal correction parameter after processing the combined adjustment weight coefficients. The adjusted gain coefficient is a proportional factor that dynamically optimizes the adjustment strength. The adjusted signal delay correction and carrier phase offset are the key parameters ultimately used to generate the forged navigation signal.

[0131] In an embodiment of the present application, the gain coefficient and the weighted reverse adjustment factor are fused in a vector superposition manner, and the superposition result is applied to the signal delay correction and the carrier phase offset to generate the adjusted signal delay correction and the carrier phase offset. This process realizes a multi-dimensional and multi-level dynamic compensation mechanism, so that the forged navigation signal can more accurately guide the drone to fly along the predetermined trajectory.

[0132] Here's a specific example:

[0133] In a scenario involving defense over a military base, an illegal reconnaissance drone needs to be lured to a camouflaged position ten kilometers away. The deviation sequence between the drone's current position and the decoy path is detected, and the dynamic trend of the instantaneous and average rates of change is calculated. This deviation dynamic trend is then decomposed into orthogonal and tangential components of the satellite propagation direction, which are converted into inverse adjustment factors for signal delay and phase offset, respectively. Joint adjustment weights are further calculated to weight the inverse adjustment factors. Based on this, the gain coefficient is dynamically adjusted based on the actual corrected residual to avoid overshoot or undershoot. Finally, the adjusted signal parameters are injected into the forged navigation signal and superimposed on the original signal, tricking the drone into believing it is in normal flight and gradually luring it away from the military base, successfully completing the electronic deception and interception mission.

[0134] In summary, steps 301 to 305 accurately capture the drone's motion inertia by calculating the deviation trend, decouple signal delay from carrier phase control through orthogonal and tangential decomposition, enhance the robustness of parameter adjustment through joint weight fusion, adjust the gain coefficient based on the residual, and suppress the influence of environmental interference, ultimately generating highly concealed corrected forged navigation signal parameters. Compared to traditional single-dimensional adjustment schemes, this method improves the smoothness of the decoy trajectory and enhances anti-interference capabilities, making it suitable for high-precision navigation countermeasures in complex battlefield environments.

[0135] In order to solve the problem of parameter adjustment lag and insufficient anti-interference ability caused by the fixed traditional gain coefficient, the scheme is based on an adaptive gain adjustment method of delay and phase residual. By calculating the instantaneous change rate and cumulative change of the delay residual and the phase residual, a joint residual trend quantity is generated and mapped to a gain correction factor; piecewise linear scaling is performed according to the modulus value of the current adjustment factor to dynamically adjust the gain coefficient. In some embodiments, the gain coefficient of the weighted reverse adjustment factor is dynamically adjusted according to the residual of the signal delay correction amount and the carrier phase offset in step 304 to obtain the adjusted gain coefficient, including:

[0136] 401. Calculate the difference between the actual update amount and the expected update amount of the signal delay correction as the delay residual, and use the difference between the actual update amount and the expected update amount of the carrier phase offset as the phase residual;

[0137] In step 401, the actual update value of the signal delay correction refers to the actual adjustment value of the signal delay correction and carrier phase offset during the current iteration cycle. The expected update value is the target value of the delay and phase correction values ​​predicted based on a theoretical model or historical data. The difference between the two is the delay residual, which is used to measure the accuracy of delay adjustment. The delay residual is the difference between the actual and expected values ​​of the signal delay correction, reflecting the parameter adjustment error. The phase residual is the difference between the actual and expected values ​​of the carrier phase offset, reflecting the phase tracking error.

[0138] In this embodiment, within each control cycle, a satellite navigation signal propagation model is used to calculate theoretical delay and phase corrections. These are used as expected updates. The actual delay correction is subtracted from the expected value to obtain the delay residual. The carrier phase offset is processed similarly to obtain the phase residual. These residuals serve as the basis for subsequent trend analysis, assessing the effectiveness of the current correction strategy and guiding the next adjustment direction.

[0139] 402. Calculate a delay residual trend factor and a phase residual trend factor according to the instantaneous change rates and cumulative changes of the delay residual and the phase residual in consecutive iteration cycles;

[0140] In step 402, the instantaneous rate of change refers to the rate of change of the residual within adjacent iteration cycles. The cumulative change is the total accumulated offset of the residual within a preset time window. The delay residual trend factor and the phase residual trend factor are comprehensive trend indicators derived by weighted fusion of the instantaneous rate of change and the cumulative change of the delay residual and phase residual, respectively. They are used to quantify the direction and intensity of error changes.

[0141] In the embodiment of the present application, a sliding window method is used to extract the delay and phase residual sequences within multiple consecutive iteration cycles. First-order difference operations are performed on the delay residual and phase residual sequences to obtain the instantaneous rate of change. The residual sequence is summed up by a sliding window to obtain the cumulative change. Using a weighted average method, the instantaneous rate of change and the cumulative change are weightedly fused to generate a delay residual trend factor and a phase residual trend factor. This trend factor reflects the overall trend of the error evolution over time, providing a basis for subsequent joint analysis.

[0142] 403. Fusing the delay residual trend factor and the phase residual trend factor in a preset ratio to obtain a joint residual trend quantity, and generating a gain correction factor according to the direction and modulus of the joint residual trend quantity;

[0143] In step 403, the joint residual trend factor is a unified error trend vector obtained by fusing the delay and phase residual trend factors according to a preset ratio. It contains direction and modulus information. The gain correction factor is an adjustment coefficient generated based on the direction and magnitude of this trend factor and is used to dynamically optimize the gain strength during the signal correction process.

[0144] In this embodiment, the delay residual trend factor and the phase residual trend factor are first weighted and summed proportionally to generate a joint residual trend quantity. The joint residual trend quantity is then normalized, and its direction angle and modulus are extracted. The vector direction angle of the joint residual trend quantity is calculated to determine whether the trend is positive enhancement or negative suppression. The modulus of the joint residual trend quantity is input into a hyperbolic tangent function and mapped to a gain correction factor.

[0145] 404 : Based on the modulus of the weighted reverse adjustment factor in the current iteration cycle, perform piecewise linear scaling on the gain correction factor to obtain an adjusted gain coefficient.

[0146] In step 404, the modulus of the weighted reverse adjustment factor represents the strength of the signal correction parameter after the combined weighting process. Piecewise linear scaling scales the gain correction factor proportionally to different intervals based on the modulus of the current reverse adjustment factor. The adjusted gain coefficient is the key parameter ultimately used to control the amplitude of the signal correction.

[0147] In the embodiment of the present application, the system first calculates the modulus of the weighted reverse adjustment factor within the current cycle, divides the modulus of the reverse adjustment factor into three intervals: low, medium, and high. Each interval corresponds to a different scaling ratio, and the corresponding scaling interval is selected. For example, if the modulus is small, high gain scaling is used to speed up the response; if the modulus is large, low gain scaling is used to prevent oscillation. The gain correction factor is scaled based on the interval to which the modulus value belongs, and the adjusted gain coefficient is output.

[0148] Here's a specific example:

[0149] To defend against reconnaissance drones over a military base, the system must covertly decoy them away from the core area. An electronic decoy system is activated, continuously monitoring the multimode satellite signals it receives and dynamically generating a forged navigation signal based on its flight trajectory. As the drone approaches, its position is detected to gradually deviate from the predetermined decoy path, triggering a deviation adjustment mechanism. Furthermore, the system incorporates a residual analysis module to calculate the error residuals after signal delay and carrier phase correction in real time and generate a trend factor based on this. By integrating the trend direction and strength, the system dynamically adjusts the gain coefficient of the signal correction, ensuring that the forged signal remains highly realistic. Even if the drone attempts to correct itself, the system quickly identifies it and redirects it, misjudging it to a safe route and away from the core area. Throughout this process, the system's gain adjustment mechanism effectively avoids abnormal fluctuations caused by sudden signal changes, ensuring the stealth and stability of the deception process.

[0150] In summary, steps 401 to 404, through a residual-driven dynamic gain adjustment mechanism, systematically optimize the parameter adaptability during drone navigation deception. Based on the quantitative calculation of time delay and phase residuals, the error distribution characteristics during parameter adjustment are accurately captured, providing a data foundation for subsequent trend analysis. The multi-dimensional fusion of the residual instantaneous rate of change and the cumulative change effectively identifies the coupled effects of environmental interference and system deviations. The gain correction factor achieves a dynamic balance between error suppression and parameter stability. A piecewise linear scaling strategy is used to dynamically adjust the gain coefficient, ensuring appropriate adaptation of the adjustment strength in different scenarios.

[0151] In order to solve the problems of polarization mismatch and power anomaly exposure during the superposition of forged signals, the solution performs polarization orthogonal decomposition of the forged signal and generates a matching polarization weighting coefficient based on the main polarization direction parameters of the drone receiving antenna. At the same time, the power balancing coefficient is dynamically calculated according to the signal strength distribution to control the relative relationship between the forged signal transmission power and the real signal. Through the dual constraints of polarization matching and power balancing, the forged signal is covertly superimposed on the real signal stream to circumvent the spectrum perception and polarization detection mechanism. In some embodiments, at the receiving end of the drone as described in step 105, the multi-mode forged navigation signal is superimposed with the multi-mode satellite signal corresponding to the original signal parameters to generate a decoy navigation trajectory, including:

[0152] 501. Extracting the main polarization direction parameter of the UAV receiving antenna based on the carrier phase in the original signal parameter, and determining the incident angle adjustment boundary based on the dynamic fluctuation of the signal strength distribution;

[0153] In step 501, the carrier phase parameter in the original signal refers to the offset of the carrier waveform transmitted from the satellite to the receiver. The main polarization parameter refers to the maximum response angle of the drone's receiving antenna to the polarization direction of the electromagnetic wave, including the polarization angle and ellipticity. The incident angle adjustment boundary refers to the range of elevation and azimuth angles within which the incident direction of the forged signal can be adjusted, which is determined by the signal strength fluctuation threshold.

[0154] In this embodiment, the polarization characteristics of the received multimode satellite signal are first analyzed, and the main polarization direction of the current receiving antenna is extracted using the spatial vector relationship of the carrier phase. The phase difference sequence is fitted with an elliptical polarization model using the least squares method, and the main polarization direction parameters are output. Subsequently, the signal intensity distribution is subjected to wavelet decomposition to extract the dynamic fluctuation characteristics caused by occlusion, and the incident angle adjustment boundary is set, providing a basis for polarization matching and angle control of the subsequent forged navigation signal.

[0155] 502. Perform polarization orthogonal decomposition on the multi-mode forged navigation signal to generate a polarization weighting coefficient that matches the main polarization direction parameter;

[0156] In step 502, polarization orthogonal decomposition decomposes the electromagnetic wave of the spurious signal into components parallel and perpendicular to the main polarization direction. The polarization weighting coefficient is based on the amplitude ratio of the main polarization component to maximize signal reception efficiency and increase the energy ratio of the spurious signal in the main polarization direction.

[0157] In this embodiment of the present application, an orthogonal polarization decomposition coordinate system is established with the main polarization direction as the reference axis. The polarization components of the forged navigation signal are projected onto the reference coordinate system, separating the main polarization component from the cross-polarization component. The ratio of the main polarization component power to the total power is calculated as the polarization weighting coefficient. This coefficient is used in the subsequent signal synthesis stage to improve the polarization consistency of the forged signal and the real signal, enhancing their fusion.

[0158] 503. Calculate a power balancing coefficient between the multi-mode forged navigation signal and the corresponding multi-mode satellite signal based on the dynamic fluctuation of the signal strength distribution, and dynamically scale the transmit power of the multi-mode forged navigation signal based on the power balancing coefficient to obtain a balanced transmit power.

[0159] In step 503, the power equalization coefficient is a dynamic adjustment factor for the power ratio of the forged signal to the real signal, which is used for covert superposition. Dynamic scaling refers to adjusting the forged signal transmission power in real time according to the fluctuation of the environmental signal strength.

[0160] In this embodiment of the present application, the instantaneous power values ​​of multimode satellite signals are collected and the average power fluctuation range is calculated. Subsequently, the power of the multimode counterfeit navigation signal is set to a preset ratio of the average power of the real signal, generating a power equalization coefficient. A digitally controlled attenuator scales the power of the multimode counterfeit navigation signal according to the equalization coefficient, outputting the equalized transmit power. Finally, this equalization coefficient is applied to the transmit module of the counterfeit signal, enabling adaptive adjustment of the transmit power, bringing it closer to the real signal strength at the receiving end and reducing the risk of it being identified as an abnormal signal.

[0161] 504. Within the incident angle adjustment boundary, superimpose the polarization weighting coefficient, the equalized transmit power, and the multi-mode satellite signal to generate a decoy navigation trajectory.

[0162] In step 504, the angle of incidence adjustment boundary refers to the allowable range of elevation and azimuth angles in the direction of the spoofed signal transmission, ensuring that the beamwidth of the receiving antenna is not exceeded. The polarization weighting coefficient is used to increase the effective gain of the spoofed signal in the primary polarization direction. Equalized transmit power ensures power consistency between the spoofed signal and the real signal. The decoy navigation trajectory is a false flight path generated by the drone due to receiving mixed signals.

[0163] In this embodiment, within a set range of incident angles, a polarization weighting coefficient is multiplied by the forged navigation signal to enhance the signal strength in the primary polarization direction. Using digital radio frequency synthesis technology, the processed forged navigation signal is spatially superimposed with the original satellite signal at an equalized transmit power. Due to consistent polarization, power matching, and proper incidence, the drone is unable to distinguish between genuine and fake signals. The drone's receiver analyzes the synthesized signal and outputs a navigation trajectory that deviates from the preset path, away from critical areas.

[0164] Here's a specific example:

[0165] To defend against reconnaissance drones over a military base, the system must induce them to deviate from the core area southwestward. This system monitors the active multimode navigation signal. The system analyzes the signal carrier phase and identifies the primary polarization of the receiving antenna. This information is then used to set the polarization matching parameters for the forged navigation signal. Simultaneously, the transmit power of the forged navigation signal is continuously adjusted based on dynamic fluctuations in signal strength, ensuring consistency with the original signal at the receiving end. Within the specified angle of incidence, the forged navigation signal is then polarization-matched and power-balanced with the original signal before being transmitted superimposed. The drone misjudges its position and gradually deviates from the core area, successfully achieving electronic jamming and trajectory deflection.

[0166] In summary, steps 501 to 504 achieve the covert over-the-air fusion of the forged signal and the real signal. Through carrier phase analysis and polarization model fitting, the main polarization parameters of the drone's receiving antenna are accurately extracted, ensuring that the polarization characteristics of the forged signal match the maximum response direction of the receiver, reducing polarization loss and effectively suppressing the risk of signal leakage caused by cross-polarization components. Dynamic adjustment of the transmit power based on signal strength fluctuations synchronizes the forged signal power with real-world fluctuations, avoiding spectral anomaly detection. Finally, multi-dimensional parameter coordination is achieved within the incident angle adjustment boundary, ensuring that the drone's navigation system can indiscriminately interpret the synthesized signal.

[0167] In order to solve the signal attenuation and exposure risks caused by insufficient polarization matching accuracy, the solution establishes a reference coordinate system based on the polarization angle and ellipticity of the main polarization direction parameters, and decomposes the forged signal into the main polarization component and the cross-polarization component. The initial adjustment amount is generated by calculating the angle error of the main polarization component, and the cross-polarization component is used for gain compensation. This method eliminates polarization residual interference through component compensation, so that the polarization characteristics of the forged signal are highly matched with the receiving antenna, reducing the probability of being identified by the anti-deception mechanism. In some embodiments, the polarization orthogonal decomposition of the multi-mode forged navigation signal described in step 502 to generate a polarization weighting coefficient that matches the main polarization direction parameter includes:

[0168] 601. Generate a polarization decomposition reference coordinate system according to the polarization angle and ellipticity of the main polarization direction parameters in the original signal parameters;

[0169] In step 601, the main polarization direction parameter refers to the direction in which the current receiving antenna has the strongest signal response. The polarization angle is the angle between the major axis of the electromagnetic wave's electric field vector and the reference direction, representing the polarization direction. The ellipticity is the ratio of the minor axis to the major axis of the polarization ellipse, describing the flattening of the polarization ellipse. The polarization decomposition reference coordinate system is an orthogonal coordinate system constructed with the main polarization direction as the reference axis, used to decompose the signal's polarization components.

[0170] In this embodiment, the primary polarization parameters of the currently received signal are first extracted based on the carrier phase and signal strength data in the original signal parameters. The polarization angle and ellipticity values ​​are then extracted from the primary polarization parameters. The direction of the primary axis of the reference coordinate system is determined based on the polarization angle, and the direction of the orthogonal secondary axis is calculated based on the ellipticity. An orthogonal polarization decomposition coordinate system is established, with the primary axis as the reference and the secondary axis as the perpendicular direction.

[0171] 602. Project the polarization components of the multi-mode forged navigation signal onto the polarization decomposition reference coordinate system, and decompose the polarization components in the polarization decomposition reference coordinate system into a main polarization component and a cross-polarization component.

[0172] In step 602, the polarization component refers to the energy distribution of the forged navigation signal in different polarization directions. By projecting the polarization state of the multimode forged signal onto the reference coordinate system established in the previous step, it can be decomposed into two components: primary polarization and cross-polarization. The primary polarization component is the component projected along the primary axis of the reference coordinate system, which matches the primary polarization of the receiving antenna. The cross-polarization component is the component projected along the secondary axis of the reference coordinate system, which can easily cause signal leakage.

[0173] In this embodiment, given a known polarization decomposition reference coordinate system, the system uses vector projection technology to decompose the polarization vector of the spurious signal onto the primary and secondary axes of the reference coordinate system. The power of the primary and secondary axis components is calculated separately. The power of the primary and secondary axis components is normalized to the total power to generate the main and cross-polarization components.

[0174] 603. Calculate a polarization angle error between the main polarization component and the main polarization direction parameter, generate an initial polarization adjustment amount based on the polarization angle error, and perform gain compensation on the main polarization component according to the cross-polarization component to obtain a gain compensation amount.

[0175] In step 603, the polarization angle error of the main polarization component refers to the angular deviation between the current main polarization component and the main polarization direction; the initial polarization adjustment amount is a correction parameter generated based on the angle error and is used to adjust the polarization state of the forged signal; and the gain compensation amount is a power compensation value of the main polarization component based on the power of the cross-polarization component.

[0176] In this embodiment, the inverse tangent function is first used to calculate the directional deviation angle between the main polarization component and the reference principal axis, which is used as the polarization angle error. This polarization angle error is then input into a proportional-integral controller, which outputs an initial polarization adjustment. Subsequently, nonlinear gain compensation is performed on the main polarization component based on the ratio of the cross-polarization component power to the main polarization component power, and the gain compensation amount is calculated.

[0177] 604. Superimpose the initial polarization adjustment amount and the gain compensation amount to generate a polarization weighting coefficient that matches the main polarization direction parameter.

[0178] In step 604, the initial polarization adjustment is used to correct the polarization state of the counterfeit signal; the gain compensation is used to increase the signal energy in the main polarization direction; and the polarization weighting coefficient is the effective utilization coefficient of the main polarization component after error compensation and gain adjustment. It is used to guide the polarization configuration of the counterfeit signal during transmission, making it closer to the polarization characteristics of the real signal.

[0179] In this embodiment, the initial polarization adjustment and gain compensation are weighted together to generate a comprehensive adjustment. This comprehensive adjustment is then fed into a hyperbolic tangent function, and the polarization weighting coefficient is verified to meet the threshold requirement for the primary polarization power ratio. This coefficient is then applied to the polarization modulation module of the forged signal to adjust the polarization state of the transmitted signal in real time.

[0180] Here's a specific example:

[0181] To defend against reconnaissance drones over a military base, it is necessary to covertly induce them to deviate from the monitoring area. The multimode navigation signal in use is monitored. The system analyzes the signal carrier phase and signal strength fluctuations, extracts the main polarization direction and ellipticity information of the current receiving antenna, and establishes a polarization decomposition reference coordinate system. Subsequently, the polarization components of the forged signal are projected onto this coordinate system and decomposed into main polarization and cross-polarization components. After detecting a slight angular deviation in the main polarization component, a polarization adjustment is generated, and gain compensation is performed in combination with the energy information of the cross-polarization component. Finally, the two are fused to generate a polarization weighting coefficient, which is used to optimize the polarization state of the forged signal, making it difficult to distinguish from the real signal at the receiving end, successfully inducing the drone to deviate from the monitoring area.

[0182] In summary, steps 601 to 604 significantly improve the polarization matching accuracy of counterfeit signals. Through polarization angle error feedback control and cross-polarization power compensation, the effective utilization rate of the main polarization component is collaboratively optimized. Through highly matched polarization weighting coefficients, the risk of cross-polarization signal leakage is minimized. This method systematically solves the signal attenuation problem caused by polarization mismatch in traditional solutions from three dimensions: polarization direction alignment, power loss compensation, and dynamic error suppression. This method improves the concealment of counterfeit signals and enhances the ability to resist multipath interference in complex electromagnetic environments, providing reliable technical support for drone defense in sensitive areas.

[0183] To address navigation verification alarms caused by insufficient spatial consistency of forged signals, the solution converts delay corrections and phase offsets into pseudorange adjustments and derivatives, dynamically fusing these into three-dimensional coordinate increments based on the real-time position deviation direction ratio. The increments are further decomposed into the line-of-sight directions of each satellite based on the satellite's geometric distribution, generating pseudorange compensations that are converted into navigation message coordinate increments. In some embodiments, step 104 dynamically compensates the three-dimensional coordinate increments of the navigation message based on the signal delay correction and carrier phase offset in the corrected forged navigation signal parameters, combined with the real-time position deviation, to generate a multi-mode forged navigation signal covering the drone's receiving end, including:

[0184] 701. Project the signal delay correction along the satellite signal propagation direction into the pseudorange domain to generate a pseudorange adjustment, and calculate the pseudorange differential in combination with the carrier phase offset;

[0185] In step 701, the signal delay correction refers to the parameter used to adjust the original satellite signal propagation time. The satellite signal propagation direction is the straight-line path from the satellite to the receiver. The pseudorange domain is the measurement space after signal propagation time is converted to distance. The pseudorange adjustment is the equivalent pseudorange value of the delay correction projected along the satellite signal propagation direction, used to simulate false range. The carrier phase offset is a small perturbation to the carrier waveform used to reflect more precise position changes. The pseudorange derivative is a pseudorange fine adjustment value converted from the carrier phase offset and is used to compensate for centimeter-level errors.

[0186] In this embodiment, the current signal delay correction is first projected into the pseudorange domain along the line of sight of each visible satellite, and the corresponding pseudorange adjustment is calculated. Simultaneously, the pseudorange differential is calculated based on the proportional relationship between the carrier phase offset and the carrier wavelength. The pseudorange adjustment and the pseudorange differential are combined to form the total pseudorange correction. This process achieves a unified mapping from the time and phase domains to the pseudorange domain, providing the basic input for subsequent spatial coordinate corrections.

[0187] 702. Perform dynamic weight fusion on the pseudorange adjustment amount and the pseudorange differential amount according to the ratio of the orthogonal component to the tangential component of the real-time position deviation to generate an adjustment amount for the three-dimensional coordinate increment;

[0188] In step 702, the orthogonal component of the real-time position deviation refers to the projection of the real-time position deviation in the direction of the satellite's line of sight. The tangential component refers to the component of the real-time position deviation perpendicular to the direction of the satellite's line of sight. Dynamic weight fusion is a weighted synthesis of the pseudorange adjustment and the pseudorange derivative based on the proportional relationship between the different components to form a three-dimensional coordinate incremental adjustment with directional characteristics.

[0189] In an embodiment of the present application, the real-time position deviation is first decomposed into components in the orthogonal and tangential directions, and the dynamic fusion weight of the pseudorange adjustment amount and the differential component is set according to the component ratio. Subsequently, the weight is applied to the fusion processing of the pseudorange adjustment amount and the pseudorange differential component, and the weighted average algorithm is used to generate the adjustment amount of the three-dimensional coordinate increment.

[0190] 703. Based on the satellite geometric distribution of the navigation message received by the UAV, decompose the adjustment amount of the three-dimensional coordinate increment into the satellite line of sight direction, and generate a dynamic distribution weight of the satellite pseudorange correction amount;

[0191] In step 703, the adjustment amount for the 3D coordinate increment refers to the spatial correction vector output in the previous step. Satellite geometry refers to the spatial configuration of satellites visible to the drone receiver, which affects the positioning precision factor. Satellite line of sight refers to the direction from each satellite to the receiver. Dynamic weighting is the proportional coefficient that appropriately distributes the coordinate increments to each satellite pseudorange channel based on the satellite geometry.

[0192] In this embodiment, the system analyzes the spatial geometry of the currently visible satellites and constructs a geometric weight matrix. This matrix is ​​then used to project the adjustments to the three-dimensional coordinate increments onto the line of sight of each satellite. By calculating the pseudorange corrections corresponding to each satellite, the pseudorange correction proportion that each satellite should bear is determined, i.e., the dynamic weighting is assigned.

[0193] 704. Perform spatial orthogonal decomposition on the satellite pseudorange correction according to the dynamically allocated weights to generate a pseudorange compensation that matches the satellite signal propagation direction.

[0194] In step 704, dynamic weights are used to guide the spatial distribution of pseudorange corrections. Spatial orthogonal decomposition decomposes pseudorange corrections into three-dimensional spatial components along the satellite signal propagation direction. The pseudorange offset is the correction parameter ultimately used to modify the navigation message to align it with the actual signal.

[0195] In this embodiment, based on the known dynamic weights assigned to each satellite, the system decomposes the pseudorange correction for each satellite in its line of sight and orthogonal directions into a primary pseudorange component and an auxiliary correction component. The primary component is then filtered based on the propagation path characteristics to generate the final pseudorange compensation. This process enables independent optimization of each satellite signal, enhancing the spatial consistency of the forged signal.

[0196] 705. Convert the pseudo-range compensation amount into a three-dimensional coordinate increment of the navigation message, and dynamically bias the increment direction in combination with the rate of change of the real-time position deviation amount to generate a multi-mode forged navigation signal covering the UAV receiving end.

[0197] In step 705, the pseudorange compensation is the key data used to modify the navigation message after spatial allocation and decomposition. The 3D coordinate increment of the navigation message is the position offset derived from the pseudorange correction through reverse calculation. The rate of change refers to the speed at which this increment changes over time. Dynamic biasing adjusts the directional weight of the coordinate increment based on the real-time rate of change of the position deviation.

[0198] In this embodiment, the pseudorange compensation for each satellite is converted into three-dimensional coordinate increments in the Earth coordinate system. Subsequently, the incremental direction is vector-weighted based on the rate of change of the real-time position deviation to strengthen the correction amplitude of the dominant deviation direction. A nonlinear bias adjustment is performed on this direction, and the dynamically biased coordinate increments are embedded in the navigation message to generate a multi-mode forged navigation signal.

[0199] Here's a specific example:

[0200] To defend against intruding drones over a military base, the system must entice them to depart from a no-fly zone in a southeasterly direction. The system continuously monitors the active multimode navigation signal, maps the signal delay correction and carrier phase offset to the pseudorange domain, generates a pseudorange adjustment, and calculates the derivative based on the carrier phase offset. Dynamic fusion is then performed based on the directional characteristics of the real-time position deviation to generate a three-dimensional spatial correction. The correction is then distributed across satellite channels based on the satellite geometry. An orthogonal decomposition is performed to extract effective compensation parameters, which are then synthesized into pseudorange compensations. Finally, these compensation parameters are converted into coordinate increments for a forged navigation message. The directional offset is then applied based on the deviation trend, generating a highly realistic forged navigation signal. Upon receiving the mixed signal, the drone misjudges its own position and gradually moves away from the no-fly zone, effectively deceiving and jamming the system.

[0201] In summary, steps 701 to 705 accurately map the time delay and phase corrections to the pseudorange domain, combining the dynamic weighting of the orthogonal and tangential components to generate three-dimensional coordinate increments that conform to the satellite's geometric configuration. Secondly, based on the satellite distribution characteristics, the increments are decomposed into the line-of-sight directions of each satellite to ensure the spatial propagation consistency of the pseudorange corrections. Finally, the correction amplitude of the dominant deviation direction is strengthened through dynamic biasing to achieve covert reconstruction of the navigation message. This method significantly improves the spatial synchronization between the forged signal and the real satellite signal from three aspects: pseudorange mapping, spatial decomposition, and direction biasing. It eliminates verification alarms caused by coordinate jumps or geometric contradictions, and enhances the stability of multi-mode signal collaborative deception in complex airspace environments, providing a highly reliable technical guarantee for the protection of sensitive areas.

[0202] Figure 2 The present invention provides a schematic diagram of a multi-mode satellite signal forgery system for UAV navigation. Figure 2 As shown, the system includes:

[0203] The acquisition module 21 collects the original signal parameters of the multi-mode satellite signal in the current navigation link of the UAV in the target area, wherein the original signal parameters include signal delay, carrier phase and signal strength distribution;

[0204] An extraction module 22 extracts the spatiotemporal characteristics of the multimode satellite signal based on the signal delay and carrier phase in the original signal parameters, and integrates the dynamic changes of the signal strength distribution to generate forged navigation signal parameters synchronized with the original signal parameters in the time domain;

[0205] A correction module 23 dynamically corrects the forged navigation signal parameters according to a real-time position deviation between the preset flight path of the UAV and the decoy target path to obtain corrected forged navigation signal parameters;

[0206] The generating module 24 dynamically compensates the three-dimensional coordinate increment of the navigation message based on the signal delay correction and carrier phase offset in the corrected forged navigation signal parameters and in combination with the real-time position deviation, thereby generating a multi-mode forged navigation signal covering the receiving end of the UAV;

[0207] The superposition module 25 superimposes the multi-mode forged navigation signal with the multi-mode satellite signal corresponding to the original signal parameters at the receiving end of the UAV to generate a decoy navigation trajectory.

[0208] Figure 2 The UAV navigation deception system based on multi-mode satellite signal forgery can be executed Figure 1 The implementation principles and technical effects of the multi-mode satellite signal forgery method for drone navigation described in the illustrated embodiment will not be elaborated upon. The specific manner in which each module and unit performs operations in the multi-mode satellite signal forgery drone navigation deception system described in the aforementioned embodiment has been described in detail in the related embodiments and will not be further elaborated here.

[0209] In one possible design, Figure 2 The embodiment shown is a drone navigation deception system based on multi-mode satellite signal forgery, which can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0210] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0211] The processing component 32 is used for the above Figure 1 The embodiment provides a drone navigation spoofing method based on multi-mode satellite signal forgery.

[0212] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0213] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0214] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0215] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0216] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0217] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0218] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a drone navigation spoofing method based on multi-mode satellite signal forgery.

[0219] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0220] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0221] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0222] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A drone navigation spoofing method based on multi-mode satellite signal forgery, characterized in that: include: Collecting the original signal parameters of the multi-mode satellite signal in the current navigation link of the UAV in the target area, the original signal parameters including signal delay, carrier phase and signal strength distribution; Extracting the spatiotemporal characteristics of the multimode satellite signal based on the signal delay and carrier phase in the original signal parameters, and fusing the dynamic changes of the signal strength distribution to generate forged navigation signal parameters synchronized with the original signal parameters in the time domain; Dynamically correcting the forged navigation signal parameters according to a real-time position deviation between a preset flight path of the UAV and a decoy target path to obtain corrected forged navigation signal parameters; Based on the signal delay correction amount in the corrected forged navigation signal parameters and the offset of the carrier phase, and in combination with the real-time position deviation, the three-dimensional coordinate increment of the navigation message is dynamically compensated to generate a multi-mode forged navigation signal covering the receiving end of the drone; At the receiving end of the UAV, the multi-mode forged navigation signal is superimposed with the multi-mode satellite signal corresponding to the original signal parameters to generate a decoy navigation trajectory; The dynamically correcting the forged navigation signal parameters according to the real-time position deviation between the preset flight path of the UAV and the decoy target path to obtain the corrected forged navigation signal parameters includes: Obtaining a real-time trajectory point sequence of a preset flight path of the UAV and an expected trajectory point sequence of a decoy target path, and calculating a real-time position deviation between the real-time trajectory point sequence and the expected trajectory point sequence at corresponding timestamps as a deviation weight coefficient; Projecting the real-time position deviation into the pseudo-range domain along the satellite signal propagation direction, and constructing the signal delay correction and carrier phase offset of the forged navigation signal parameters in combination with the deviation weight coefficient; Based on the rate of change of the real-time position deviation, the signal delay correction amount and the carrier phase offset are reversely adjusted to obtain the adjusted signal delay correction amount and carrier phase offset; The adjusted signal delay correction amount and carrier phase offset amount are dynamically compensated using the historical accumulation of the deviation weight coefficient to generate corrected forged navigation signal parameters.

2. The method according to claim 1, characterized in that The reverse joint adjustment of the signal delay correction amount and the carrier phase offset based on the rate of change of the real-time position deviation to obtain the adjusted signal delay correction amount and the carrier phase offset includes: Based on the historical sequence of the real-time position deviation, a weighted combination value of the instantaneous change rate and the average change rate of the real-time position deviation is calculated as a deviation dynamic trend value; Decomposing the direction of the deviation dynamic trend into an orthogonal component and a tangential component of the satellite signal propagation direction, and converting the orthogonal component and the tangential component into inverse adjustment factors of the signal delay correction amount and the carrier phase offset amount; Calculating a joint adjustment weight coefficient according to the modulus of the real-time position deviation and the deviation weight coefficient, and weighting the reverse adjustment factor according to the joint adjustment weight coefficient to obtain a weighted reverse adjustment factor; Dynamically adjusting the gain coefficient of the weighted reverse adjustment factor according to the residual of the signal delay correction amount and the carrier phase offset amount to obtain an adjusted gain coefficient; The weighted reverse adjustment factor and the adjusted gain coefficient are added to the signal delay correction amount and the carrier phase offset to generate an adjusted signal delay correction amount and a carrier phase offset.

3. The method according to claim 2, characterized in that The dynamically adjusting the gain coefficient of the weighted reverse adjustment factor according to the residual of the signal delay correction amount and the carrier phase offset to obtain the adjusted gain coefficient includes: Calculating the difference between the actual update amount and the expected update amount of the signal delay correction as the delay residual, and the difference between the actual update amount and the expected update amount of the carrier phase offset as the phase residual; Calculating a delay residual trend factor and a phase residual trend factor according to the instantaneous change rate and cumulative change of the delay residual and the phase residual in consecutive iteration cycles; The delay residual trend factor and the phase residual trend factor are integrated in a preset ratio to obtain a joint residual trend quantity, and a gain correction factor is generated according to the direction and modulus of the joint residual trend quantity; Based on the modulus of the weighted reverse adjustment factor in the current iteration cycle, the gain correction factor is piecewise linearly scaled to obtain an adjusted gain coefficient.

4. The method according to claim 1, wherein The method of superimposing the multi-mode forged navigation signal and the multi-mode satellite signal corresponding to the original signal parameters at the receiving end of the UAV to generate a decoy navigation trajectory includes: Based on the carrier phase in the original signal parameters, the main polarization direction parameters of the drone receiving antenna are extracted, and the incident angle adjustment boundary is determined based on the dynamic fluctuation of the signal strength distribution; Performing polarization orthogonal decomposition on the multi-mode forged navigation signal to generate a polarization weighting coefficient that matches the main polarization direction parameter; Calculating a power balancing coefficient between the multi-mode forged navigation signal and the corresponding multi-mode satellite signal based on the dynamic fluctuation of the signal strength distribution, and dynamically scaling the transmit power of the multi-mode forged navigation signal based on the power balancing coefficient to obtain a balanced transmit power; Within the incident angle adjustment boundary, the polarization weighting coefficient, the equalized transmit power and the multi-mode satellite signal are superimposed to generate a decoy navigation trajectory.

5. The method according to claim 4, characterized in that The performing polarization orthogonal decomposition on the multi-mode forged navigation signal to generate a polarization weighting coefficient matching the main polarization direction parameter includes: Generate a polarization decomposition reference coordinate system according to the polarization angle and ellipticity of the main polarization direction parameters described in the original signal parameters; Projecting the polarization components of the multi-mode forged navigation signal onto the polarization decomposition reference coordinate system, and decomposing the polarization components in the polarization decomposition reference coordinate system into a main polarization component and a cross-polarization component; calculating a polarization angle error between the main polarization component and the main polarization direction parameter, generating an initial polarization adjustment amount based on the polarization angle error, and performing gain compensation on the main polarization component according to the cross-polarization component to obtain a gain compensation amount; The initial polarization adjustment amount is superimposed on the gain compensation amount to generate a polarization weighting coefficient that matches the main polarization direction parameter.

6. The method according to claim 1, characterized in that The method comprises: dynamically compensating the three-dimensional coordinate increment of the navigation message based on the signal delay correction amount and the carrier phase offset in the corrected forged navigation signal parameters and combining the real-time position deviation amount to generate a multi-mode forged navigation signal covering the receiving end of the drone, including: Projecting the signal delay correction amount along the satellite signal propagation direction into the pseudorange domain to generate a pseudorange adjustment amount, and calculating the pseudorange differential amount in combination with the carrier phase offset; According to the ratio of the orthogonal component to the tangential component of the real-time position deviation, the pseudorange adjustment amount and the pseudorange differential amount are dynamically weighted to generate an adjustment amount of the three-dimensional coordinate increment; Based on the satellite geometric distribution of the navigation message received by the UAV, the adjustment amount of the three-dimensional coordinate increment is decomposed into the satellite line of sight direction to generate the dynamic distribution weight of the satellite pseudorange correction amount; Performing spatial orthogonal decomposition on the satellite pseudorange correction amount according to the dynamic allocation weight to generate a pseudorange compensation amount that matches the satellite signal propagation direction; The pseudo-range compensation amount is converted into a three-dimensional coordinate increment of the navigation message, and the incremental direction is dynamically biased in combination with the change rate of the real-time position deviation amount to generate a multi-mode forged navigation signal covering the UAV receiving end.

7. A drone navigation deception system based on multi-mode satellite signal forgery, characterized in that: include: An acquisition module collects raw signal parameters of the multi-mode satellite signal in the current navigation link of the UAV in the target area, including signal delay, carrier phase, and signal strength distribution; an extraction module that extracts the spatiotemporal characteristics of the multimode satellite signal based on the signal delay and carrier phase in the original signal parameters, and integrates the dynamic changes of the signal strength distribution to generate forged navigation signal parameters synchronized with the original signal parameters in the time domain; a correction module, which dynamically corrects the forged navigation signal parameters according to a real-time position deviation between the preset flight path of the UAV and the decoy target path to obtain corrected forged navigation signal parameters; A generation module dynamically compensates for the three-dimensional coordinate increment of the navigation message based on the signal delay correction and carrier phase offset in the corrected forged navigation signal parameters and in combination with the real-time position deviation, thereby generating a multi-mode forged navigation signal covering the receiving end of the drone; a superposition module, at the receiving end of the UAV, superimposing the multi-mode forged navigation signal with the multi-mode satellite signal corresponding to the original signal parameters to generate a decoy navigation trajectory; The dynamically correcting the forged navigation signal parameters according to the real-time position deviation between the preset flight path of the UAV and the decoy target path to obtain the corrected forged navigation signal parameters includes: Obtaining a real-time trajectory point sequence of a preset flight path of the UAV and an expected trajectory point sequence of a decoy target path, and calculating a real-time position deviation between the real-time trajectory point sequence and the expected trajectory point sequence at corresponding timestamps as a deviation weight coefficient; Projecting the real-time position deviation into the pseudo-range domain along the satellite signal propagation direction, and constructing the signal delay correction and carrier phase offset of the forged navigation signal parameters in combination with the deviation weight coefficient; Based on the rate of change of the real-time position deviation, the signal delay correction amount and the carrier phase offset are reversely adjusted to obtain the adjusted signal delay correction amount and carrier phase offset; The adjusted signal delay correction amount and carrier phase offset amount are dynamically compensated using the historical accumulation of the deviation weight coefficient to generate corrected forged navigation signal parameters.

8. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a drone navigation deception method based on multi-mode satellite signal forgery as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the method for spoofing a drone navigation based on multi-mode satellite signal forgery as described in any one of claims 1 to 6 is implemented.

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