Signal interference shielding method for unmanned aerial vehicle navigation

Abstract

The invention relates to the technical field of unmanned aerial vehicle navigation and anti-interference, and discloses a signal interference shielding method for unmanned aerial vehicle navigation, which comprises the following steps of: performing communication perception and micro-motion perception cooperative scanning through a radio receiver and an airborne ultra-wideband radar; a mobile cooperative interference platform is identified in combination with dual consistency verification and a motion intention identification algorithm, frequency hopping is performed on the identified interference platform to an anti-tracking standby frequency point, and a carrier phase and a pseudo-range predicted value of the hopping frequency point are regenerated in real time by using inertial navigation and visual odometer data, so that smooth transition and fusion of navigation observation values are realized; and meanwhile, by constructing a multi-dimensional integrity monitoring mechanism, when the satellite navigation reliability is continuously reduced, a navigation mode is switched in a weight gradual change mode. According to the method, a full-closed-loop self-adaptive anti-interference mode from sensing, recognition, decision making to recovery is constructed, and the navigation robustness and task reliability of the unmanned aerial vehicle in a complex electromagnetic environment are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) navigation and anti-interference technology, and in particular to a signal interference shielding method for UAV navigation. Background Technology

[0002] Currently, the field of anti-jamming for UAV navigation mainly focuses on signal-level countermeasures. However, some new jamming systems are mobile platforms carrying jamming sources, which have the ability to quickly reconstruct and track signals. Traditional anti-jamming strategies are easily intercepted and tracked, and lack the perception and identification of the physical entity of the jamming platform, resulting in blind anti-jamming decisions and delayed responses.

[0003] Existing research on the fusion of radar and communication signals is mostly limited to static scenes or known target databases, lacking real-time micro-motion feature extraction and behavioral intent analysis of small maneuvering targets. Especially in complex terrain and urban environments, radar echoes are easily affected by multipath and clutter, making it difficult to stably extract micro-moving targets that are spatiotemporally correlated with jamming signals, resulting in the difficulty in accurately identifying cooperative jamming platforms.

[0004] Regarding navigation recovery mechanisms, existing methods mostly adopt a hard switching mode, that is, directly switching to inertial or visual navigation after satellite navigation failure. They lack a mechanism for predicting and fusing observations during the transition phase. At the same time, existing integrity monitoring mostly relies on a single satellite navigation protection level and lacks the ability to comprehensively evaluate the consistency of multiple sensors and the quality of signal reacquisition after frequency hopping, making it difficult to achieve smooth and reliable mode switching. Summary of the Invention

[0005] The purpose of this invention is to provide a signal interference shielding method for unmanned aerial vehicle (UAV) navigation in order to solve the problems in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a signal interference shielding method for UAV navigation, comprising the following steps: S1: During the flight of the UAV, communication sensing data is obtained by scanning within the navigation signal frequency band through a radio receiver, and micro-motion sensing data of the surrounding environment is obtained through an airborne ultra-wideband radar. S2: Perform collaborative analysis on the acquired communication sensing data and micro-motion sensing data to determine whether there are any related malicious interference signals or abnormal physical targets; S3: If a correlation is determined, the direction of the malicious interference signal is determined, and the distance and angle of the abnormal physical target are measured. The correlation analysis of the above direction, distance and angle results is used to identify whether the abnormal physical target is a mobile cooperative interference platform. S4: If identified as a mobile cooperative jamming platform, control the radio receiver to switch to a frequency point not affected by the malicious jamming signal, and use inertial navigation and visual odometry data to regenerate the carrier phase and pseudorange prediction values. S5: Monitor the integrity index of the UAV navigation system in real time. If the integrity index does not recover to above the safety threshold within a preset time, switch to a passive navigation mode based on vision and inertia.

[0007] The beneficial effects of the technical solution provided by this invention include at least the following: This invention achieves real-time identification of mobile cooperative jamming platforms through communication and radar heterogeneous sensing collaboration and spatiotemporal correlation analysis. While performing signal-level analysis, this method combines the target's micro-motion patterns, motion trajectories, and behavioral intentions to achieve a paradigm leap in entity-signal cooperative anti-jamming, significantly improving the accuracy of jamming identification and system response speed in complex environments.

[0008] This invention proposes a dual consistency verification and motion intent recognition algorithm. The system can accurately locate suspicious moving platforms that coordinate with interference signals in space, power and behavior in strong clutter and multi-target scenarios. It solves the problems of high false alarm rate and slow response in traditional methods, and provides high-confidence target intelligence support for subsequent intelligent anti-interference decision-making.

[0009] This invention designs an intelligent frequency hopping and navigation smooth transition mechanism based on observation value regeneration. During frequency switching, it uses inertial / visual data to predict and regenerate the carrier phase and pseudorange information of the frequency point after the hop in real time, and performs adaptive fusion with the real observation values. This technology breaks through the bottleneck of slow reacquisition during frequency hopping and improves the mission reliability of UAVs in dynamic interference environments.

[0010] This invention achieves a smooth mode migration from fusion navigation to passive navigation by constructing a multi-dimensional integrity monitoring and weight gradual switching strategy, avoiding filter divergence and state jumps caused by hard switching. Furthermore, it enhances the self-diagnosis and fault tolerance capabilities of the navigation system through multi-source consistency monitoring, enabling UAVs to maintain usable navigation accuracy and system stability even under extreme interference, demonstrating significant practical adaptability and technological advancement. Attached Figure Description

[0011] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1This is a flowchart of a method provided in an embodiment of the present invention. Detailed Implementation

[0013] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a signal interference shielding method for UAV navigation proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0014] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0015] The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0016] The following description, in conjunction with the accompanying drawings, details a specific scheme for a signal interference shielding method for UAV navigation provided by the present invention.

[0017] Please see Figure 1 The diagram illustrates a method flowchart for signal interference shielding for unmanned aerial vehicle (UAV) navigation according to an embodiment of the present invention. The method includes the following steps: S1: During the flight of the UAV, communication sensing data is obtained by scanning within the navigation signal frequency band through a radio receiver, and micro-motion sensing data of the surrounding environment is obtained through an airborne ultra-wideband radar. S2: Perform collaborative analysis on the acquired communication sensing data and micro-motion sensing data to determine whether there are any related malicious interference signals or abnormal physical targets; S3: If a correlation is determined, the direction of the malicious interference signal is determined, and the distance and angle of the abnormal physical target are measured. The correlation analysis of the above direction, distance and angle results is used to identify whether the abnormal physical target is a mobile cooperative interference platform. S4: If identified as a mobile cooperative jamming platform, control the radio receiver to switch to a frequency point not affected by malicious jamming signals, and use inertial navigation and visual odometry data to regenerate the carrier phase and pseudorange prediction values. S5: Monitor the integrity index of the UAV navigation system in real time. If the integrity index does not recover to above the safety threshold within a preset time, switch to a passive navigation mode based on vision and inertia.

[0018] In one embodiment of the present invention, the step of acquiring communication sensing data by scanning within the navigation signal frequency band using a radio receiver includes: Based on the pre-configured list of navigation signal frequency bands and scanning strategy, the radio receiver is controlled to scan and obtain the original radio frequency signal; The original radio frequency signal is down-converted and analog-to-digital converted to obtain digital intermediate frequency data; Real-time signal quality metrics, including signal-to-noise ratio, carrier-to-noise power density ratio, and multipath error estimate, are calculated based on digital intermediate frequency data. Digital intermediate frequency data is encapsulated with real-time signal quality indicators to form communication sensing data.

[0019] It should be noted that the pre-configured navigation signal frequency band list is a structured data table stored in the database of the UAV's onboard processing unit or ground control station. This list explicitly specifies the frequency bands used by the Global Navigation Satellite System (GNSS) and other key navigation / positioning signals that need to be monitored. Commonly used bands include GPS L1 (1575.42 MHz), GPS L2 (1227.60 MHz), GLONASS G1 (1602 MHz), BeiDou B1I (1561.098 MHz), and Galileo E1 (1575.42 MHz).

[0020] In this embodiment, the pre-configured scanning strategy is a scanning method that combines periodic polling and event triggering, specifically including: performing a fast scan of all frequency bands every 100 milliseconds, and automatically increasing the scanning frequency of a frequency band when the signal quality of a certain frequency band continues to decline.

[0021] In this embodiment, the radio receiver is a software-defined radio (SDR) receiver used for signal quality monitoring and interference detection, such as ADALM-PLUTO or USRP. A typical receiver configuration is as follows: receiving bandwidth ≥ 20 MHz, sampling rate ≥ 40 MS / s, capable of switching to different navigation frequency bands in real time and supporting multi-channel parallel reception.

[0022] Downconversion refers to mixing the received raw radio frequency signal (e.g., 1575.42 MHz) to an intermediate frequency (e.g., 70 MHz) using a local oscillator, then removing out-of-band noise using a low-pass filter, and finally converting the analog-to-digital signal using a general-purpose analog-to-digital converter (e.g., AD9361 model), outputting digital intermediate frequency data for subsequent processing.

[0023] In one embodiment of the present invention, the step of acquiring micro-motion sensing data of the surrounding environment through an airborne ultra-wideband radar includes: Control the airborne ultra-wideband radar to transmit detection signals and receive echo signals, perform range compression processing on the echo signals, and generate a range-amplitude spectrum; Based on the range-amplitude spectrum, point cloud data containing target range, azimuth, and radial velocity information is generated through beamforming and Doppler processing. Within the set monitoring range gate, coherent accumulation and micro-Doppler analysis are performed on the range-amplitude spectrum of multiple consecutive pulses to extract the slow time dimension micro-Doppler features and distinguish the regular micro-motion components generated by potential suspicious targets. Regular micro-motion components and their associated point cloud data are encapsulated to form micro-motion sensing data.

[0024] It should be noted that, in this embodiment, the airborne ultra-wideband radar is a pulse compression radar system, and a typical parameter configuration is as follows: Operating frequency band: 3.1-10.6 GHz, instantaneous bandwidth not less than 500 MHz; Signal type: Linear frequency modulated continuous wave (LFMCW), modulation period 1 ms, frequency modulation slope 5 MHz / μs; Antenna system: 4-channel planar array antenna with an element spacing of half a wavelength (center frequency of 6.85 GHz), supporting digital beamforming; Transmitted power: The average radiated power density complies with FCC Part 15.519 and does not exceed -41.3 dBm / MHz; In addition, each receiving channel includes a low-noise amplifier, mixer, intermediate frequency filter and 16-bit analog-to-digital converter with a sampling rate of 100 MS / s. The controller integrates an FPGA to realize real-time signal processing, and a DSP is used for motion parameter extraction and target tracking.

[0025] The distance-amplitude spectrum is generated through the following steps: (1) Mix the received echo signal with the local oscillator linear frequency modulated signal to obtain the difference frequency signal; (2) Perform a 256-point Fast Fourier Transform (FFT) on the difference frequency signal within each pulse repetition period to map the frequency domain to the distance domain; (3) Take the magnitude of the FFT result to form a distance-amplitude spectrum, with the horizontal axis representing the distance cell (each cell corresponds to 0.585 m) and the vertical axis representing the echo amplitude value (unit dB). (4) The background clutter is estimated by recursive averaging and the background clutter is subtracted from the current range-amplitude spectrum to enhance the echo of the moving target.

[0026] Beamforming and Doppler processing are performed in the radar signal processor according to the following procedure: (1) Adaptive weighting of the signals from the four receiving channels is performed to form eight receiving beams with a coverage azimuth of ±60° and a fixed direction; (2) Coherently accumulate 64 consecutive pulse echoes in each receiving beam and each range cell, and perform Doppler FFT on the accumulated pulse sequence to form a range-Doppler matrix; (3) Extract moving targets in Doppler using a constant false alarm rate (CFAR) detector and output point cloud data containing target distance, azimuth angle and radial velocity.

[0027] To reduce computational load and focus on potentially suspicious targets, this embodiment includes a configurable surveillance range gate. A typical surveillance range gate configuration is as follows: Door range: The default setting is 15-80 meters, with each 5-meter interval divided into a sub-door, for a total of 13 sub-doors; Threshold control: Only echo data inside the gate is used for subsequent micro-motion analysis, while data outside the gate is used only for background monitoring; Adaptive adjustment: If a continuously approaching target is detected outside a certain distance gate, the system can temporarily and automatically expand the distance gate range.

[0028] Coherent accumulation and micro-Doppler analysis are performed in the slow time dimension. In this embodiment, each frame of the coherent accumulation contains 256 pulse repetition cycles (corresponding to a time length of 25.6). (ms), with 50% inter-frame overlap, a short-time Fourier transform (STFT) is performed on the slow-time signal within the same range-azimuth cell to generate a time-frequency map. The horizontal axis represents slow time (unit: seconds), and the vertical axis represents Doppler frequency (unit: Hz). The micro-Doppler frequency curve is separated from the time-frequency map using a ridge extraction algorithm. Principal component analysis (PCA) is used to reduce the dimensionality of the multi-period micro-Doppler curves, and stable feature components are extracted as slow-time dimension micro-Doppler features, which reflect the periodic micro-motions of target components (such as rotors, wheels, or limbs), including: fundamental frequency, number of harmonic components, modulation depth, and symmetry index. The periodic consistency of the micro-Doppler curves is calculated using an autocorrelation function. Micro-Doppler features with a periodic consistency higher than 0.7 are identified as corresponding to regular micro-motions. Targets with regular micro-motions are marked as potential suspicious targets, and the associated micro-motion components are encapsulated together with the point cloud data into a micro-motion sensing data package for use by the subsequent collaborative analysis module.

[0029] In one embodiment of the present invention, the step of collaboratively analyzing the acquired communication sensing data and micro-motion sensing data to determine whether there are related malicious interference signals and abnormal physical targets includes: Perform spectrum analysis and signal quality monitoring on the communication sensing data to identify malicious interference signals and extract their direction of arrival α_s and the time t_s when their power first exceeds the interference decision threshold; Kinematic filtering and trajectory analysis are performed on the micro-motion sensing data to screen out abnormal physical targets and extract their azimuth angle α_t and the time t_t when they first exceed the tracking decision threshold in the radar tracking filter. Calculate the time difference Δt between t_s and t_t, and calculate the angle difference Δθ between α_s and α_t; If Δt is less than the preset time threshold T and Δθ is less than the preset angle threshold Θ, then the malicious interference signal is determined to be related to the abnormal physical target.

[0030] It should be noted that, in this embodiment, the process of performing spectral analysis and signal quality monitoring on the communication sensing data to identify malicious interference signals includes: Welch method is used to estimate the power spectral density (PSD) of communication sensing data and detect the presence of unexpected spectral peaks or broadband uplifts in the navigation signal frequency band (e.g., the frequency band of GPS L1 is 1575.42 MHz ± 2 MHz). If the peak power of the spectrum is detected to exceed the nominal satellite signal power by more than 10 dB, or if the broadband noise power is detected to rise by more than -110 dBm within a 1MHz bandwidth, it will be initially marked as suspected interference. The following signal quality metrics are calculated in real time: (1) Carrier phase noise: exceeding 1° RMS is considered abnormal; (2) Pseudorange jitter: A value exceeding 0.3 meters is considered abnormal; (3) Carrier noise power density ratio (C / N_0): A drop of more than 5 dB-Hz within 0.1 seconds is considered abnormal; If at least two of the above indicators are abnormal at the same time, and the duration of the abnormality exceeds 0.5 seconds, then the suspicious interference is confirmed as a malicious interference signal. The direction of arrival α_s of the malicious interference signal is calculated using the MUSIC algorithm or a direction-of-arrival estimation array. The moment when C / N_0 ≤ 35 dB-Hz and pseudorange jitter ≥ 0.5 m for 100 ms is recorded as the moment when the interference decision threshold is first exceeded.

[0031] In this embodiment, the process of performing kinematic filtering and trajectory analysis on the micro-motion sensing data to filter out abnormal physical targets includes: An interactive multi-model (IMM) filtering algorithm, which includes both uniform velocity and uniform acceleration motion models, is used to track and filter point cloud targets in each micro-motion sensing data, and output their smoothed position, velocity and acceleration estimates. If any point cloud target simultaneously meets the following conditions, it is determined to be an anomalous physical target: (1) Located within the monitoring distance of the door; (2) The speed is between 0.5 and 20 m / s (excluding stationary targets and high-speed aircraft); (3) The trajectory curvature exceeds 0.1 m^2 or the rate of change of velocity continuously exceeds 2 m / s^2; After determining that there is an abnormal physical target, the azimuth angle α_t of the abnormal physical target relative to the UAV is calculated with the UAV's nose direction as 0° (accuracy ≤ 1°). The target is continuously tracked for ≥5 frames, and the velocity vector change rate is ≥1 m / s². 2 The radar frame time is t_t.

[0032] After obtaining α_s, t_s, α_t, and t_t, the system calculates: The time difference Δt = |t_s - t_t|, with a preset time threshold T = 200 milliseconds; The angle difference Δθ = |α_s - α_t|, with a preset angle threshold Θ = 10°; If Δt < T and Δθ < Θ, then the malicious interference signal is determined to have a spatiotemporal correlation with the abnormal physical target, and enters the identification process of the subsequent collaborative interference platform.

[0033] In one embodiment of the present invention, the steps of direction finding of malicious interference signals, ranging and angle measurement of abnormal physical targets, and correlation analysis of the direction finding, ranging and angle measurement results to identify whether the abnormal physical target is a mobile cooperative jamming platform include: High-precision direction finding is performed on malicious interference signals to obtain their arrival direction parameters, and the measured distance and angle parameters of abnormal physical targets are extracted simultaneously from micro-motion sensing data; If the arrival direction parameters of the interference signal are compared with the angle parameters of the abnormal physical target, and the deviation between the two in spatial pointing is less than the preset first tolerance, then the spatial pointing consistency check is considered to have passed. Based on the received power and propagation model of the interference signal, estimate its possible distance range in the direction of arrival, and determine whether the measured distance of the abnormal physical target falls within the possible distance range. If so, it is considered to have passed the distance propagation consistency check.

[0034] It should be noted that in this embodiment, the arrival direction parameter of the malicious interference signal is obtained by the four-element cross array antenna on the UAV in conjunction with the MUSIC direction finding algorithm, and the angle parameter of the abnormal physical target is extracted from the micro-motion sensing data of the airborne ultra-wideband radar in the aforementioned steps, including the horizontal azimuth angle α_t and the pitch angle β_t. The arrival direction parameters of the interference signal, the azimuth and elevation angles of the abnormal physical target are uniformly transformed to the Northeast Sky (ENU) local coordinate system with the UAV as the origin, and the pointing vectors of both in three-dimensional space are calculated: Interference signal pointing vector: D_s = (cosβ_s · cosα_s, cosβ_s · sinα_s, sinβ_s); The vector pointing to the anomalous physical target: D_t = (cosβ_t · cosα_t, cosβ_t · sinα_t, sinβ_t); Calculate the angle deviation between the two vectors: Δφ = arccos[(D_s · D_t) / (|D_s| · |D_t|)]; In this embodiment, a preset first tolerance of 5° is set. That is, if Δφ ≤ 5°, the spatial pointing consistency check is considered to have passed. If it fails, the abnormal physical target is determined to have no direct spatial correlation with the interference signal, and the current identification process is terminated.

[0035] In this embodiment, the process of estimating the possible distance range of the interference signal in its direction of arrival based on the received power and propagation model of the interference signal, and comparing it with the measured distance of the abnormal physical target, includes: The received power P_r (in dBm) of the malicious interference signal is measured by a radio receiver, and a hybrid model combining the free-space propagation model and the ground reflection correction model is established: P_r = P_t + G_t + G_r - L_fs - L_ref; In the formula, P_t is the transmit power of the interference source, G_t and G_r are the transmit antenna gain and receive antenna gain of the UAV (in dBi), respectively, L_fs is the free space path loss, L_ref is the additional loss introduced by ground reflection, and L_fs and L_ref are related to the distance between the UAV and the interference source, the altitude of the UAV and the ground dielectric constant, and can be obtained by interpolation through table lookup. The interference source's transmitted power P_t is considered to be uniformly distributed within the range of 20-30 dBm. Using the inverse propagation model, the distance solution set corresponding to the range of Pt values ​​in the direction-finding direction (α_s, β_s) is calculated to obtain the possible distance interval [d_min, d_max]. Subsequently, the measured distance d_t of the abnormal physical target is extracted from the micro-motion sensing data, and it is determined whether d_t satisfies the following conditions: d_min≤d_t≤d_max If the conditions are met, then the consistency check is considered to have passed the distance propagation test. If the measured distance is much smaller than d_min (e.g., d_t < 0.8 × d_min), it is determined to be another reflector or a non-interference source; If the measured distance is much greater than d_max (e.g., d_t > 1.2 × d_max), it is determined to be an indirect interference source.

[0036] If the same anomalous physical target (and the interference signal identified as associated in the previous steps) passes both the spatial pointing consistency check and the distance propagation consistency check, then the anomalous physical target and the interference signal are determined to be consistent in spatial and power propagation, and proceed to the next step of motion characteristic and intent analysis. If either check fails, the target is marked as a "non-cooperative interference object" and other targets continue to be monitored.

[0037] As one embodiment of the present invention, the following steps are performed on abnormal physical targets identified by spatial pointing consistency verification and distance propagation consistency verification: The average velocity V_avg and maximum acceleration A_max of the abnormal physical target are continuously calculated. If V_avg is within the preset speed range of the maneuvering platform [V_min, V_max] and A_max is greater than the preset acceleration threshold A_th, then it is determined that it has the typical motion characteristics of the maneuvering platform. The velocity vector direction of the anomalous physical target, its approach velocity V_closure relative to the UAV, and the rate of change of its relative azimuth angle Δβ are continuously calculated, where: If V_closure remains positive and is greater than the preset approach speed threshold V_th, while |Δβ| is less than the preset azimuth change threshold β_th, then its movement intention is determined to be interception. If the angle between the velocity vector direction of the abnormal physical target and the velocity vector direction of the UAV itself is consistently less than a preset parallel angle threshold, then its movement intention is determined to be tracking. If the target is determined to have typical motion characteristics of a mobile platform and has the intention to track or intercept a drone, then the abnormal physical target is considered to be a mobile cooperative jamming platform.

[0038] In one embodiment of the present invention, the step of controlling the radio receiver to switch to a spare frequency point unaffected by malicious interference signals includes: Among all available navigation frequencies, candidate frequencies that meet preset quality standards are selected based on current signal quality and historical interference records. The technical feasibility and reaction time of mobile cooperative jamming platforms in implementing tracking jamming on candidate frequencies were evaluated, and the anti-tracking jamming capability of candidate frequencies was inferred. Considering the signal quality, anti-tracking interference capability, and navigation continuity loss caused by frequency switching of the candidate frequencies, the frequency with the best overall evaluation is selected as the optimal backup frequency, and the radio receiver is controlled to switch to that frequency.

[0039] It should be noted that the current signal quality includes the signal-to-noise ratio, carrier noise power density ratio, pseudorange measurement noise, and multipath error estimation acquired in real time for each frequency point, and the acquisition method has been described in the aforementioned steps; the historical interference record is the number of times each frequency point was interfered with in the recent period (e.g., the last 10 minutes), the average duration of interference, the time interval after the last interference ended, and the characteristics of the interference signal (e.g., bandwidth, modulation type, etc.) recorded by the system.

[0040] In this embodiment, a frequency point must simultaneously meet the following conditions (preset quality standards) to be considered as a candidate frequency point: (1) Current signal-to-noise ratio ≥ 30 dB; (2) The current carrier noise power density ratio is ≥ 40 dB-Hz; (3) Pseudorange measurement noise ≤ 0.5 meters; (4) Not attacked by the same interference source (abnormal physical target) within 5 minutes; (5) The frequency of interference within 10 minutes is ≤ 3 times / minute.

[0041] In this embodiment, the specific process for determining the anti-tracking interference capability of candidate frequency points is as follows: Referring to the typical parameter range of known jammers in existing technical data, it is assumed that the jamming platform has a known maximum tuning speed and maximum jamming bandwidth (e.g., the tuning speed of a software-defined radio jamming platform is usually no higher than 200 MHz / millisecond, and the bandwidth of a tracking jamming platform is usually no higher than 20 MHz). Based on the frequency difference Δf between the candidate frequency and the currently interfered frequency, calculate the minimum tuning time T_tune required for the interference platform to perform tracking: T_tune = |Δf| / maximum tuning speed; The reaction time T_reaction is defined as the total time including the switching of the detection frequency by the jamming platform, the relocking of the signal, and the re-implementation of the jamming. Assuming a typical value of 200-500 milliseconds, the quantized value R_antitrack of the anti-tracking jamming capability is calculated using the following formula: R_antitrack = (T_tune + T_reaction) / frequency switching time; The larger the value of R_antitrack, the more difficult it is for the frequency to be tracked and interfered with quickly.

[0042] Establish a comprehensive evaluation function, and calculate the frequency point comprehensive evaluation score S_i using the following formula: S_i = w_1 · Q_i + w2 · A_i - w_3 · L_i; In the formula, Qi is the signal quality score (usually obtained by normalizing the signal-to-noise ratio, carrier noise power density ratio, pseudorange measurement noise, and multipath error estimation), A is the anti-tracking interference capability score, and Li is the handover loss score (usually obtained by normalizing the frequency difference, reacquisition time, and multi-system compatibility score). w_1, w_2, and w_3 are the weighting coefficients of Qi, A, and Li, respectively. In this embodiment, w1=0.5, w1=0.3, and w1=0.2. Select the frequency point with the highest S_i as the optimal backup frequency point, control the radio receiver to switch to this frequency point and start the subsequent carrier phase and pseudorange prediction value regeneration process.

[0043] In one embodiment of the present invention, the step of regenerating carrier phase and pseudorange prediction values ​​using inertial navigation and visual odometry data includes: During and after frequency hopping, the predicted position and speed of the drone are continuously calculated using inertial navigation and visual odometry data. Based on the predicted position and velocity, combined with the satellite ephemeris, the predicted carrier phase and pseudorange values ​​corresponding to the optimal backup frequency are calculated and regenerated in real time. The regenerated carrier phase prediction value and pseudorange prediction value are fused with the navigation signal measurement value actually received and calculated by the radio receiver after the jump, and the fusion result is injected into the UAV's navigation filter.

[0044] It should be noted that inertial navigation systems and visual odometry are common accessories for UAVs. The former is used to provide high-frequency attitude and velocity information, while the latter is used to provide relative displacement observation. The two are fused through an error state Kalman filter to output the predicted position and predicted velocity of the UAV. The specific fusion method can adopt a scheme known in the field.

[0045] After switching to the optimal backup frequency, the UAV system calculates and regenerates the corresponding regenerated navigation observations based on the predicted position and velocity, combined with the satellite ephemeris corresponding to that frequency (including satellite orbital parameters, satellite clock errors, ionospheric and tropospheric delay models, etc., which are usually updated every 2 hours). The pseudorange prediction value ρ is calculated using the following formula: ρ~ = |P_sat - P_pred| + c · (δ_tsat - δ_tuav) + I + T + ε; In the formula, P_sat is the satellite position calculated based on satellite ephemeris, c is the speed of light, δ_tsat is the satellite clock error (given by satellite ephemeris), δ_tuav is the UAV clock error (the rate of change estimated by inertial / visual state), I and T are the ionospheric and tropospheric delays (given by satellite ephemeris), respectively, and ε is the multipath and noise residual (a preset random small quantity). The carrier phase prediction value φ is calculated using the following formula: φ~ = |P_sat - P_pre| / (λ + N + δ_φclock + δ_φatm + ε_φ); In the formula, λ is the inherent carrier wavelength of the frequency point, N is the integer ambiguity (obtained by geometric initialization before and after the jump), δ_φclock and δ_φatm are the clock delay coefficient and atmospheric phase delay coefficient, respectively; The pseudorange prediction value ρ and carrier phase prediction value φ for each visible satellite are weighted and fused with the actual measurement value calculated by the radio receiver at the spare frequency. The weights can be dynamically adjusted according to the prediction accuracy, signal quality and receiver lock state. The fused observation sequence is output and finally the fused result is injected into the navigation filter of the UAV.

[0046] As one embodiment of the present invention, the step of real-time monitoring of the solution integrity index of the UAV navigation system, and switching to a passive navigation mode based primarily on vision and inertia if the solution integrity index fails to recover to above a safety threshold within a preset time includes: In a parallel monitoring UAV navigation system, the integrity index of the solution is obtained by considering the protection level of satellite navigation, the pose consistency deviation between inertial navigation and visual odometry data, and the jump error based on satellite navigation solution before and after frequency hopping. Based on the integrity index, a quantified navigation confidence value is generated through a state fusion algorithm. If the navigation confidence value is continuously lower than the preset security confidence threshold within a preset time window, the navigation mode switching is triggered.

[0047] It should be noted that in UAV navigation systems, the receiver calculates the horizontal and vertical protection levels based on factors such as the geometry of currently visible satellites, the signal quality of each satellite, and user ranging errors, using conventional integrity algorithms. When the protection level exceeds the preset alarm limit, it indicates that the current satellite navigation solution is unreliable, and the system needs to take corresponding fault-tolerant measures. The protection level of satellite navigation is a commonly used integrity indicator in this field for evaluating the reliability of positioning results.

[0048] The pose consistency deviation between inertial navigation and visual odometry data can effectively reflect whether any sensor is abnormal or degraded. If the deviation continues to exceed a reasonable threshold, it indicates that there may be problems such as loss of visual features or drift of inertial devices between sensors. In this field, this consistency deviation monitoring is usually used as a basic means to judge the health status of the fusion navigation system.

[0049] After a UAV performs frequency hopping due to interference, by comparing the position or velocity information calculated by satellite navigation before and after the frequency hopping, and subtracting the reasonable displacement calculated from inertial / visual data, the abnormal hopping error caused by the frequency switching can be obtained. If this error significantly exceeds the range expected by sensor noise and dynamic model, it indicates that frequency hopping may cause abnormal satellite signal re-acquisition or inaccurate navigation calculation. This monitoring is the standard method for judging the navigation recovery status in frequency hopping anti-interference systems and is a commonly used technical means in this field.

[0050] In one embodiment of the present invention, after triggering the navigation mode switch, the following steps are further included: The UAV's navigation filter initializes a local navigation coordinate system with the current fused navigation solution as the origin; In the drone's navigation filter, the weight of satellite navigation data is gradually reduced and eventually set to zero, while the fusion weight of inertial navigation and visual odometry data is gradually increased, forming a passive navigation mode that is mainly based on vision and inertia.

[0051] It should be noted that when a navigation mode switch is triggered, the UAV system establishes a new local coordinate system starting from the current moment. This coordinate system is usually oriented with Northeast-East (ENU), with its X-axis pointing to geographic east, Y-axis pointing to geographic north, and Z-axis pointing vertically upward to the zenith. The latitude, longitude, and altitude information of the coordinate system origin are provided by the fused navigation solution before the switch, serving as the initial reference benchmark for subsequent pure inertial / visual navigation. This method is a routine initialization operation when switching modes in a multi-source navigation system.

[0052] During the transition period after the handover is initiated, the weight of satellite navigation observations in the filter update is gradually reduced in a linear or exponential manner. For example, the weight is reduced by 10% of the initial value every 0.1 seconds within 3 seconds after the handover is initiated, until it is finally set to zero, in order to avoid filter divergence caused by the sudden removal of satellite data. During the same period of reducing satellite weight, the fusion weight of inertial navigation and visual odometry data is gradually increased with corresponding step sizes, with inertial data as the main factor and visual data as the error correction. The weight of the two is eventually increased to the point that they together constitute 100% of the observation update source.

[0053] When the satellite weight is reduced to zero, the system enters a passive navigation mode that is primarily based on vision and inertia. In this mode, the UAV's navigation filter performs state prediction and updates solely based on the inertial measurement unit and visual odometry.

[0054] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A signal interference shielding method for UAV navigation, characterized in that, The method includes: S1: During the flight of the UAV, communication sensing data is obtained by scanning within the navigation signal frequency band through a radio receiver, and micro-motion sensing data of the surrounding environment is obtained through an airborne ultra-wideband radar. S2: Perform collaborative analysis on the acquired communication sensing data and micro-motion sensing data to determine whether there are any related malicious interference signals or abnormal physical targets; S3: If a correlation is determined, the direction of the malicious interference signal is determined, and the distance and angle of the abnormal physical target are measured. The correlation analysis of the above direction, distance and angle results is used to identify whether the abnormal physical target is a mobile cooperative interference platform. S4: If identified as a mobile cooperative jamming platform, control the radio receiver to switch to a frequency point not affected by the malicious jamming signal, and use inertial navigation and visual odometry data to regenerate the carrier phase and pseudorange prediction values. S5: Monitor the integrity index of the UAV navigation system in real time. If the integrity index does not recover to above the safety threshold within a preset time, switch to a passive navigation mode based on vision and inertia.

2. The signal interference shielding method for UAV navigation according to claim 1, characterized in that: The step of acquiring communication sensing data by scanning within the navigation signal frequency band using a radio receiver includes: According to the pre-configured navigation signal frequency band list and scanning strategy, the radio receiver is controlled to scan and obtain the original radio frequency signal; The original radio frequency signal is down-converted and analog-to-digital converted to obtain digital intermediate frequency data; Real-time signal quality indicators are calculated based on the digital intermediate frequency data, including signal-to-noise ratio, carrier noise power density ratio, and multipath error estimate. The digital intermediate frequency data and the real-time signal quality indicators are encapsulated to form the communication sensing data.

3. The signal interference shielding method for UAV navigation according to claim 1, characterized in that: The steps of acquiring micro-motion sensing data of the surrounding environment through airborne ultra-wideband radar include: The airborne ultra-wideband radar is controlled to transmit detection signals and receive echo signals. Range compression processing is performed on the echo signals to generate a range-amplitude spectrum. Based on the range-amplitude spectrum, point cloud data containing target range, azimuth angle and radial velocity information is generated through beamforming and Doppler processing. Within the set monitoring range gate, coherent accumulation and micro-Doppler analysis are performed on the range-amplitude spectrum of multiple consecutive pulses to extract the slow time dimension micro-Doppler features and distinguish the regular micro-motion components generated by potential suspicious targets. The regular micro-motion components and their associated point cloud data are encapsulated to form the micro-motion sensing data.

4. The signal interference shielding method for UAV navigation according to claim 1, characterized in that: The step of collaboratively analyzing the acquired communication sensing data and micro-motion sensing data to determine whether there are related malicious interference signals and abnormal physical targets includes: Perform spectrum analysis and signal quality monitoring on the communication sensing data to identify malicious interference signals and extract their direction of arrival α_s and the time t_s when their power first exceeds the interference decision threshold; Kinematic filtering and trajectory analysis are performed on the micro-motion sensing data to screen out abnormal physical targets and extract their azimuth angle α_t and the time t_t when they first exceed the tracking decision threshold in the radar tracking filter. Calculate the time difference Δt between t_s and t_t, and calculate the angle difference Δθ between α_s and α_t; If Δt is less than a preset time threshold T and Δθ is less than a preset angle threshold Θ, then it is determined that the malicious interference signal is associated with the abnormal physical target.

5. The signal interference shielding method for UAV navigation according to claim 1, characterized in that: The steps of direction finding of malicious interference signals, ranging and angle measuring of abnormal physical targets, correlation analysis of the direction finding, ranging and angle measuring results, and identification of whether the abnormal physical target is a mobile cooperative jamming platform include: The malicious interference signal is subjected to high-precision direction finding to obtain its arrival direction parameters, and the measured distance and angle parameters of the abnormal physical target are extracted from the micro-motion sensing data simultaneously. If the arrival direction parameter of the interference signal is compared with the angle parameter of the abnormal physical target, and the deviation between the two in spatial pointing is less than the preset first tolerance, then the spatial pointing consistency check is considered to have passed. Based on the received power and propagation model of the interference signal, estimate its possible distance range in the direction of arrival, and determine whether the measured distance of the abnormal physical target falls within the possible distance range. If so, it is considered to have passed the distance propagation consistency check.

6. The signal interference shielding method for UAV navigation according to claim 5, characterized in that: For anomalous physical targets identified through the spatial pointing consistency check and distance propagation consistency check, the following steps are performed: The average velocity V_avg and maximum acceleration A_max of the abnormal physical target are continuously calculated. If V_avg is within the preset speed range of the maneuvering platform [V_min, V_max] and A_max is greater than the preset acceleration threshold A_th, then it is determined that it has the typical motion characteristics of the maneuvering platform. The velocity vector direction of the anomalous physical target, its approach velocity V_closure relative to the UAV, and the rate of change of its relative azimuth angle Δβ are continuously calculated, where: If V_closure remains positive and is greater than the preset approach speed threshold V_th, while |Δβ| is less than the preset azimuth change threshold β_th, then its movement intention is determined to be interception. If the angle between the velocity vector direction of the abnormal physical target and the velocity vector direction of the UAV itself is consistently less than a preset parallel angle threshold, then its movement intention is determined to be tracking. If the target is determined to have typical motion characteristics of a mobile platform and has the intention to track or intercept a drone, then the abnormal physical target is considered to be a mobile cooperative jamming platform.

7. The signal interference shielding method for UAV navigation according to claim 1, characterized in that: The step of controlling the radio receiver to switch to a backup frequency unaffected by the malicious interference signal includes: Among all available navigation frequencies, candidate frequencies that meet preset quality standards are selected based on current signal quality and historical interference records. The technical feasibility and reaction time of the mobile cooperative jamming platform in implementing tracking jamming on the candidate frequency points are evaluated, and the anti-tracking jamming capability of the candidate frequency points is inferred. Taking into account the signal quality, anti-tracking interference capability, and navigation continuity loss caused by frequency switching of the candidate frequency points, the frequency point with the best comprehensive evaluation is selected as the optimal backup frequency point, and the radio receiver is controlled to switch to that frequency point.

8. The signal interference shielding method for UAV navigation according to claim 1 or 7, characterized in that: The steps for regenerating carrier phase and pseudorange prediction values ​​using inertial navigation and visual odometry data include: During and after frequency hopping, the predicted position and speed of the drone are continuously calculated using inertial navigation and visual odometry data. Based on the predicted position and predicted velocity, and combined with the satellite ephemeris, the predicted carrier phase and pseudorange values ​​corresponding to the optimal backup frequency are calculated and regenerated in real time. The regenerated carrier phase prediction value and pseudorange prediction value are fused with the navigation signal measurement value actually received and calculated by the radio receiver after the jump, and the fusion result is injected into the navigation filter of the UAV.

9. The signal interference shielding method for UAV navigation according to claim 1, characterized in that: The step of switching to a passive navigation mode based primarily on vision and inertia if the solution integrity index of the real-time monitoring UAV navigation system fails to recover to above a safety threshold within a preset time includes: In a parallel monitoring UAV navigation system, the integrity index of the solution is obtained by considering the protection level of satellite navigation, the pose consistency deviation between inertial navigation and visual odometry data, and the jump error based on satellite navigation solution before and after frequency hopping. Based on the solution integrity index, a quantified navigation confidence value is generated through a state fusion algorithm. If the navigation confidence value is continuously lower than a preset security confidence threshold within a consecutive preset time window, a navigation mode switch is triggered.

10. The signal interference shielding method for UAV navigation according to claim 9, characterized in that: After triggering the navigation mode switch, the following steps are also included: The UAV's navigation filter initializes a local navigation coordinate system with the current fused navigation solution as the origin; In the drone's navigation filter, the weight of satellite navigation data is gradually reduced and eventually set to zero, while the fusion weight of inertial navigation and visual odometry data is gradually increased, forming a passive navigation mode that is mainly based on vision and inertia.

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