An anti-interference method and system for dynamic navigation of an unmanned aerial vehicle based on a single Beidou satellite

Abstract

The present invention relates to the technical field of satellite navigation and anti-jamming, and discloses a method and system for dynamic navigation anti-jamming of an unmanned aerial vehicle (UAV) based on a single Beidou satellite, including: the UAV receives navigation signals through the single Beidou satellite for initialization. Interference enhancement detection is carried out through multi-frequency domain joint filtering technology. The carrier phase tracking spectrum is analyzed to perform autonomous integrity monitoring on the single Beidou satellite. Spoofing signals are isolated and navigation parameters are corrected to achieve the effect of dynamic navigation anti-jamming of the UAV. The present invention uses multi-frequency domain joint filtering technology and analyzes the carrier phase tracking spectrum to identify spoofing signals that sneak into the loop in the absence of strong interference signals, ensuring the authenticity of navigation signals. By combining IMU data and historical pseudorange changes, and through real-time comparison of navigation data and IMU-derived data, spoofing signals are isolated and the navigation parameters of the UAV are corrected to ensure that the UAV can continuously and stably perform dynamic flight.

Description

Technical Field

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

[0002] With the rapid development of global satellite navigation systems, unmanned aerial vehicles are increasingly widely used in various civilian and military tasks. Especially in complex dynamic environments, unmanned aerial vehicles rely on satellite signals to achieve high-precision navigation. However, existing multi-satellite navigation systems, such as GPS, GLONASS, etc., usually use redundant signals of multiple satellites to ensure the accuracy and anti-jamming ability of navigation. However, these systems often still have the risk of being jammed in high-noise, harsh environments or when subjected to spoofing attacks. In recent years, the Beidou satellite navigation system has been gradually improved and has become an important part of global navigation. For certain application scenarios, especially the flight of unmanned aerial vehicles in complex electromagnetic environments, the Beidou navigation system can independently provide reliable navigation services.

[0003] Most of the existing navigation anti-jamming technologies are based on the redundancy of multi-satellite signals to identify and filter out interference signals, which is relatively effective for the situation where multiple satellites are visible simultaneously. 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-jamming and anti-spoofing capabilities are weak, and the system is easily attacked by external electromagnetic interference or spoofing signals. In addition, traditional interference detection technologies, such as the automatic gain control (AGC) method, can only identify interference signals with relatively high power and cannot effectively detect hidden spoofing signals. This makes the anti-jamming and anti-spoofing capabilities of unmanned aerial vehicles in an environment relying on a single satellite navigation urgently need to be improved. The existing technologies cannot fully cope with the spoofing interference of pseudo-range and carrier in single satellite signals. Especially in scenarios where the flight dynamics change violently, traditional methods are prone to misjudgment or loss of navigation data. Summary of the Invention

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

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

[0006] To solve the above technical problems, the present invention provides the following technical solution: A method for anti-interference of UAV dynamic navigation based on a single Beidou satellite, including: The UAV receives navigation signals through a single Beidou satellite for initialization;

[0007] Perform interference enhancement detection through multi-frequency domain joint filtering technology;

[0008] Perform AGC interference detection. There are two AGCs in the satellite navigation receiver. One works in the analog intermediate frequency part, which is IF-AGC; the other works in the digital baseband part, which is CH-AGC;

[0009] A sharp drop in the control voltage of IF-AGC indicates strong external interference; a rapid rise or fall in the control voltage of CH-AGC reflects interference on the baseband signal;

[0010] Set different power thresholds for signals in different frequency bands. Based on AGC detection, introduce multi-frequency domain joint filtering technology. First, after AGC detects potential interference, process the received signal 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 3 frequency bands, specifically divided into a low frequency band of 0 - 1 MHz, a medium frequency band of 1 - 3 MHz, and a high frequency band of 3 - 5 MHz to adapt to the identification of wideband interference and narrowband interference;

[0011] Perform consistency analysis on the signals in multiple frequency bands, and set different power thresholds for signals in different frequency bands, with a difference not exceeding 3 dB;

[0012] Further determine the spectral characteristics of the interference signal by detecting the power change between different frequency bands; if it is found that the signal power in some frequency bands is abnormally higher than that in other frequency bands by more than 3 dB, it is judged as wideband or narrowband interference;

[0013] Adopt a combined filter network, where the bandwidth and center frequency of each filter are dynamically adjusted according to the characteristics of the interference signal, and the transfer function of the filter is expressed as:

[0014]

[0015] Among them, f i represents the center frequency of the i-th frequency band, Δf i represents the bandwidth of the filter; j represents the imaginary unit, which is used to construct complex calculations in complex number representations; 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;

[0016] The center frequencies of the three frequency bands are f1 = 0.5 MHz, f2 = 2 MHz, and f3 = 4 MHz respectively. For Δf iTake values, set a small bandwidth for narrowband interference and a large bandwidth for broadband interference;

[0017] Analyze the carrier phase tracking spectrum and perform autonomous integrity monitoring on a single Beidou satellite;

[0018] Isolate spoofing signals, correct navigation parameters, and achieve the effect of anti-interference for UAV dynamic navigation.

[0019] As a preferred solution of the UAV dynamic navigation anti-interference method based on a single Beidou satellite described in the present invention, wherein: the UAV receives navigation signals through a single Beidou satellite, and the initialization includes, under the Beidou satellite navigation system, the UAV completes the initialization process of the navigation system by receiving navigation signals transmitted from the Beidou satellite. The initialization includes aligning the antenna with the signal source for initial settings and guiding the received signals into the receiving module. The receiver performs preliminary power calibration to ensure stable reception of signals from the Beidou satellite.

[0020] As a preferred solution of the UAV dynamic navigation anti-interference method based on a single Beidou satellite described in the present invention, wherein: the analysis of the carrier phase tracking spectrum includes that for spoofing interference, in order to improve concealment and avoid the receiver detecting and identifying spoofing signals through the AGC of the RF circuit, it is necessary to analyze the time-frequency domain of the post-correlation waveform to identify the type and characteristics of the currently loop-tracked signal and complete the detection of spoofing signals.

[0021] The receiver phase-locked loop uses the I / Q demodulation method to complete the phase discrimination task of the input signal. The loop input signal is expressed as:

[0022]

[0023] wherein, u i (t) represents the loop input signal, a represents the signal amplitude, D(t) represents the navigation information modulated on the carrier, a i represents the frequency of the input signal, θ i represents the phase of the input signal, and n represents the noise term.

[0024] The voltage-controlled oscillator outputs two local replica carrier signals u os (t) and u oc (t). These two signals have the same frequency and a phase difference of π / 2, and are expressed as:

[0025]

[0026] wherein, u os (t) represents the locally replicated sine carrier signal, u oc(t) represents the locally replicated cosine carrier signal, a0 represents the angular frequency of the local oscillator, a0 = 2πf0, f0 represents the local oscillator frequency, and θ0 represents the phase of the local carrier.

[0027] The input signal is multiplied by the locally replicated carrier signal to obtain the in-phase branch signal i P (t) and the quadrature branch signal q P (t).

[0028] A frequency difference ω e = ω i - ω0 and a phase difference θ e = θ i - θ0 are introduced. After passing through a low-pass filter, the high-frequency part of the first term in the two signals is filtered out, and the remaining low-frequency term results in I P (t) and Q p (t).

[0029] I P (t) and Q p (t) form the vector r p (t), where the in-phase signal I P (t) serves as the real part of the vector, and the quadrature signal Q p (t) serves as the imaginary part, resulting in:

[0030]

[0031] where r p (t) represents the output signal of the phase detector in complex form, j represents the imaginary unit, represents the phase component of the signal, represents the noise.

[0032] When the frequency of the spoofing signal is consistent with the satellite signal, ω i = ω0, the phase deviation speed ω s is uniform, and ω s << ω i , then there is:

[0033] θ e = θ i (t)-θ0 = a s t - θ0

[0034] where θ e represents the phase difference, θ i (t) represents the phase of the input signal, θ0 represents the phase of the local signal, a s represents the phase deviation speed, representing the rate of phase shift caused by the spoofing signal. t represents time.

[0035] At this time, the phase detection result is expressed as:

[0036]

[0037] Among them, ω s represents the spoofing signal frequency, and represents noise.

[0038] Perform Fourier transform, which is expressed as:

[0039]

[0040] Among them, R P (ω) represents the spectrum of the output signal of the phase discriminator, ω represents the frequency variable, and D(ω - ω s ) represents the navigation information modulated on the frequency.

[0041] As a preferred scheme of the method for anti-interference of UAV dynamic navigation based on a single Beidou satellite according to the present invention, among them: the autonomous integrity monitoring of the single Beidou satellite includes, in the single Beidou satellite signal, recording the change of the pseudorange in the past 10 seconds, performing a difference with the current pseudorange, and if the difference exceeds 3 times the standard deviation of the normal fluctuation, it is determined that there is a spoofing signal. The pseudorange difference is expressed as:

[0042] ΔP = P current -P past

[0043] Among them, ΔP represents the pseudorange difference, P current represents the current pseudorange measurement value, and P past represents the historical pseudorange data.

[0044] Use IMU data to calculate the current position information of the UAV based on the data of the accelerometer and gyroscope, and compare it 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 the single Beidou satellite, it is determined that there is a spoofing signal.

[0045] Integrate the acceleration data of the IMU once to obtain the velocity, and then integrate the velocity once to obtain the position.

[0046] As a preferred scheme of the method for anti-interference of UAV dynamic navigation based on a single Beidou satellite according to the present invention, among them: the autonomous integrity monitoring of the single Beidou satellite further includes, when the instantaneous change of the velocity exceeds 10% of the velocities at the previous and subsequent moments, it is determined that there is a spoofing signal, and when the deviation between the position and the position provided by the satellite exceeds the error range, it is determined that there is a spoofing signal.

[0047] As a preferred solution of the anti-interference method for UAV dynamic navigation based on a single Beidou satellite of the present invention, wherein: isolating spoofing signals and correcting navigation parameters includes, through interference enhancement detection by multi-frequency domain joint filtering technology, analysis of carrier phase tracking spectrum, and autonomous integrity monitoring of a single Beidou satellite, for the detected spoofing signals, through a signal shielding mechanism, stop processing the spoofing signals to avoid any impact of the spoofing signals on 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 navigation signals.

[0048] After isolating the spoofing signals, use the acceleration data in the IMU inertial measurement unit to correct the current position and speed of the UAV. When the spoofing signals are isolated, update the navigation parameters through the real-time speed and acceleration calculated by the IMU to ensure the accuracy of the flight state.

[0049] An anti-interference system for UAV dynamic navigation based on a single Beidou satellite, characterized by including:

[0050] An initialization module, the UAV receives navigation signals through a single Beidou satellite for initialization.

[0051] A spoofing signal detection module, which performs interference enhancement detection through multi-frequency domain joint filtering technology. Analyze the carrier phase tracking spectrum and perform autonomous integrity monitoring on a single Beidou satellite.

[0052] A correction module, which isolates spoofing signals and corrects navigation parameters to achieve the effect of anti-interference for UAV dynamic navigation.

[0053] A computer device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method described above.

[0054] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the method described above.

[0055] Advantages of the present invention: Through the multi-frequency domain joint filtering technology, the system can enhance the 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, during the carrier phase tracking process, the phase deviation introduced by spoofing signals is identified. Through the time-frequency domain analysis of the carrier frequency and phase, in the absence of strong interference signals, the disguised spoofing signals sneaking into the loop can be identified to ensure the authenticity of navigation signals. Combining IMU data and historical pseudorange changes, the system can isolate spoofing signals and correct the navigation parameters of the UAV by comparing the navigation data and the IMU-derived data in real time, ensuring that the UAV can fly dynamically in a continuous and stable manner. IMU data provides an additional navigation information source for the system, compensating for the data deficiency in spoofing signal detection of a single satellite navigation system and improving the robustness of the system through data fusion. Overall, after isolating spoofing signals, the system can rely on IMU data to complete the correction of navigation parameters, thereby ensuring the flight safety and stability of the UAV in a complex electromagnetic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] 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, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0057] Figure 1 FIG. is the overall flowchart of a method for anti-interference of UAV dynamic navigation based on a single Beidou satellite provided by the first embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] To make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0059] Example 1, referring to Figure 1 , which is an embodiment of the present invention, provides a method for anti-interference of UAV dynamic navigation based on a single Beidou satellite, including:

[0060] S1: The UAV receives navigation signals through a single Beidou satellite and performs initialization.

[0061] Under the Beidou satellite navigation system, the UAV completes the initialization process of the navigation system by receiving navigation signals transmitted from Beidou satellites. Initialization includes aligning the antenna with the signal source for initial settings and guiding the received signals into the receiving module. The receiver performs preliminary power calibration to ensure stable reception of signals from Beidou satellites.

[0062] It should be noted that the navigation signals mainly include data such as pseudorange information, carrier phase, navigation message, and timestamp received from Beidou satellites. The pseudorange information is calculated based on the time difference between the signals sent by the satellite and those received by the receiver, reflecting the distance between the satellite and the receiver. The carrier phase is used to improve accuracy and perform precise positioning by analyzing the phase changes of the received signals. The navigation message contains satellite orbit information, time correction parameters, etc., which can help the receiver calculate the specific position and operating state of the current satellite. The timestamp is used to correct the receiver's clock and compare it with the time sent by the satellite to further ensure the accuracy of positioning.

[0063] In the initialization step, first, the UAV captures the visible Beidou satellite signals through a single Beidou satellite receiving module and demodulates the navigation message, pseudorange, and carrier phase information. Then, the receiver preliminarily calculates the current position information and speed state of the UAV based on the obtained satellite orbit information and time data. This process is initially estimated by 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.

[0064] S2: Perform interference enhancement detection through multi-frequency domain joint filtering technology.

[0065] First, perform AGC interference detection. There are two AGCs in the satellite navigation receiver. One works in the analog intermediate frequency part, which is the IF-AGC. The other works in the digital baseband part, which is the CH-AGC.

[0066] A sharp drop in the control voltage of the IF-AGC indicates strong external interference. A rapid rise or fall in the control voltage of the CH-AGC reflects interference to the baseband signal.

[0067] To further improve the detection accuracy of wideband and narrowband interference signals, multi-frequency domain joint filtering technology is introduced based on AGC detection.

[0068] It should be noted that in traditional AGC (Automatic Gain Control) detection methods, the system mainly relies on the power change of the signal to detect interference. Although AGC can effectively identify strong interference with power significantly higher than that of normal signals, for weak interference with power close to that of normal signals, the detection effect of AGC is poor. To solve this problem, the present invention introduces a multi-frequency domain joint filtering technique on the basis of AGC detection. By dividing the received signal into multiple frequency bands, the power and frequency characteristics of each frequency band are analyzed separately. This method can refine the analysis of interference signals in each frequency band. Especially for wideband and narrowband interference, they can be processed separately, avoiding the limitation of only detecting strong interference signals.

[0069] Furthermore, the design of the combined filter network further enhances the adaptability of the system to different interference signals. The filter network filters the signal according to different frequency sections. By monitoring the power fluctuation of each section, it can more accurately identify interference signals in different frequency bands. The design idea is to combine multiple band-pass filters together, and each filter is responsible for the signal analysis of 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 a high-interference environment, this design significantly improves the anti-interference accuracy and sensitivity.

[0070] First, after AGC detects potential interference, the received signal is processed by 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 3 frequency bands, specifically divided into a low-frequency band of 0 - 1 MHz, a middle-frequency band of 1 - 3 MHz, and a high-frequency band of 3 - 5 MHz, to adapt to the identification of wideband and narrowband interference.

[0071] Perform consistency analysis on the signals of multiple frequency bands, and set different power thresholds for signals in different frequency bands, with a difference not exceeding 3 dB.

[0072] By detecting the power change between different frequency bands, further determine the spectral characteristics of the interference signal. If it is found that the signal power in certain frequency bands is abnormally higher than that in other frequency bands by more than 3 dB, it is judged as wideband or narrowband interference.

[0073] Adopt a combined filter network, where 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:

[0074]

[0075] where, f i represents the center frequency of the i-th frequency band, and Δf i represents the bandwidth of the filter.

[0076] The center frequencies of the three frequency bands are f1 = 0.5 MHz, f2 = 2 MHz, and f3 = 4 MHz respectively. For Δf i When taking values, a small bandwidth is set for narrowband interference, and a large bandwidth is set for broadband interference.

[0077] S3: Analyze the carrier phase tracking spectrum and perform autonomous integrity monitoring on a single Beidou satellite.

[0078] It should be noted that when analyzing the carrier phase tracking spectrum, by performing time-frequency domain analysis on the phase change of the carrier signal, it is judged whether there is a spoofing signal. When a spoofing signal enters the system, it usually causes abnormal changes in the carrier phase. Especially when the phase of the spoofing signal is inconsistent with that of the normal satellite signal, the phase-locked loop (PLL) inside the receiver will generate a phase frequency offset, and this frequency offset will be reflected in the phase tracking spectrum. Specifically, when the spoofing signal attempts to pull the carrier phase, the system will observe a change in the frequency distribution of the phase, especially irregular oscillations will occur in the low-frequency band.

[0079] Furthermore, it can not only detect strong interference signals, but also identify those camouflaged spoofing signals that sneak into the system through weak phase perturbations. By analyzing the spectral changes in the phase tracking spectrum, the system can accurately identify signals with discontinuous or abnormal phase changes, and then judge which signals may be spoofing signals. Compared with traditional interference detection methods, this step improves the system's ability to identify complex spoofing 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 an effective basis for the system to make judgments.

[0080] In order to improve the concealment of spoofing interference and prevent the receiver from detecting and identifying spoofing signals through the AGC of the radio frequency circuit, it is necessary to perform time-frequency domain analysis on the post-correlation waveform to identify the types and characteristics of the currently loop-tracked signals and complete the detection of spoofing signals.

[0081] The receiver phase-locked loop uses the I / Q demodulation method to complete the phase discrimination task of the input signal. The loop input signal is expressed as:

[0082]

[0083] where, u i (t) represents the loop input signal, a represents the signal amplitude, D(t) represents the navigation information modulated on the carrier, a i represents the frequency of the input signal, θ i represents the phase of the input signal, and n represents the noise term.

[0084] The voltage-controlled oscillator outputs two local replica carrier signals u os (t) and u oc(t), the frequencies of these two signals are the same, and the phase difference is π / 2, expressed as:

[0085]

[0086] where u os (t) represents the locally replicated sine carrier signal, u oc (t) represents the locally replicated cosine carrier signal, a0 represents the angular frequency of the local oscillator, a0 = 2πf0, f0 represents the local oscillator frequency, and θ0 represents the phase of the local carrier.

[0087] The input signal is multiplied by the locally replicated carrier signal to obtain the in-phase branch signal i P (t) and the quadrature branch signal q P (t). P represents instantaneously, and the two signals are expressed as:

[0088] i p (t) = -αD(t)[cos((ω i +ω0)t+(θ i +θ0)) - cos((ω i -ω0)t+(θ i -θ0))] + n i,p

[0089] q P (t) = αD(t)[sin((α ξ +α0)t+(θ ξ +θ0)) + sin((α ξ -α0)t+(θ ξ -θ0))] + n q,z

[0090] where i p (t) represents the demodulation output of the in-phase branch, q P (t) represents the demodulation output of the quadrature branch signal, α represents the signal attenuation factor, D(t) represents the navigation information modulated on the carrier, ω i represents the frequency of the input signal, θ i represents the phase of the input signal, ω0 represents the frequency of the local oscillator, θ0 represents the phase of the local oscillator, α ξ represents the frequency difference amount of the signal, θ ξ represents the signal phase difference amount, n i,p represents the noise of the in-phase branch, n q,z represents the noise of the quadrature branch.

[0091] Introduce the frequency difference amount ω e = ω i -ω0 and the phase difference amount θ e = θi -θ0, after passing through a low-pass filter, the high-frequency part of the first term in the two signals is filtered out, and the remaining low-frequency term results in I P (t) and Q p (t) is expressed as:

[0092]

[0093] where, I p (t) represents the output signal of the low-frequency in-phase branch, Q P (t) represents the output signal of the low-frequency quadrature branch,

[0094] a e represents the angular frequency difference, θ e represents the linear frequency difference, θ e represents the phase difference amount, representing the phase difference between the input signal and the local signal. represents the noise of the in-phase branch, represents the noise of the quadrature branch.

[0095] I P (t) and Q p (t) constitute the vector r p (t), where the in-phase signal I P (t) serves as the real part of the vector, and the quadrature signal Q p (t) serves as the imaginary part, obtaining:

[0096]

[0097] where, r p (t) represents the output signal of the phase detector in complex form, j represents the imaginary unit, representing the phase component of the signal, represents the noise.

[0098] Assume that the frequency of the spoofing signal is consistent with the satellite signal, i.e., ω i = ω0, and its phase deviation speed is uniform ω s , and ω s << ω i , then there is:

[0099] θ e = θ i (t) - θ0 = a s t - θ0

[0100] where, θ e represents the phase difference amount, θ i (t) represents the phase of the input signal, θ0 represents the phase of the local signal, a s represents the phase deviation speed, representing the rate of phase shift caused by the spoofing signal. t represents time.

[0101] At this time, the phase discrimination result is expressed as:

[0102]

[0103] Among them, ω s represents the frequency of the spoofing signal, represents the noise.

[0104] Performing Fourier transform is expressed as:

[0105]

[0106] Among them, R P (ω) represents the spectrum of the output signal of the phase discriminator, ω represents the frequency variable, D(ω - ω s ) represents the navigation information modulated on the frequency.

[0107] By performing spectral analysis on the output signal of the PLL loop phase discriminator, it is detected whether there is a spoofing signal in the signal.

[0108] It can be seen from the spectrum of the output signal of the phase discriminator that the spectrum obtained after performing Fourier transform on the PD backend signal is determined by the speed of the phase deviation of the spoofing signal and the navigation information data. After the action of the spoofing signal in the loop, the energy of the backend signal of the phase discriminator is concentrated in the low-frequency part.

[0109] It should be noted that in the single Beidou satellite system, due to the lack of redundant information of multiple satellites, the system must ensure the reliability of navigation data through autonomous integrity monitoring. For this purpose, the present invention combines IMU data to verify the position information of the UAV. The core of autonomous integrity monitoring lies in comparing the navigation data provided by the IMU (Inertial Measurement Unit) and the Beidou satellite to judge the continuity and consistency of the navigation data. If there is a significant difference between the position data provided by the Beidou satellite and the position data calculated by the IMU, it may indicate that the signal is interfered or spoofed.

[0110] In the single Beidou satellite signal, record the change of the pseudorange in the past 10 seconds, perform differential with the current pseudorange. If the difference exceeds 3 times the standard deviation of the normal fluctuation, it is determined that there is a spoofing signal. The pseudorange difference is expressed as:

[0111] ΔP = P current - P past

[0112] Among them, ΔP represents the pseudorange difference, P current represents the current pseudorange measurement value, P past represents the historical pseudorange data.

[0113] Using IMU data, the current position information of the UAV is calculated based on the data of 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 that there is a spoofing signal.

[0114] 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 again.

[0115] When the instantaneous change in velocity exceeds 10% of the velocities at the previous and current moments, it is determined that there is a spoofing signal. When the deviation between the position and the position provided by the satellite exceeds the error range, it is determined that there is a spoofing signal.

[0116] It should be noted that the setting of the error range is based on the dynamic performance of the UAV and the environmental complexity. 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 the high-speed flight state, 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. Finally, the system determines a dynamic error range according to 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.

[0117] S4: Isolate the spoofing signal, correct the navigation parameters, and achieve the effect of anti-interference for the dynamic navigation of the UAV.

[0118] Through the interference enhancement detection of the multi-frequency domain joint filtering technology, the analysis of the carrier phase tracking spectrum, and the autonomous integrity monitoring of a single Beidou satellite, for the detected spoofing signal, through the signal shielding mechanism, the processing of this signal is stopped to avoid any impact of this signal on the navigation solution. And the loop will start the automatic isolation function to shield this signal source to prevent it from affecting the demodulation of the navigation signal.

[0119] After isolating the spoofing signal, the acceleration data in the IMU inertial measurement unit is used to correct the current position and velocity of the UAV. When the spoofing signal is isolated, the real-time velocity and acceleration calculated by the IMU are used to update the navigation parameters to ensure the accuracy of the flight state.

[0120] It should be noted that when the system detects and isolates the spoofing signal, the IMU, as an inertial sensor, provides the calculation of the position and velocity by measuring data such as the acceleration and angular velocity of the UAV, so as to correct the current navigation parameters. It includes the following steps:

[0121] 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, while the angular velocity data is used to calculate the attitude of the drone (such as roll angle, pitch angle and yaw angle).

[0122] Data fusion and calculation: By integrating the IMU acceleration data once, the current speed of the drone is obtained, and then by integrating the speed, the current position information of the drone is obtained. In addition, by integrating the angular velocity data, the system can calculate the attitude change of the drone. After the interference signal is isolated, the IMU provides instant position information for correcting the navigation parameters of the system.

[0123] Historical data comparison: The system combines the 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 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.

[0124] Attitude correction: According to the angular velocity data of the gyroscope, the system can update the attitude information of the drone in real time. This is particularly important in complex environments to ensure that the drone can maintain the stability of its flight attitude even when disturbed.

[0125] 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-interference of dynamic navigation of a drone based on a single Beidou satellite.

[0126] Those of ordinary skill in the art can understand that all or part of the processes in the methods of 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 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 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 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.

[0127] Embodiment 2 is an embodiment of the present invention, which provides a method and system for anti-interference of 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.

[0128] Two sets of test schemes are selected in this embodiment: one is based on the existing single Beidou satellite navigation technology, and the other 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.

[0129] When the drone flies in a complex electromagnetic environment, there are extensive multi-band interference signals and camouflage deception signals in the environment. The environmental noise power density is set to -120 dBm, and the power range of the interference signals is between -100 dBm and -110 dBm, with the frequency band varying from 1 MHz to 5 MHz. The Beidou satellite receiving module used is a BDS-3 single Beidou satellite receiver.

[0130] The prior art measures the pseudorange through single Beidou satellite navigation and uses the traditional automatic gain control (AGC) method to detect interference signals. The specific steps are as follows:

[0131] Navigation signal reception: The drone receives pseudorange information through a single Beidou satellite and transmits the current position information through communication between the base station and the Beidou satellite.

[0132] AGC interference detection: During the reception process, the AGC circuit adjusts the signal power. When a signal with an abnormally large power is received, the AGC control voltage will decrease to avoid receiver overload. However, this method mainly relies on the detection of strong interference signals and cannot effectively detect concealed interference signals with a power slightly higher than the normal signal.

[0133] Navigation data calculation: Under the interference signal, the system demodulates the carrier phase. However, due to the inability to identify weak deception signals, there are large errors in the navigation data.

[0134] The solution of our invention: The anti-jamming method for dynamic navigation of drones based on a single Beidou satellite proposed in this invention adopts multi-frequency domain joint filtering technology, carrier phase tracking spectrum analysis, and autonomous integrity monitoring, and improves the anti-jamming ability of the system through more refined interference detection and parameter correction. The specific steps are as in Embodiment 1.

[0135] During the flight of the drone, two groups of data are recorded in sequence: one group is the result of the prior art solution, and the other group is the result of using the method of this invention. Each test scenario lasts for 120 seconds, and the navigation error of the drone, the interference detection accuracy rate of the signal, and the anti-jamming ability of the system are measured respectively. The experimental results are shown in Table 1.

[0136] Table 1 Experimental data

[0137]

[0138] It can be clearly seen from the above test data that the present invention shows significant advantages over the prior art in multiple key performance indicators. The AGC method in the prior art can only detect strong interference signals, resulting in an interference detection accuracy rate of only 63%. The multi-frequency domain joint filtering technology in the present invention can independently analyze and process signals in each frequency band, and can not only detect strong interference signals, but also detect weak and hidden interference signals. Therefore, in the three tests, the interference detection accuracy rate of the present invention remains between 85% and 87%, which is significantly better than the prior art.

[0139] In the prior art, due to the inability to effectively isolate weak spoofing signals, the navigation error is relatively large, averaging 7.2 meters. The present invention can accurately identify and isolate spoofing signals through multi-frequency filtering and carrier phase tracking spectrum analysis, avoiding the influence of false signals on navigation solution, and finally reducing the navigation error to the range of 2.2 to 2.5 meters. This significant reduction shows that the present invention can effectively improve the navigation accuracy in an anti-interference environment.

[0140] In the prior art, due to the lack of the ability to detect weak spoofing signals, the spoofing signal recognition rate is only 45%. The present invention can identify the phase offset effect introduced by spoofing signals through carrier phase tracking spectrum analysis, especially when detecting the change of carrier phase, and the recognition rate reaches 90%-92%. This data shows that the present invention has a high spoofing signal recognition ability and can effectively ensure the safety of UAV navigation.

[0141] In the prior art solution, real-time correction of navigation data is not achieved. Therefore, when the signal is interfered, the system cannot respond in time. The present invention conducts navigation correction by combining IMU data through autonomous integrity monitoring, and the average response time is within 32-35 milliseconds, greatly reducing the response time of the navigation system to interference and ensuring the continuous flight of the UAV in a complex environment.

[0142] 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. The present invention significantly reduces the rate of error accumulation by timely correcting navigation parameters, and the error accumulation time remains between 4 and 6 seconds in the three tests. This shows that the present invention can effectively control the accumulation of errors and enhance the anti-interference ability of the system.

[0143] In summary, through the comparison of the test data in this embodiment, it can be clearly seen that compared with the prior art, the present invention shows obvious advantages in interference detection, navigation accuracy, spoofing signal recognition, and navigation parameter correction. The technical solution of the present invention overcomes the problem of insufficient ability to detect weak spoofing signals in the prior art, effectively improves the anti-interference ability of the UAV navigation in complex environments, and provides more stable and reliable technical support for future single Beidou satellite navigation applications.

[0144] Embodiment 3, an embodiment of the present invention, provides a UAV dynamic navigation anti-interference system based on a single Beidou satellite, including an initialization module. 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. Analyze the carrier phase tracking spectrum to perform autonomous integrity monitoring on the single Beidou satellite. A correction module isolates spoofing signals and corrects navigation parameters to achieve the effect of UAV dynamic navigation anti-interference.

[0145] The initialization module is responsible for the initialization operation of the UAV system, including receiving navigation signals from a single Beidou satellite and performing basic navigation information processing. When the UAV starts, it receives navigation signals through the Beidou satellite, obtains pseudorange, carrier phase, and time information of the satellite signal, and performs preliminary calculations of position, speed, and time. The main task of this module is to provide basic navigation data for subsequent interference detection and anti-interference operations.

[0146] The spoofing signal detection module is responsible for performing interference enhancement detection through multi-frequency domain joint filtering technology and analyzing the carrier phase tracking spectrum to achieve autonomous integrity monitoring of the single Beidou satellite signals received by the UAV and identify potential interference or spoofing signals. This module divides the frequency domain of the received signal, analyzes the intensity of signals in each frequency band, and determines whether there are abnormal power fluctuations. Through this interference enhancement detection technology, the system can identify multi-band interference, especially wide-band and narrow-band interference signals, and improve the accuracy of interference detection. Further analyze the time-frequency domain changes of the carrier phase, especially in the case of spoofing signals pulling the carrier phase, and low-frequency abnormalities can be identified 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 recognized by AGC or other simple interference detection technologies. By monitoring the continuity and consistency of the received pseudorange, carrier phase and other information, and combining IMU data, the system can determine whether there are signal abnormalities. This module can not only identify static interference but also perform real-time detection on navigation signals during dynamic flight.

[0147] After detecting the spoofing signal, the correction module is responsible for isolating the interference signal and ensuring that the UAV can continue to perform accurate navigation operations by correcting the navigation parameters in real time. When an interference or spoofing signal is detected, the system eliminates the signal from the navigation solution process through an isolation mechanism. For the frequency band determined to be an interference signal, the system stops processing the signal in that frequency band to prevent the signal from interfering with the navigation result. 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.

[0148] 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-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: Among them, f i represents the center frequency of the ith frequency band, Δf i 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 f1 = 0.5 MHz, f2 = 2 MHz, and f3 = 4 MHz. i 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 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: ΔP=P current -P past , Where ΔP represents the pseudorange difference, P current Represents the pseudorange measurement value at the current moment, P past 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.

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 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 deviation between the position and the position provided by the satellite exceeds an error range.

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 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.

6. A UAV dynamic navigation anti-interference system based on a single Beidou satellite using the method according to any one of claims 1 to 5, 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.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. 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 5 are implemented.

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