Satellite signal anti-cheating identification method and system based on multi-channel parallel processing

Abstract

The invention relates to the technical field of satellite signal processing, and discloses a satellite signal anti-cheating identification method and system based on multichannel parallel processing, and the method comprises the steps: obtaining an unmanned plane satellite navigation signal, and carrying out the preprocessing; and decomposing the preprocessed signal into a plurality of independent channels, and carrying out deception signal monitoring on the signal of each channel. The detected deception signals are marked and isolated, and the inertial measurement unit is used for calculating the real-time speed and acceleration to update satellite parameters. According to the method, the signal is decomposed into a plurality of independent channels, and smooth change and rapid change characteristics of the signal are effectively distinguished, so that a potential deception signal is rapidly identified. The deception signal monitoring adopts the combination of a plurality of means, so that the monitoring comprehensiveness is improved, and the self-adaptive capability of the system is enhanced. The local isolation processing mode ensures that the system can still utilize effective signal information to update navigation parameters when facing deception signals, and the anti-interference effect is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite signal processing, and particularly to an anti-spoofing recognition method and system for satellite signals based on multi-channel parallel processing. Background Art

[0002] With the rapid development of unmanned aerial vehicle (UAV) technology, satellite navigation systems have played a crucial role in the flight control and path planning of UAVs. In recent years, the maturity of satellite navigation technology, especially the BeiDou satellite navigation system, has greatly improved the navigation accuracy and reliability of UAVs in complex environments. However, with the popularization of the technology, the risks of malicious interference and spoofing attacks have become increasingly prominent. Spoofing attacks make UAVs mistakenly believe that they are in a safe position by forging satellite signals, causing them to deviate from the predetermined flight path and even fly out of control. Therefore, how to ensure the safety and accuracy of UAVs when using satellite navigation signals has become an urgent problem to be solved.

[0003] Currently, traditional satellite signal processing technologies mainly rely on single-channel signal processing methods. This method can work properly when the signal quality is good and the environment is relatively simple, but its limitations gradually emerge when facing complex interference or spoofing signals. First, single-channel processing cannot effectively distinguish normal signals from forged signals because they may overlap in time, making it difficult to identify forged signals. Second, single-channel processing cannot effectively compare using the complementarity between multi-channel signals and cannot accurately detect abnormal signals. In addition, the existing technologies are also insufficient in the depth and breadth of signal monitoring and are difficult to comprehensively monitor various potential spoofing signals in real time. These deficiencies make UAVs lack effective protection measures when facing complex signal environments, thus reducing the safety and reliability of the overall navigation system. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed.

[0005] Therefore, the technical problem to be solved by the present invention is that spoofing attacks make UAVs mistakenly believe that they are in a safe position by forging satellite signals, causing them to deviate from the predetermined flight path and even fly out of control. Therefore, how to ensure the safety and accuracy of UAVs when using satellite navigation signals has become an urgent problem to be solved.

[0006] To solve the above technical problem, the present invention provides the following technical solution: an anti-spoofing recognition method for satellite signals based on multi-channel parallel processing, including: acquiring satellite navigation signals of a UAV and performing preprocessing; decomposing the preprocessed signals into multiple independent channels and monitoring spoofing signals for the signals of each channel; Mark and isolate the detected spoofing signals, and use the inertial measurement unit to estimate the real-time speed and acceleration to update the satellite parameters; Monitor the spoofing signals for each channel, including AGC gain monitoring, signal power monitoring, satellite visibility monitoring, and autonomous integrity monitoring; The AGC gain monitoring includes determining whether there is interference by monitoring the change of the control voltage of the AGC in the receiver. When an interference signal enters the RF circuit, the AGC circuit detects the change of the input signal level and triggers the adjustment of the AGC control voltage. The intensity of the interference signal is judged by the change of the AGC gain; The signal power monitoring includes detecting the power of the satellite signals received by the receiver. When the power of the spoofing signal received by the receiver is much greater than the power of the real satellite signals received under normal circumstances, the spoofing signal is identified; The satellite visibility monitoring includes tracking the new satellite signals, calculating the azimuth and elevation angles according to the prior ephemeris, and judging whether the satellite is in the field of view. If it is not in the field of view, it is judged that there is a spoofing signal; The autonomous integrity monitoring includes calculating a value E to characterize the consistency of the measurement values. When E is large, it means that the measurement values have poor consistency, and when E is small, it means that the measurement values have good consistency; setting a decision threshold, and comparing E with the decision threshold to decide whether to send an alarm signal; Marking and isolating the detected spoofing signals, and using the inertial measurement unit to estimate the real-time speed and acceleration to update the satellite parameters includes marking all the spoofing signals detected by the spoofing signal monitoring and recording the relevant information, including the signal channel and the marking time; Locally isolate the spoofing signals. If the signal is not marked, keep the original value of the signal. If the signal is marked as a spoofing signal, take the average value of the signal values at the two time points before and after it to replace the spoofing signal at the current moment, which is expressed as:

[0007] where, represents the signal value after local isolation processing at time t, represents the value of the original signal at time t, represents the signal value of the original signal at a sampling point before time t, represents the signal value of the original signal at a sampling point after time t; Using an inertial measurement unit to calculate real-time speed and acceleration to update satellite parameters includes: The IMU, as an inertial sensor, measures data such as the acceleration and angular velocity of the drone, provides the estimation of position and speed, and corrects the current navigation parameters; Using the acceleration data in the IMU inertial measurement unit to correct the current position and speed of the drone; When the spoofing signal is isolated, the real-time speed and acceleration calculated by the IMU are used to update the navigation parameters to ensure the accuracy of the flight state.

[0008] As a preferred solution of the method for anti-spoofing identification of satellite signals based on multi-channel parallel processing according to the present invention, wherein: obtaining the satellite navigation signal of the drone and performing preprocessing includes, under the Beidou satellite navigation system, receiving the navigation signal transmitted from the Beidou satellite.

[0009] Preprocessing includes denoising and filtering the signal to ensure the signal quality.

[0010] As a preferred solution of the method for anti-spoofing identification of satellite signals based on multi-channel parallel processing according to the present invention, wherein: decomposing the preprocessed signal into multiple independent channels includes using wavelet transform to decompose the preprocessed signal and extract components of different frequencies.

[0011] According to different scales of wavelet coefficients, the signal is decomposed into two independent channels, including a high-frequency channel and a low-frequency channel. The low-frequency channel contains the smooth part of the signal and is suitable for slow-changing features. The high-frequency channel contains the fast-changing part of the signal and is suitable for burst features.

[0012] As a preferred solution of the method for anti-spoofing identification of satellite signals based on multi-channel parallel processing according to the present invention, wherein: the autonomous integrity monitoring further includes identifying each satellite, excluding the faulty satellite from navigation, and calculating the measurement consistency characterization value of each satellite.

[0013] The larger the measurement consistency characterization value of each satellite, the worse the consistency of the satellite measurement value. The satellite with the largest characterization value is determined as the faulty satellite and is excluded from the navigation calculation, and at the same time, the faulty satellite number is output.

[0014] An anti-spoofing identification system for satellite signals based on multi-channel parallel processing, characterized in that it includes: A preprocessing module that obtains the satellite navigation signal of the drone and performs preprocessing.

[0015] A spoofing detection module that decomposes the preprocessed signal into multiple independent channels and monitors the spoofing signal for the signal of each channel.

[0016] The anti-spoofing module marks and isolates the detected spoofing signals, and uses the inertial measurement unit to calculate the real-time speed and acceleration to update the satellite parameters.

[0017] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method described above are implemented.

[0018] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of the method described above are implemented.

[0019] Advantages of the present invention: The signal is decomposed into multiple independent channels, including high-frequency channels and low-frequency channels, which can analyze different characteristics. Real-time monitoring is carried out at different frequency levels to effectively distinguish the smooth change and fast change characteristics of the signal, so as to quickly identify potential spoofing signals. Compared with the traditional single-channel processing technology, this multi-channel decomposition strategy significantly enhances the ability to identify abnormal signals. The spoofing signal monitoring combines multiple means, which improves the comprehensiveness of monitoring and also enhances the adaptive ability of the system. By marking and locally isolating the detected spoofing signals, interference to normal signals is effectively avoided. The local isolation processing method ensures that when the system faces spoofing signals, it can still use effective signal information to update navigation parameters. This process calculates the real-time speed and acceleration through the inertial measurement unit, ensuring the accuracy of the UAV flight state and greatly improving the anti-spoofing ability of the system. Description of the Drawings

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is the overall flowchart of a method and system for anti-spoofing identification of satellite signals based on multi-channel parallel processing provided by the first embodiment of the present invention.

[0022] Figure 2 It is the curve graph of the relationship between the input interference intensity and the AGC gain change of a method and system for anti-spoofing identification of satellite signals based on multi-channel parallel processing provided by the first embodiment of the present invention.

[0023] Figure 3 It is the flowchart of satellite visibility monitoring of a method and system for anti-spoofing identification of satellite signals based on multi-channel parallel processing provided by the first embodiment of the present invention. Detailed Embodiments

[0024] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0025] Example 1, referring to Figures 1 to 3 , which is an embodiment of the present invention, provides a method for anti-spoofing identification of satellite signals based on multi-channel parallel processing, including: S1: Obtain the satellite navigation signal of the unmanned aerial vehicle and perform preprocessing.

[0026] Under the Beidou satellite navigation system, receive the navigation signal transmitted from the Beidou satellite.

[0027] The preprocessing includes denoising and filtering the signal to ensure the signal quality.

[0028] S2: Decompose the preprocessed signal into multiple independent channels and monitor the spoofing signals for each channel.

[0029] Use wavelet transform to decompose the preprocessed signal and extract components of different frequencies.

[0030] According to different scales of wavelet coefficients, the signal is decomposed into two independent channels, including a high-frequency channel and a low-frequency channel. The low-frequency channel contains the smooth part of the signal and is suitable for slow-changing features. The high-frequency channel contains the fast-changing part of the signal and is suitable for burst features.

[0031] Further, the wavelet transform is expressed as:

[0032] Among them, represents the wavelet coefficient, represents the preprocessed signal, represents the wavelet function, j represents the scale parameter, and k represents the translation parameter.

[0033] For each channel, use the inverse wavelet transform to reconstruct the wavelet coefficients into a time-domain signal:

[0034] Save the signals of each channel as independent data streams for subsequent processing.

[0035] It should be noted that the low-frequency channel contains the smooth part of the signal and is suitable for capturing slow-changing features, such as long-term trends and stability, which is very important for monitoring the overall state of the monitoring system. The high-frequency channel contains the fast-changing part of the signal and is suitable for capturing sudden features, such as interference or instantaneous changes, which is crucial for identifying spoofing signals or abnormal behaviors. After decomposing the signal into low and high frequency components, they can be analyzed separately, making it easier to identify and extract important information in a complex signal environment.

[0036] Furthermore, high-frequency signals are usually more vulnerable to noise and interference. Through wavelet transform, high-frequency and low-frequency signals can be processed independently, which helps to improve the system's resistance to interference. The decomposed low-frequency and high-frequency channels can be processed independently, which contributes to the implementation of multi-channel parallel processing and improves the overall processing efficiency and real-time performance of the system.

[0037] Spoofing signal monitoring includes AGC gain monitoring, signal power monitoring, satellite visibility monitoring, and autonomous integrity monitoring.

[0038] AGC gain monitoring includes determining whether there is interference by monitoring the change of the control voltage of AGC in the receiver. When an interference signal enters the RF circuit, the AGC circuit detects the change of the input signal level and triggers the adjustment of the AGC control voltage, and judges the intensity of the interference signal through the change of the AGC gain.

[0039] It should be noted that in the RF circuit of the existing time service receiving module, there is an AGC (Auto Gain Control) device, which can monitor and adjust the input RF signal level to ensure that the signal level input to the A / D converter meets the requirements of optimal quantization. Under normal working conditions, the intensity of the spread spectrum signal is much lower than the noise intensity, and the main factor affecting the AGC control voltage is the noise level, which is restricted by the device performance and usually does not change much. However, when the interference intensity is greater than the noise intensity, the main factor affecting the AGC control voltage is the interference signal intensity. AGC adopts a negative feedback control strategy. When the input signal intensity rises, AGC reduces the signal amplification factor. Therefore, when a high-intensity interference enters the RF circuit, it will prompt the AGC circuit to detect the change of the input signal level and keep the amplified signal level stable by means of voltage control. That is, whether there is an interference signal entering the receiver circuit can be detected through the detection of the AGC control voltage, and a quantitative judgment of the interference intensity can be made according to the specific value of the gain.

[0040] Such as Figure 2As shown, as the intensity of the input interference changes, the AGC gain will make corresponding adjustments so that the magnitude of the amplified level can be kept stable. Moreover, as the interference intensity increases, the AGC will further reduce the amplification gain. Thus, the change situation of the AGC gain can be used to accurately judge the current interference intensity situation.

[0041] Signal power monitoring includes detecting the power of the satellite signals received by the receiver. When the power of the spoofing signal received by the receiver is much greater than the power of the real satellite signals received under normal circumstances, the spoofing signal is identified.

[0042] Satellite visibility monitoring is as Figure 3 shown. Track the new satellite signals, calculate the azimuth and elevation angles according to the prior ephemeris, and judge whether the satellite is within the field of view. If it is not within the field of view, it is judged that there is a spoofing signal.

[0043] Autonomous integrity monitoring includes calculating a value E to characterize the consistency of the measurement values. When E is large, it indicates poor consistency of the measurement values; when E is small, it indicates good consistency of the measurement values.

[0044] It should be noted that in the application, the decision threshold can be flexibly adjusted as needed, and the E value is compared with the decision threshold to determine whether to send an alarm signal.

[0045] Furthermore, the calculation steps of E are as follows:

[0046]

[0047] Among them, W represents the weight matrix used to solve the least squares solution, H represents the direction cosine matrix for least squares solution, H T represents the transpose of the direction cosine matrix, with a dimension of 4*12. Z represents the pseudorange increment matrix participating in the solution, with a dimension of 12*1. I represents the 12*12 identity matrix. P represents an 8*1 matrix. V represents an 8*12 matrix. The subscripts i and j respectively represent the row and column positions of the element, and n represents the number of satellites participating in the solution. E represents the consistency characterization value of the measurement values. P i represents the pseudorange increment residual.

[0048] Identify each satellite, exclude the faulty satellite from the navigation, and calculate the measurement consistency characterization values of each satellite.

[0049] The larger the measurement consistency characterization value of each satellite, the worse the consistency of the satellite measurement values. The satellite with the largest characterization value is determined as the faulty satellite and excluded from the navigation calculation. At the same time, the faulty satellite number can be output.

[0050] It should be noted that the measurement consistency characterization value of each satellite is expressed as:

[0051] Among them, FDj represents the measurement consistency characterization value of the j-th satellite. P represents the pseudorange increment matrix. j represents the satellite number, and the value range of j is from 0 to 11. represents the j-th column vector of matrix V.

[0052] S3: Mark and isolate the detected spoofing signal, and use the inertial measurement unit to estimate the real-time speed and acceleration to update the satellite parameters.

[0053] Mark all the spoofing signals detected by the spoofing signal monitoring, and record the relevant information, including the signal channel and the marking time.

[0054] Perform local isolation on the spoofing signal. If the signal is not marked, keep the original value of the signal. If the signal is marked as a spoofing signal, take the average value of the signal values at the two time points before and after it to replace the spoofing signal at the current moment, which is expressed as:

[0055] Among them, represents the signal value after local isolation processing at time t, represents the value of the original signal at time t, represents the signal value of the original signal at a sampling point before time t, represents the signal value of the original signal at a sampling point after time t.

[0056] Using the inertial measurement unit to estimate the real-time speed and acceleration to update the satellite parameters includes using the acceleration data in the IMU inertial measurement unit to correct the current position and speed of the UAV. After the spoofing signal is isolated, the real-time speed and acceleration estimated by the IMU are used to update the navigation parameters to ensure the accuracy of the flight state.

[0057] It should be noted that as an inertial sensor, the IMU provides the estimation of position and speed by measuring data such as the acceleration and angular velocity of the UAV to correct the current navigation parameters. It includes the following steps: Real-time acceleration and angular velocity measurement: The accelerometer and gyroscope in the IMU measure the acceleration and angular velocity of the UAV in three directions respectively. The acceleration data is used to estimate the speed and position of the UAV, and the angular velocity data is used to estimate the attitude (roll angle, pitch angle and yaw angle) of the UAV.

[0058] Data Fusion and Deduction: By integrating the IMU acceleration data once, the current speed of the UAV is obtained, and then by integrating the speed, the current position information of the UAV is obtained. In addition, by integrating the angular velocity data, the system can deduce the attitude change of the UAV. After the interference signal is isolated, the IMU provides immediate position information for correcting the navigation parameters of the system.

[0059] Historical Data Comparison: The system combines IMU data and historical navigation data (such as Beidou satellite navigation data in the previous few seconds) to smooth the current position and prevent sudden navigation offsets. The position information deduced by the IMU is compared with the historical data. If the difference is within a reasonable range, the IMU data is used to correct the navigation parameters to ensure the smoothness and continuity of the navigation path.

[0060] Attitude Correction: According to the angular velocity data of the gyroscope, the system can update the attitude information of the UAV in real time. This is particularly important in complex environments to ensure that the UAV can maintain the stability of its flight attitude even when interfered.

[0061] It should be noted that integrating the IMU data to obtain the speed and position of the UAV is expressed as: Among them, V(t) represents the speed at time t, V(t - 1) represents the speed at time t - 1, and p(t) represents the position at time t. represents the sampling time interval. a(t) represents the acceleration.

[0062] The computer device can be a server. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the data cluster data of the power monitoring system. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it realizes a method for anti-spoofing identification of satellite signals based on multi-channel parallel processing.

[0063] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the various embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the various embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0064] Embodiment 2, an embodiment of the present invention, provides a method and system for anti-spoofing identification of satellite signals based on multi-channel parallel processing. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0065] First, a Beidou satellite navigation receiver was installed on the unmanned aerial vehicle, an IMU inertial measurement unit was prepared, and the corresponding signal processing software was set.

[0066] With the support of the Beidou satellite navigation system, navigation signals from Beidou satellites were received. After the signals were acquired, denoising and filtering were performed to ensure the signal quality. In this step, high-pass and low-pass filters were used to effectively remove the noise with a frequency lower than 1 Hz.

[0067] The preprocessed signal is decomposed using wavelet transform, and the signal is divided into a high-frequency channel and a low-frequency channel. The low-frequency channel extracts the smooth part of the signal, while the high-frequency channel focuses on the mutation characteristics of the signal.

[0068] Deception signal monitoring is implemented for each channel, including AGC gain monitoring, signal power monitoring, satellite visibility monitoring, and autonomous integrity monitoring.

[0069] The detected deception signals are marked, and the signal channels and time are recorded. Subsequently, the marked deception signals are replaced with the average values of the signal values at the previous and subsequent time points to achieve local isolation. IMU data is used to calculate the real-time speed and acceleration to ensure that the UAV accurately updates the navigation parameters during flight and maintains the stability of the flight state.

[0070] The existing technologies for comparison mainly rely on single-channel signal processing methods, which only perform simple denoising on the received signals, reflecting the significant differences and advantages between the present invention and the existing technologies. In terms of signal quality (SNR), the signal quality of our invention reaches 35 dB, while that of the existing technology is only 20 dB, indicating that in the signal preprocessing stage, our invention can significantly improve the clarity and effectiveness of the signal. The reduction of noise power is also a key indicator. The noise power of our invention is -100 dBm, while that of the existing technology is -80 dBm.

[0072] In terms of the deception signal detection time, our invention achieves a fast detection of 0.5 seconds. In contrast, the existing technology requires 2 seconds, indicating that it can respond faster in emergency situations and reduce potential risks. In addition, the overall accuracy rate is increased to 95%, while the accuracy rate of the existing technology is only 70%, indicating that through multi-channel parallel processing, the efficiency and accuracy of deception signal recognition have been greatly improved.

[0073] The efficiency of our invention reaches 90%, while that of the existing technology is only 50%. This indicates that through the local isolation method, deception signals can be better processed, thereby ensuring the integrity of normal signals. In terms of the navigation parameter update delay, the delay of our invention is only 0.2 seconds, while that of the existing technology is 1.5 seconds, indicating that the present invention has obvious advantages in real-time performance.

[0074] The significant improvement in the faulty satellite recognition rate (98% compared to 60%) indicates that through the autonomous integrity monitoring mechanism, our invention can effectively identify and exclude unreliable satellite signals, ensuring the safety and reliability of UAV navigation.

[0075] Embodiment 3, which is an embodiment of the present invention, provides a multi-channel parallel processing-based satellite signal anti-deception recognition system, including a preprocessing module, a deception detection module, and an anti-deception module.

[0076] The preprocessing module obtains the satellite navigation signal of the drone and performs preprocessing.

[0077] The spoofing detection module decomposes the preprocessed signal into multiple independent channels and monitors the spoofing signals for the signals of each channel.

[0078] The anti-spoofing module marks and isolates the detected spoofing signals, and uses the inertial measurement unit to estimate the real-time speed and acceleration to update the satellite parameters.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for anti-spoofing recognition of satellite signals based on multi-channel parallel processing, characterized in that, Including: Obtain the satellite navigation signal of the drone and perform preprocessing; Decompose the preprocessed signal into multiple independent channels, and monitor the spoofing signals for each channel; Mark and isolate the detected spoofing signals, and use the inertial measurement unit to estimate the real-time speed and acceleration to update the satellite parameters; Monitoring the spoofing signals for each channel includes AGC gain monitoring, signal power monitoring, satellite visibility monitoring, and autonomous integrity monitoring; The AGC gain monitoring includes, in the receiver, determining whether there is interference by monitoring the change of the control voltage of the AGC. When an interference signal enters the RF circuit, the AGC circuit detects the change of the input signal level and triggers the adjustment of the AGC control voltage, and judges the intensity of the interference signal according to the change of the AGC gain; The signal power monitoring includes detecting the power of the satellite signal received by the receiver. When the power of the spoofing signal received by the receiver is much greater than the power of the real satellite signal received under normal circumstances, the spoofing signal is identified; The satellite visibility monitoring includes tracking the new satellite signal, calculating the azimuth and elevation angle according to the prior ephemeris, and judging whether the satellite is within the field of view. If it is not within the field of view, it is judged that there is a spoofing signal; The autonomous integrity monitoring includes calculating a value E to characterize the consistency of the measurement values. When E is large, it means that the measurement values have poor consistency, and when E is small, it means that the measurement values have good consistency; setting a decision threshold, and comparing E with the decision threshold to decide whether to send an alarm signal; Marking and isolating the detected spoofing signals, and using the inertial measurement unit to estimate the real-time speed and acceleration to update the satellite parameters includes marking all the spoofing signals detected by the spoofing signal monitoring and recording the relevant information, including the signal channel and the marking time; Locally isolate the spoofing signal. If the signal is not marked, keep the original value of the signal. If the signal is marked as a spoofing signal, take the average value of the signal values at the two time points before and after it to replace the spoofing signal at the current moment, which is expressed as: , Among them, represents the signal value after local isolation processing at time t, represents the value of the original signal at time t, represents the signal value of the original signal at a sampling point before time t, represents the signal value of the original signal at a sampling point after time t; Using the inertial measurement unit to estimate the real-time speed and acceleration to update the satellite parameters includes that the IMU, as an inertial sensor, provides the estimation of the position and speed by measuring data such as the acceleration and angular velocity of the drone, and corrects the current navigation parameters; Use the acceleration data in the IMU inertial measurement unit to correct the current position and speed of the drone; after the spoofing signal is isolated, update the navigation parameters through the real-time speed and acceleration estimated by the IMU to ensure the accuracy of the flight state.

2. The anti-spoofing recognition method for satellite signals based on multi-channel parallel processing according to claim 1, characterized in that: The obtaining of the satellite navigation signal of the drone and performing preprocessing includes, under the Beidou satellite navigation system, receiving the navigation signal transmitted from the Beidou satellite; The preprocessing includes denoising and filtering the signal to ensure the signal quality.

3. The anti-spoofing recognition method for satellite signals based on multi-channel parallel processing according to claim 2, wherein: The decomposing of the preprocessed signal into multiple independent channels includes using wavelet transform to decompose the preprocessed signal and extract components of different frequencies; According to the different scales of the wavelet coefficients, the signal is decomposed into two independent channels, including a high-frequency channel and a low-frequency channel; The low-frequency channel contains the smooth part of the signal and is suitable for slow-changing features; the high-frequency channel contains the fast-changing part of the signal and is suitable for burst features.

4. The anti-spoofing recognition method for satellite signals based on multi-channel parallel processing according to claim 3, characterized in that: The autonomous integrity monitoring further includes identifying each satellite, excluding faulty satellites from navigation, and calculating the measurement consistency characterization values of each satellite; The larger the measurement consistency characterization value of each satellite, the worse the consistency of the satellite measurement values. The satellite with the largest characterization value is determined as the faulty satellite, which is excluded from the navigation calculation, and at the same time, the faulty satellite number is output.

5. A multi-channel parallel processing satellite signal anti-spoofing identification system, characterized in that: A preprocessing module, which acquires the UAV satellite navigation signal and performs preprocessing; A spoofing detection module, which decomposes the preprocessed signal into multiple independent channels and monitors the spoofing signals of the signals in each channel; An anti-spoofing module, which marks and isolates the detected spoofing signals, and uses the inertial measurement unit to estimate the real-time speed and acceleration to update the satellite parameters.

6. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method described in any one of claims 1 to 4.

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