Unmanned aerial vehicle dynamic navigation anti-interference method and system based on single Beidou satellite

Abstract

The invention relates to the technical field of satellite navigation and anti-interference, and discloses an unmanned aerial vehicle dynamic navigation anti-interference method and system based on a single Beidou satellite, and the method comprises the steps: an unmanned aerial vehicle receives a navigation signal through the single Beidou satellite, and carries out the initialization; and performing interference enhancement detection through a multi-frequency domain joint filtering technology. And analyzing a carrier phase tracking spectrum, and carrying out autonomous integrity monitoring on the single Beidou satellite. Deception signals are isolated, navigation parameters are corrected, and the dynamic navigation anti-interference effect of the unmanned aerial vehicle is achieved. According to the method, a carrier phase tracking spectrum is analyzed through a multi-frequency-domain joint filtering technology, a disguise deception signal of a submerged loop is identified under the condition that no strong interference signal exists, and the authenticity of a navigation signal is ensured. In combination with IMU data and historical pseudo-range changes, through real-time comparison of navigation data and IMU calculation data, deception signals are isolated, navigation parameters of the unmanned aerial vehicle are corrected, and it is ensured that the unmanned aerial vehicle can continuously, stably and dynamically fly.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and anti-interference technology, and in particular to a method and system for anti-interference of dynamic navigation of an unmanned aerial vehicle based on a single Beidou satellite. Background Art

[0002] With the rapid development of global satellite navigation systems, drones are increasingly used in various civil and military missions, especially in complex dynamic environments, where drones rely on satellite signals for high-precision navigation. However, existing multi-satellite navigation systems, such as GPS and GLONASS, usually use redundant signals from multiple satellites to ensure navigation accuracy and anti-interference capabilities, but these systems are often at risk of being interfered with in high noise, harsh environments, or when subjected to spoofing attacks. In recent years, the Beidou satellite navigation system has gradually improved and has become an important part of global navigation. For some application scenarios, especially drone flights in complex electromagnetic environments, the Beidou navigation system can independently provide reliable navigation services.

[0003] Most of the existing navigation anti-interference technologies are based on the redundancy of multi-satellite signals to identify and filter interference signals, which is more effective when multiple satellites are visible at the same time. However, in the case of a single satellite (such as a single Beidou satellite), due to the lack of multi-source data support, the anti-interference and anti-deception capabilities are weak, and the system is vulnerable to external electromagnetic interference or even deception signals. In addition, traditional interference detection technologies, such as the automatic gain control (AGC) method, can only identify interference signals with higher power, and cannot effectively detect hidden camouflaged deception signals. This means that in an environment that relies on single satellite navigation, the anti-interference and anti-deception capabilities of drones need to be improved urgently. Existing technologies cannot fully deal with the deception interference of pseudorange and carrier in single satellite signals, especially in scenarios where flight dynamics change dramatically, and traditional methods are prone to misjudgment or loss of navigation data. Summary of the invention

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

[0005] Therefore, the technical problem solved by the present invention is that the existing technology usually relies on the redundant information of multiple satellites for interference detection and anti-interference operations, and cannot cope with the deception and interference scenarios of single satellite signals. In addition, the traditional AGC method has low sensitivity to weak deception signals, which can easily lead to the system being misled by disguised signals that sneak into the loop. The present invention effectively solves the problem of detecting interference and deception signals in a single satellite environment through multi-frequency domain joint filtering and time-frequency domain analysis of carrier phase tracking spectrum, ensuring that high-precision navigation services can still be provided under limited signal conditions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for anti-interference of dynamic navigation of a UAV based on a single Beidou satellite, comprising: the UAV receives a navigation signal through a single Beidou satellite and performs initialization; Interference enhancement detection is performed through multi-frequency domain joint filtering technology; AGC interference detection is performed. The satellite navigation receiver contains two AGCs, one working in the analog intermediate frequency part, which is IF-AGC; the other working in the digital baseband part, which is CH-AGC; A sharp drop in the IF-AGC control voltage indicates strong external interference; a rapid rise or fall in the CH-AGC control voltage reflects interference to the baseband signal; Set power thresholds for different frequency bands for signals in different frequency bands, and introduce multi-frequency domain joint filtering technology based on AGC detection. First, after AGC detects potential interference, the received signal is processed through a multi-frequency domain filter; the filter divides the signal into multiple frequency bands, and each frequency band is analyzed separately; the received signal is divided into three frequency bands, specifically low frequency band 0-1MHz, medium frequency band 1-3MHz, and high frequency band 3-5MHz, which is suitable for the identification of broadband interference and narrowband interference; Perform consistency analysis on signals in multiple frequency bands and set power thresholds for signals in different frequency bands, with the difference not exceeding 3dB. By detecting the power changes between different frequency bands, the spectrum characteristics of the interference signal are further determined; if the signal power of some frequency bands is found to be abnormally higher than that of other frequency bands by more than 3dB, it is judged to be broadband or narrowband interference; A combined filter network is used, in which the bandwidth and center frequency of each filter are dynamically adjusted according to the characteristics of the interference signal. The transfer function of the filter is expressed as: , in, represents the center frequency of the ith frequency band, represents the bandwidth of the filter; j represents the imaginary unit, which is used to construct the calculation of complex form in the complex representation; f represents the frequency parameter, and N represents the number of frequency bands, which is used to indicate the number of frequency bands involved in multi-band processing; The center frequencies of the three frequency bands are , , ,for Set the value to set a small bandwidth when narrowband interference occurs and a large bandwidth when broadband interference occurs. Analyze carrier phase tracking spectrum and conduct autonomous integrity monitoring of a single BeiDou satellite; Isolate deceptive signals and correct navigation parameters to achieve the anti-interference effect of UAV dynamic navigation.

[0007] As a preferred solution of the anti-interference method for dynamic navigation of unmanned aerial vehicles based on a single Beidou satellite described in the present invention, wherein: the unmanned aerial vehicle receives navigation signals through a single Beidou satellite, and initialization includes, under the Beidou satellite navigation system, the unmanned aerial vehicle completes the initialization process of the navigation system by receiving navigation signals transmitted from the Beidou satellite. Initialization includes the initial setting of the antenna to the signal source and guiding the received signal to the receiving module. The receiver performs preliminary power calibration to ensure that it can stably receive signals from the Beidou satellite.

[0008] As a preferred solution of the anti-interference method for dynamic navigation of an unmanned aerial vehicle based on a single Beidou satellite described in the present invention, the analysis of the carrier phase tracking spectrum includes: in order to improve the concealment of deception interference and avoid the receiver identifying the deception signal through the RF circuit AGC detection, it is necessary to identify the type and characteristics of the current loop tracking signal through the time-frequency domain analysis of the post-correlation waveform to complete the detection of the deception signal.

[0009] The receiver phase-locked loop uses I / Q demodulation to complete the phase detection task of the input signal. The loop input signal is expressed as: , in, represents the loop input signal, represents the signal amplitude, D(t) represents the navigation information modulated on the carrier, represents the frequency of the input signal, represents the phase of the input signal, represents the noise term.

[0010] The voltage controlled oscillator outputs two local replica carrier signals and , the frequencies of these two signals are the same, and the phase difference , expressed as: , , in, represents the locally replicated sinusoidal carrier signal, represents the locally replicated cosine carrier signal, represents the angular frequency of the local oscillator, , represents the local oscillator frequency, Indicates the phase of the local carrier.

[0011] The input signal is multiplied by the local replica carrier signal to obtain the in-phase branch signal and orthogonal branch signals .

[0012] Introducing frequency difference and phase difference , after the low-pass filter, the high-frequency part of the first item in the two signals is filtered out, leaving the low-frequency item result and .

[0013] and Constitutes a vector , where the in-phase signal As the real part of the vector, the orthogonal signal As the imaginary part, we get: , in, represents the complex form of the phase detector output signal, represents the imaginary unit, which represents the phase component of the signal, Indicates noise.

[0014] When the spoofing signal frequency is consistent with the satellite signal, , phase shift speed is a uniform speed, and , then: , in, represents the phase difference, represents the input signal phase, represents the local signal phase, It represents the phase deviation speed, which indicates the rate at which the deceptive signal causes phase deviation. Indicates time.

[0015] At this time, the phase discrimination result is expressed as: , in, represents the spoofing signal frequency, Indicates noise.

[0016] Perform Fourier transform, expressed as: , in, represents the frequency spectrum of the phase detector output signal, represents a frequency variable, Represents navigation information modulated on frequency.

[0017] As a preferred solution of the anti-interference method for dynamic navigation of unmanned aerial vehicles based on a single Beidou satellite according to the present invention, the autonomous integrity monitoring of the single Beidou satellite includes recording the pseudorange change in the past 10 seconds in the single Beidou satellite signal, and performing a difference with the current pseudorange. If the difference exceeds 3 times the standard deviation of the normal fluctuation, it is determined that there is a deception signal, and the pseudorange difference is expressed as: , in, represents the pseudorange difference, represents the pseudorange measurement value at the current moment, Represents historical pseudorange data.

[0018] Using IMU data, the current position of the drone is calculated based on the data from the accelerometer and gyroscope, and compared with the navigation data provided by the Beidou satellite. If the position information calculated by the IMU is inconsistent with the navigation result provided by a single Beidou satellite, it is determined to indicate the presence of a spoofing signal.

[0019] The velocity is obtained by integrating the acceleration data of the IMU once, and the position is obtained by integrating the velocity once more.

[0020] As a preferred solution of the method for anti-interference of dynamic navigation of unmanned aerial vehicle based on a single Beidou satellite described in the present invention, the autonomous integrity monitoring of the single Beidou satellite also includes: when the instantaneous change in speed exceeds 10% of the speed before and after, it is determined that a deception signal exists; when the deviation between the position and the position provided by the satellite exceeds the error range, it is determined that a deception signal exists.

[0021] As a preferred solution of the anti-interference method for dynamic navigation of unmanned aerial vehicles based on a single Beidou satellite described in the present invention, wherein: the isolation of deception signals and correction of navigation parameters include interference enhancement detection through multi-frequency domain joint filtering technology, analysis of carrier phase tracking spectrum and autonomous integrity monitoring of a single Beidou satellite, and stopping the processing of the detected deception signals through a signal shielding mechanism to avoid any influence of the deception signals on navigation solution. And the loop will start the automatic isolation function to shield the deception signal source to prevent it from affecting the demodulation of the navigation signal.

[0022] After isolating the spoofing signal, the acceleration data in the IMU inertial measurement unit is used 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 status.

[0023] A UAV dynamic navigation anti-interference system based on a single Beidou satellite, characterized in that: In the initialization module, the UAV receives navigation signals through a single Beidou satellite for initialization.

[0024] The spoofing signal detection module performs interference enhancement detection through multi-frequency domain joint filtering technology. It analyzes the carrier phase tracking spectrum and performs autonomous integrity monitoring of a single Beidou satellite.

[0025] The correction module isolates the deception signals and corrects the navigation parameters to achieve the anti-interference effect of the UAV's dynamic navigation.

[0026] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0027] A computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

[0028] Beneficial effects of the present invention: Through multi-frequency domain joint filtering technology, the system can enhance detection of broadband and narrowband interference signals, separate potential interference frequency bands, and further identify the spectral characteristics of interference signals. By dividing the signal into multiple frequency bands and setting power thresholds for different frequency bands, the detection accuracy of interference signals is effectively improved. By analyzing the carrier phase tracking spectrum, the phase deviation introduced by the deception signal is identified during the carrier phase tracking process. Through the time-frequency domain analysis of the carrier frequency and phase, the disguised deception signal that sneaks into the loop can be identified in the absence of a strong interference signal to ensure the authenticity of the navigation signal. Combined with IMU data and historical pseudo-range changes, the system can isolate the deception signal by comparing the navigation data and IMU estimated data in real time, and correct the navigation parameters of the drone to ensure that the drone can continue to perform dynamic flight stably. IMU data provides an additional source of navigation information for the system, making up for the lack of data in the single satellite navigation system in the detection of deception signals, and improving the robustness of the system through data fusion. Overall, after isolating the spoofing signals, the system can rely on IMU data to complete navigation parameter correction, thereby ensuring the safety and stability of the UAV's flight in complex electromagnetic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0030] Figure 1 An overall flow chart of a method for anti-interference of dynamic navigation of a UAV based on a single Beidou satellite provided for the first embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0032] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a method for anti-interference of dynamic navigation of a UAV based on a single Beidou satellite, comprising: S1: The drone receives navigation signals from a single Beidou satellite and initializes.

[0033] Under the Beidou satellite navigation system, the drone completes the initialization process of the navigation system by receiving the navigation signal transmitted by the Beidou satellite. The initialization includes the initial setting of the antenna to the signal source and guiding the received signal to the receiving module. The receiver performs preliminary power calibration to ensure that it can stably receive the signal from the Beidou satellite.

[0034] It should be noted that the navigation signal mainly includes pseudo-range information, carrier phase, navigation message and timestamp data received from Beidou satellites. Pseudo-range information is calculated by the time difference between the signal sent by the satellite and the signal received by the receiver, reflecting the distance between the satellite and the receiver. Carrier phase is used to improve accuracy and perform precise positioning by analyzing the phase change of the received signal. The navigation message contains satellite orbit information, time correction parameters, etc., which can help the receiver calculate the specific position and operating status of the current satellite. The timestamp is used to calibrate the receiver's clock and compare it with the time sent by the satellite to further ensure the accuracy of positioning.

[0035] In the initialization step, first, the drone captures the currently visible Beidou satellite signal through a single Beidou satellite receiving module, and demodulates the navigation message, pseudorange and carrier phase information. Then, the receiver will preliminarily calculate the current position information and speed state of the drone based on the acquired satellite orbit information and time data. This process is preliminarily estimated through the least squares method, and then enters the anti-interference detection and correction process in the subsequent steps. The key to initialization is to obtain accurate initial position and speed to provide basic support for subsequent dynamic navigation.

[0036] S2: Interference enhancement detection through multi-frequency domain joint filtering technology.

[0037] First, AGC interference detection is performed. The satellite navigation receiver contains two AGCs, one working in the analog intermediate frequency part, which is IF-AGC, and the other working in the digital baseband part, which is CH-AGC.

[0038] A sharp drop in the IF-AGC control voltage indicates strong external interference, while a rapid rise or fall in the CH-AGC control voltage reflects interference to the baseband signal.

[0039] In order to further improve the detection accuracy of broadband and narrowband interference signals, multi-frequency domain joint filtering technology is introduced based on AGC detection.

[0040] It should be noted that in the traditional AGC (automatic gain control) detection method, the system mainly relies on the power change of the signal to detect interference. Although AGC can effectively identify strong interference whose power is significantly higher than that of normal signals, the detection effect of AGC is poor for weak interference whose power is close to that of normal signals. In order to solve this problem, the present invention introduces multi-frequency domain joint filtering technology on the basis of AGC detection, by dividing the received signal into multiple frequency bands and analyzing the power and frequency characteristics of each frequency band separately. This method can refine the analysis of interference signals in each frequency band, especially for broadband and narrowband interference, which can be processed separately, avoiding the limitation of only detecting strong interference signals.

[0041] Furthermore, the design of the combined filter network further enhances the system's adaptability to different interference signals. The filter network filters the signal according to different frequency segments, and by monitoring the power fluctuations in each segment, it can more accurately identify interference signals in different frequency bands. The design idea is to combine multiple bandpass filters together, with each filter responsible for signal analysis in different frequency bands to ensure that abnormal signals in each frequency band can be effectively captured. The advantage is that the system can detect complex multi-band interference signals, especially in high-interference environments. This design significantly improves the accuracy and sensitivity of anti-interference.

[0042] First, after the AGC detects potential interference, the received signal is processed through a multi-frequency domain filter. The filter divides the signal into multiple frequency bands, and each frequency band is analyzed separately. The received signal is divided into three frequency bands, specifically low frequency band 0-1MHz, medium frequency band 1-3MHz, and high frequency band 3-5MHz, which is suitable for the identification of broadband interference and narrowband interference.

[0043] Perform consistency analysis on signals in multiple frequency bands and set power thresholds for signals in different frequency bands, with the difference not exceeding 3dB.

[0044] By detecting the power changes between different frequency bands, the spectrum characteristics of the interference signal can be further determined. If the signal power of some frequency bands is found to be abnormally higher than that of other frequency bands by more than 3dB, it is judged to be broadband or narrowband interference.

[0045] A combined filter network is used, in which the bandwidth and center frequency of each filter are dynamically adjusted according to the characteristics of the interference signal. The transfer function of the filter is expressed as: , in, represents the center frequency of the ith frequency band, Represents the bandwidth of the filter.

[0046] The center frequencies of the three frequency bands are , , ,for Set the value to set a small bandwidth for narrowband interference and a large bandwidth for broadband interference.

[0047] S3: Analyze the carrier phase tracking spectrum and perform autonomous integrity monitoring of a single BeiDou satellite.

[0048] It should be noted that when analyzing the carrier phase tracking spectrum, the phase change of the carrier signal is analyzed in the time and frequency domain to determine whether there is a spoofing signal. When a spoofing signal enters the system, it usually causes abnormal changes in the carrier phase. In particular, when the phase of the spoofing signal is inconsistent with the normal satellite signal, the phase-locked loop (PLL) inside the receiver will produce a phase frequency deviation, which will be reflected in the phase tracking spectrum. Specifically, when the spoofing signal attempts to pull the carrier phase, the system will observe changes in the frequency distribution of the phase, especially irregular oscillations in the low frequency band.

[0049] Furthermore, it can not only detect strong interference signals, but also identify those disguised deception signals that sneak into the system through weak phase disturbances. By analyzing the spectrum changes in the phase tracking spectrum, the system can accurately identify signals with discontinuous or abnormal phase changes, and then determine which signals may be deception signals. Compared with traditional interference detection methods, this step improves the system's ability to identify complex deception signals through detailed analysis of the spectrum, especially when the signal power is close to the normal range, the subtle changes in the carrier phase can provide the system with an effective basis for judgment.

[0050] In order to improve the concealment of deceptive interference and prevent the receiver from identifying the deceptive signal through the RF circuit AGC detection, it is necessary to identify the type and characteristics of the current loop tracking signal through time and frequency domain analysis of the post-correlation waveform to complete the detection of the deceptive signal.

[0051] The receiver phase-locked loop uses I / Q demodulation to complete the phase detection task of the input signal. The loop input signal is expressed as: , in, represents the loop input signal, represents the signal amplitude, D(t) represents the navigation information modulated on the carrier, represents the frequency of the input signal, represents the phase of the input signal, represents the noise term.

[0052] The voltage controlled oscillator outputs two local replica carrier signals and , the frequencies of these two signals are the same, and the phase difference , expressed as: , , in, represents the locally replicated sinusoidal carrier signal, represents the locally replicated cosine carrier signal, represents the angular frequency of the local oscillator, , represents the local oscillator frequency, Indicates the phase of the local carrier.

[0053] The input signal is multiplied by the local replica carrier signal to obtain the in-phase branch signal and orthogonal branch signals , P means instant, and the two-way signals are expressed as: , , in, represents the demodulated output of the in-phase branch, represents the demodulated output of the orthogonal branch signal, represents the signal attenuation factor, D(t) represents the navigation information modulated on the carrier, represents the frequency of the input signal, represents the phase of the input signal, represents the frequency of the local oscillator, represents the phase of the local oscillator, Represents the frequency difference of the signal, Indicates the signal phase difference, represents the noise of the in-phase branch, represents the noise of the orthogonal branches.

[0054] Introducing frequency difference and phase difference , after the low-pass filter, the high-frequency part of the first item in the two signals is filtered out, leaving the low-frequency item result and It is expressed as: , , in, Represents the low-frequency in-phase branch output signal, represents the low-frequency quadrature branch output signal, represents the angular frequency difference, represents the linear frequency difference, Indicates the phase difference, which is the phase difference between the input signal and the local signal. represents the noise of the in-phase branch, represents the noise of the orthogonal branches.

[0055] and Constitutes a vector , where the in-phase signal As the real part of the vector, the orthogonal signal As the imaginary part, we get: , in, represents the complex form of the phase detector output signal, represents the imaginary unit, which represents the phase component of the signal, Indicates noise.

[0056] Assume that the frequency of the deception signal is consistent with the satellite signal, that is, ω i =ω 0 , its phase deviation speed is uniform ω s , and ω s <<ω i , then: θ e =θ i (t)-θ 0 =a s t-θ 0 Among them, θ e Represents the phase difference, θ i (t) represents the input signal phase, θ 0 represents the local signal phase, a s represents the phase deviation speed, which means the rate at which the deceptive signal causes the phase deviation. t represents time.

[0057] At this time, the phase discrimination result is expressed as: , in, represents the spoofing signal frequency, Indicates noise.

[0058] Perform Fourier transform, expressed as: , in, represents the frequency spectrum of the phase detector output signal, represents a frequency variable, Represents navigation information modulated on a frequency.

[0059] By performing spectrum analysis on the output signal of the PLL loop phase detector, it is detected whether the signal contains a fraudulent signal.

[0060] It can be seen from the spectrum of the phase detector output signal that the spectrum obtained after Fourier transform of the PD back-end signal is determined by the speed of the phase deviation of the spoofing signal and the navigation information data. After the spoofing signal acts in the loop, the energy of the phase detector back-end signal is concentrated in the low-frequency part.

[0061] It should be noted that in a single Beidou satellite system, due to the lack of redundant information from multiple satellites, the system must ensure the reliability of navigation data through autonomous integrity monitoring. To this end, the present invention combines IMU data to verify the location information of the drone. The core of autonomous integrity monitoring is to compare the navigation data provided by the IMU (inertial measurement unit) and the Beidou satellite to determine the continuity and consistency of the navigation data. If there is a significant difference between the location data provided by the Beidou satellite and the location data calculated by the IMU, it may indicate that the signal is interfered with or deceived.

[0062] In a single Beidou satellite signal, the pseudorange changes in the past 10 seconds are recorded and differentiated from the current pseudorange. If the difference exceeds 3 times the standard deviation of the normal fluctuation, it is determined that there is a deception signal. The pseudorange difference is expressed as: , in, represents the pseudorange difference, represents the pseudorange measurement value at the current moment, Represents historical pseudorange data.

[0063] Using IMU data, the current position of the drone is calculated based on the data from the accelerometer and gyroscope, and compared with the navigation data provided by the Beidou satellite. If the position information calculated by the IMU is inconsistent with the navigation result provided by a single Beidou satellite, it is determined to indicate the presence of a spoofing signal.

[0064] It should be noted that the velocity is obtained by integrating the acceleration data of the IMU once, and the position is obtained by integrating the velocity once more.

[0065] When the instantaneous change in speed exceeds 10% of the speed before and after, it is determined that a deception signal exists. When the deviation between the position and the position provided by the satellite exceeds the error range, it is determined that a deception signal exists.

[0066] It should be noted that the error range is set based on the dynamic performance of the drone and the complexity of the environment. For example, under static conditions, the error between the position calculated by the IMU and the position provided by the Beidou satellite should not exceed 2 meters, while in high-speed flight conditions, the error range can be appropriately relaxed to 5-10 meters. The actual error setting depends on factors such as flight speed, acceleration, and environmental noise. Ultimately, the system determines a dynamic error range based on the real-time flight environment and historical data. If the deviation between the IMU data and the satellite data exceeds the set threshold, the system determines that the signal is abnormal.

[0067] S4: Isolate deceptive signals and correct navigation parameters to achieve the anti-interference effect of UAV dynamic navigation.

[0068] Through interference enhancement detection using multi-frequency domain joint filtering technology, analysis of carrier phase tracking spectrum, and autonomous integrity monitoring of a single Beidou satellite, the detected deception signal is shielded by a signal shielding mechanism to stop processing the signal and prevent the signal from having any impact on navigation solution. The loop will also start the automatic isolation function to shield the signal source to prevent it from affecting navigation signal demodulation.

[0069] After isolating the spoofing signal, the acceleration data in the IMU inertial measurement unit is used 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 status.

[0070] It should be noted that when the system detects and isolates the spoofing signal, the IMU, as an inertial sensor, measures the acceleration, angular velocity and other data of the drone to provide position and velocity estimation to correct the current navigation parameters. The steps include: Real-time acceleration and angular velocity measurement: The accelerometer and gyroscope in the IMU measure the acceleration and angular velocity of the drone in three directions respectively. The acceleration data is used to calculate the speed and position of the drone, and the angular velocity data is used to calculate the attitude of the drone (such as roll angle, pitch angle and yaw angle).

[0071] Data fusion and extrapolation: The current speed of the drone is obtained by integrating the IMU acceleration data once, and the current position of the drone is obtained by integrating the speed. In addition, by integrating the angular velocity data, the system can extrapolate the attitude change of the drone. After the interference signal is isolated, the IMU provides real-time position information for correcting the navigation parameters of the system.

[0072] Historical data comparison: The system combines IMU data and historical navigation data (such as Beidou satellite navigation data from the previous few seconds) to smooth the current position to prevent sudden navigation offsets. The position information calculated 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.

[0073] Attitude correction: Based on the angular velocity data of the gyroscope, the system can update the attitude information of the drone in real time. This is especially important in complex environments, ensuring that the drone can maintain the stability of its flight attitude even when it is disturbed.

[0074] The computer device may be a server. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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 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 an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a dynamic navigation anti-interference method for a drone based on a single Beidou satellite is implemented.

[0075] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. 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), magnetic 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0076] Example 2 is an embodiment of the present invention, which provides an anti-interference method and system for dynamic navigation of an unmanned aerial vehicle based on a single Beidou satellite. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0077] This embodiment uses two groups of test schemes: one group is based on the existing single Beidou satellite navigation technology, and the other group uses the multi-frequency domain joint filtering technology, carrier phase tracking spectrum analysis and autonomous integrity monitoring of the present invention for anti-interference and correction.

[0078] The drone flies in a complex electromagnetic environment, where there are extensive multi-band interference signals and camouflage deception signals. The environmental noise power density is set to -120 dBm, the power range of the interference signal is between -100 dBm and -110 dBm, and the frequency band ranges from 1 MHz to 5 MHz. The Beidou satellite receiving module used is the BDS-3 single Beidou satellite receiver.

[0079] The existing technology uses a single Beidou satellite navigation to perform pseudo-range measurement and uses the traditional automatic gain control (AGC) method to detect interference signals. The specific steps are as follows: Navigation signal reception: The drone receives pseudo-range information through a single Beidou satellite and transmits current location information through base station and Beidou satellite communication.

[0080] AGC interference detection: During the reception process, the AGC circuit adjusts the signal power. When an abnormally high-power signal is received, the AGC control voltage will be reduced to avoid receiver overload. However, this method mainly relies on the detection of strong interference signals and cannot effectively detect hidden interference signals with power slightly higher than normal signals.

[0081] Navigation data calculation: In the presence of interference signals, the system demodulates the carrier phase, but since it cannot identify weak spoofing signals, there are large errors in the navigation data.

[0082] The solution invented by us: The anti-interference method for dynamic navigation of unmanned aerial vehicles based on a single Beidou satellite proposed in the present invention adopts multi-frequency domain joint filtering technology, carrier phase tracking spectrum analysis and autonomous integrity monitoring, and improves the anti-interference ability of the system through more sophisticated interference detection and parameter correction. The specific steps are as in Example 1.

[0083] During the flight of the drone, two sets of data were recorded in sequence: one set was the results of the prior art solution, and the other set was the results of the method of the present invention. Each test scenario lasted for 120 seconds, and the navigation error of the drone, the interference detection accuracy of the signal, and the anti-interference ability of the system were measured respectively. The experimental results are shown in Table 1.

[0084] Table 1 Experimental data , It can be clearly seen from the above test data that the present invention has shown significant advantages over the prior art in a number of key performance indicators. The AGC method in the prior art can only detect stronger interference signals, resulting in its interference detection accuracy of only 63%. The multi-frequency domain joint filtering technology in the present invention can independently analyze and process the signals of each frequency band, and can not only detect strong interference signals, but also weaker hidden interference signals. Therefore, in three tests, the interference detection accuracy of the present invention remained between 85% and 87%, which is significantly better than the prior art.

[0085] In the prior art, due to the inability to effectively isolate weak spoofing signals, the navigation error is large, reaching an average of 7.2 meters. However, the present invention can accurately identify and isolate spoofing signals through multi-frequency filtering and carrier phase tracking spectrum analysis, avoiding the impact of false signals on navigation solutions, and ultimately reducing the navigation error to a range of 2.2 to 2.5 meters. This significant reduction shows that the present invention can effectively improve navigation accuracy in an anti-interference environment.

[0086] In the prior art, due to the lack of detection capability for weak spoofing signals, the recognition rate of spoofing signals is only 45%. However, the present invention can identify the phase deviation effect introduced by spoofing signals through carrier phase tracking spectrum analysis, especially when detecting carrier phase changes, and the recognition rate reaches 90%-92%. This data shows that the present invention has a high spoofing signal recognition capability and can effectively ensure the safety of drone navigation.

[0087] The existing technical solutions do not realize real-time correction of navigation data, so when the signal is interfered, the system cannot respond in time. However, the present invention uses autonomous integrity monitoring and combines IMU data to make navigation corrections, with an average response time of 32-35 milliseconds, which greatly reduces the response time of the navigation system to interference and ensures the continuous flight of the UAV in complex environments.

[0088] In a complex electromagnetic environment, the navigation error of the UAV in the prior art accumulates rapidly over time, and the navigation error exceeds the expected range within 25 seconds. However, the present invention significantly reduces the rate of error accumulation by timely correcting the navigation parameters, and the error accumulation time is kept between 4 and 6 seconds in three tests. This shows that the present invention can effectively control the accumulation of errors and enhance the anti-interference ability of the system.

[0089] In summary, through the comparison of the test data of this embodiment, it can be clearly seen that the present invention has obvious advantages over the prior art in terms of interference detection, navigation accuracy, spoofing signal identification and navigation parameter correction. The technical solution of the present invention overcomes the problem of insufficient detection capability of weak spoofing signals in the prior art, effectively improves the navigation anti-interference capability of the UAV in complex environments, and provides more stable and reliable technical support for future single Beidou satellite navigation applications.

[0090] Embodiment 3 is an embodiment of the present invention, which provides a UAV dynamic navigation anti-interference system based on a single Beidou satellite, including an initialization module, in which the UAV receives navigation signals through a single Beidou satellite for initialization. A spoofing signal detection module performs interference enhancement detection through multi-frequency domain joint filtering technology. The carrier phase tracking spectrum is analyzed to perform autonomous integrity monitoring of a single Beidou satellite. A correction module isolates the spoofing signal and corrects the navigation parameters to achieve the effect of anti-interference of the UAV dynamic navigation.

[0091] The initialization module is responsible for the initialization operation of the UAV system, including receiving the navigation signal of a single Beidou satellite and performing basic navigation information processing. When the UAV is started, it receives the navigation signal through the Beidou satellite, obtains the pseudorange, carrier phase and time information of the satellite signal, and performs preliminary calculations of the position, speed and time. The main task of this module is to provide basic navigation data for subsequent interference detection and anti-interference operations.

[0092] The spoofing signal detection module is responsible for interference enhancement detection through multi-frequency domain joint filtering technology, and analyzes the carrier phase tracking spectrum to realize autonomous integrity monitoring of the single Beidou satellite signal received by the drone, and identify potential interference or spoofing signals. The module divides the frequency domain of the received signal, analyzes the strength of the signal in each frequency band, and determines whether there is abnormal power fluctuation. Through this interference enhancement detection technology, the system can identify multi-band interference, especially broadband and narrowband interference signals, and improve the accuracy of interference detection. Further analysis of the time-frequency domain changes of the carrier phase, especially for the case where the spoofing signal pulls the carrier phase, can identify low-frequency anomalies in the phase spectrum, thereby detecting hidden spoofing signals. This technology can effectively supplement traditional interference detection methods and identify weak spoofing signals that are not identified by AGC or other simple interference detection technologies. By monitoring the continuity and consistency of the received pseudorange, carrier phase and other information, combined with IMU data, the system can determine whether there is a signal anomaly. This module can not only identify static interference, but also perform real-time detection of navigation signals in dynamic flight.

[0093] After detecting the spoofing signal, the correction module is responsible for isolating the interference signal and correcting the navigation parameters in real time to ensure that the drone can continue to perform accurate navigation operations. When an interference or spoofing signal is detected, the system removes the signal from the navigation solution process through an isolation mechanism. For frequency bands that are determined to be interference signals, the system stops processing the signal in that frequency band to prevent the signal from interfering with the navigation results. After isolating the interference signal, the system uses the acceleration, attitude and other data provided by the IMU (inertial measurement unit) to correct the position and speed. The IMU data is combined with the historical Beidou navigation data to ensure that the system can still provide high-precision navigation information even under the influence of interference signals.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for anti-interference of dynamic navigation of unmanned aerial vehicle based on a single Beidou satellite, characterized in that: include: The drone receives navigation signals from a single BeiDou satellite for initialization; Interference enhancement detection is performed through multi-frequency domain joint filtering technology; AGC interference detection is performed. The satellite navigation receiver contains two AGCs, one working in the analog intermediate frequency part, which is IF-AGC; the other working in the digital baseband part, which is CH-AGC; A sharp drop in the IF-AGC control voltage indicates strong external interference; a rapid rise or fall in the CH-AGC control voltage reflects interference to the baseband signal; The power thresholds of different frequency bands are set for signals in different frequency bands. Based on AGC detection, multi-frequency domain joint filtering technology is introduced. First, after AGC detects potential interference, the received signal is processed through a multi-frequency domain filter. The filter divides the signal into multiple frequency bands, and each frequency band is analyzed separately; the received signal is divided into three frequency bands, specifically divided into low frequency band 0-1MHz, medium frequency band 1-3MHz, and high frequency band 3-5MHz, which is suitable for the identification of broadband interference and narrowband interference; Perform consistency analysis on signals in multiple frequency bands and set power thresholds for signals in different frequency bands, with the difference not exceeding 3dB. By detecting the power changes between different frequency bands, the spectrum characteristics of the interference signal are further determined; if the signal power of some frequency bands is found to be abnormally higher than that of other frequency bands by more than 3dB, it is judged to be broadband or narrowband interference; A combined filter network is used, in which the bandwidth and center frequency of each filter are dynamically adjusted according to the characteristics of the interference signal. The transfer function of the filter is expressed as: ， in, represents the center frequency of the ith frequency band, represents the bandwidth of the filter; j represents the imaginary unit, which is used to construct the calculation of complex form in the complex representation; f represents the frequency parameter, and N represents the number of frequency bands, which is used to indicate the number of frequency bands involved in multi-band processing; The center frequencies of the three frequency bands are , , ,for Set the value to set a small bandwidth when narrowband interference occurs and a large bandwidth when broadband interference occurs. Analyze carrier phase tracking spectrum and conduct autonomous integrity monitoring of a single BeiDou satellite; Isolate deceptive signals and correct navigation parameters to achieve the anti-interference effect of UAV dynamic navigation.

2. The method for anti-interference of dynamic navigation of unmanned aerial vehicle based on a single Beidou satellite as claimed in claim 1, characterized in that: The UAV receives the navigation signal through a single Beidou satellite, and the initialization includes that, under the Beidou satellite navigation system, the UAV completes the initialization process of the navigation system by receiving the navigation signal transmitted from the Beidou satellite; the initialization includes the initial setting of the antenna aligning the signal source and guiding the received signal to the receiving module; The receiver performs preliminary power calibration to ensure that it can stably receive signals from Beidou satellites.

3. The method for anti-interference of dynamic navigation of unmanned aerial vehicle based on a single Beidou satellite as claimed in claim 2, characterized in that: The analysis of the carrier phase tracking spectrum includes: in order to improve the concealment of the deception interference and prevent the receiver from identifying the deception signal through the radio frequency circuit AGC detection, it is necessary to identify the type and characteristics of the current loop tracking signal through the time and frequency domain analysis of the post-correlation waveform to complete the detection of the deception signal; The receiver phase-locked loop uses I / Q demodulation to complete the phase detection task of the input signal. The loop input signal is expressed as: ， in, represents the loop input signal, represents the signal amplitude, D(t) represents the navigation information modulated on the carrier, represents the frequency of the input signal, represents the phase of the input signal, represents the noise term, t represents the time variable; The voltage controlled oscillator outputs two local replica carrier signals and , the frequencies of these two signals are the same, and the phase difference , expressed as: ， ， in, represents the locally replicated sinusoidal carrier signal, represents the locally replicated cosine carrier signal, represents the angular frequency of the local oscillator, , represents the local oscillator frequency, Indicates the phase of the local carrier; The input signal is multiplied by the local replica carrier signal to obtain the in-phase branch signal and orthogonal branch signals ; Introducing frequency difference and phase difference , after the low-pass filter, the high-frequency part of the first item in the two signals is filtered out, leaving the low-frequency item result and ; and Constitutes a vector , where the in-phase signal As the real part of the vector, the orthogonal signal As the imaginary part, we get: ， in, represents the complex form of the phase detector output signal, represents the imaginary unit, which represents the phase component of the signal, Indicates noise; When the spoofing signal frequency is consistent with the satellite signal, , phase shift speed is a uniform speed, and ω s ≪ω i , then: ， in, represents the phase difference, represents the input signal phase, represents the local signal phase, It represents the phase deviation speed, which indicates the rate at which the deceptive signal causes the phase deviation; Indicates time; At this time, the phase discrimination result is expressed as: ， in, represents the spoofing signal frequency, Indicates noise; Perform Fourier transform, expressed as: ， in, represents the frequency spectrum of the phase detector output signal, represents a frequency variable, Represents navigation information modulated on a frequency.

4. The method for anti-interference of dynamic navigation of unmanned aerial vehicle based on a single Beidou satellite as claimed in claim 3, characterized in that: The autonomous integrity monitoring of a single Beidou satellite includes recording the pseudorange change in the past 10 seconds in the single Beidou satellite signal, and performing a difference with the current pseudorange. If the difference exceeds three times the standard deviation of the normal fluctuation, it is determined that a deception signal exists. The pseudorange difference is expressed as: ， in, represents the pseudorange difference, Represents the pseudorange measurement value at the current moment, Represents historical pseudorange data; Using IMU data, the current position of the drone is calculated based on the data from the accelerometer and gyroscope, and compared with the navigation data provided by the Beidou satellite. If the position information calculated by the IMU is inconsistent with the navigation result provided by a single Beidou satellite, it is determined to indicate the presence of a spoofing signal. The velocity is obtained by integrating the acceleration data of the IMU once, and the position is obtained by integrating the velocity once more.

5. The method for anti-interference of dynamic navigation of unmanned aerial vehicle based on a single Beidou satellite as claimed in claim 4, characterized in that: The autonomous integrity monitoring of a single Beidou satellite also includes determining that a deception signal exists when the instantaneous change in speed exceeds 10% of the speed before and after, and determining that a deception signal exists when the position deviation from the position provided by the satellite exceeds an error range.

6. The method for anti-interference of dynamic navigation of unmanned aerial vehicle based on a single Beidou satellite as claimed in claim 5, characterized in that: The isolating the spoofing signal and correcting the navigation parameters include: using interference enhancement detection of multi-frequency domain joint filtering technology, analysis of carrier phase tracking spectrum and autonomous integrity monitoring of a single Beidou satellite, stopping the processing of the detected spoofing signal through a signal shielding mechanism to avoid any influence of the spoofing signal on the navigation solution; and the loop will start the automatic isolation function to shield the spoofing signal source to prevent it from affecting the demodulation of the navigation signal; After isolating the spoofing signal, the acceleration data in the IMU inertial measurement unit is used 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 status.

7. A UAV dynamic navigation anti-interference system based on a single Beidou satellite using the method according to any one of claims 1 to 6, characterized in that: Initialization module: the drone receives navigation signals from a single Beidou satellite for initialization; The spoofing signal detection module performs interference enhancement detection through multi-frequency domain joint filtering technology; analyzes the carrier phase tracking spectrum and performs autonomous integrity monitoring of a single Beidou satellite; The correction module isolates the deception signals and corrects the navigation parameters to achieve the anti-interference effect of the UAV's dynamic navigation.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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