A method and system for identifying deceptive electromagnetic interference based on cooperative perception

Abstract

Some embodiments of the specification provide a method and system for identifying fraudulent electromagnetic interference based on cooperative perception, the method comprising: obtaining a first signal feature and a second signal feature; the first signal feature is a radio feature associated with the three-dimensional motion of the unmanned aerial vehicle during flight and the antenna receiving angle, and the second signal feature is a radio feature collected by the ground base station; the collection time and signal source corresponding to the second signal feature are the same as the first signal feature; according to the re-projection Doppler deviation of different feature points in the first signal feature and the second signal feature, and the signal-to-noise ratio of the antenna end of the unmanned aerial vehicle and the ground base station, different feature points are respectively configured with confidence weight; based on the confidence weight, a space-time consistency verification matrix is constructed; according to the analysis result of the space-time consistency verification matrix, the confidence of different feature points existing fraudulent interference signals is determined; in response to the confidence satisfying the preset condition, the unmanned aerial vehicle is controlled to perform temporary switching of the navigation source.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a method for identifying deceptive electromagnetic interference based on cooperative perception. Background Technology

[0002] With the rapid development of the low-altitude economy, drones are increasingly being used in power line inspection, logistics delivery, and urban security. These tasks heavily rely on the precise positioning and timing services provided by the Global Navigation Satellite System (GNSS) to ensure that drones fly autonomously along predetermined routes. However, GNSS signals have publicly known frequencies and low power characteristics, making them highly susceptible to electromagnetic interference. Compared to suppression jamming, deceptive electromagnetic interference, by emitting fake signals that are highly similar to real satellite signals, can covertly induce drones to deviate from their flight paths or even illegally seize control, posing a serious threat to public safety.

[0003] Existing deception and interference identification technologies primarily focus on detecting the presence of interference, making it difficult to effectively distinguish between genuine signals and highly realistic deception signals. However, during low-altitude flight, the violent maneuvers of drones, multipath effects, and channel fading such as Doppler shift can lead to false alarms or missed detections in feature comparison. Furthermore, the clock asynchrony and transmission delay between the end and network can easily be confused with the spoofing delay of the deception source, causing identification failure.

[0004] Therefore, how to accurately identify deceptive interference and achieve rapid protection in complex environments is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] This specification provides one or more embodiments of a deceptive electromagnetic interference identification method and system based on cooperative sensing. The method includes: acquiring a first signal feature and a second signal feature; the first signal feature is a radio feature associated with the three-dimensional motion and antenna reception angle of a UAV during flight, and the second signal feature is a radio feature collected by a ground base station, wherein the acquisition time and signal source corresponding to the second signal feature are the same as those of the first signal feature; assigning confidence weights to different feature points based on the reprojection Doppler bias of different feature points in the first and second signal features, and the signal-to-noise ratio at the antenna ends of the UAV and the ground base station; constructing a spatiotemporal consistency verification matrix based on the confidence weights; determining the confidence level of the presence of deceptive interference signals at different feature points based on the analysis results of the spatiotemporal consistency verification matrix; and controlling the UAV to perform a temporary switching of navigation sources in response to the confidence level meeting a preset condition.

[0006] This specification provides one or more embodiments of a deceptive electromagnetic interference identification system based on cooperative perception, characterized by comprising: a signal processing module configured to acquire a first signal feature and a second signal feature; the first signal feature is a radio feature associated with the three-dimensional motion and antenna reception angle of a UAV during flight, and the second signal feature is a radio feature collected by a ground base station, wherein the acquisition time and signal source corresponding to the second signal feature are the same as those of the first signal feature; a weight allocation module configured to assign confidence weights to different feature points based on the reprojection Doppler deviation of different feature points in the first and second signal features, and the signal-to-noise ratio at the antenna ends of the UAV and the ground base station; a construction module configured to construct a spatiotemporal consistency verification matrix based on the confidence weights; a determination module configured to determine the confidence level of the existence of deceptive interference signals at different feature points based on the analysis results of the spatiotemporal consistency verification matrix; and a control module configured to control the UAV to perform a temporary switching of navigation sources in response to the confidence level meeting a preset condition.

[0007] The beneficial effects of the above-mentioned invention include, but are not limited to: 1) Assigning confidence weights to physical layer feature segments based on reprojection Doppler bias and signal-to-noise ratio, and constructing a spatiotemporal consistency verification matrix, which can effectively suppress the interference of multipath effects and maneuvering noise on feature comparison, reduce false alarm rate and false negative rate, and improve the accuracy and robustness of deception identification; 2) Controlling the UAV to perform temporary switching of navigation source (such as switching from GNSS to inertial navigation and visual odometry fusion mode) according to the confidence of deception signal alarm, and removing contaminated demodulation symbols or pseudorange messages, can block the penetration of deceptive interference signals from the underlying hardware, ensuring that the UAV maintains absolute consistency with the real physical spacetime in a strong interference environment, and improving flight safety; 3) Dynamically adjusting the filtering strategy according to the UAV's flight speed (removing Doppler outliers at high speeds and performing temporal cumulative smoothing at low speeds), which can ensure detection accuracy while taking into account real-time performance, and avoid complex collaborative preprocessing leading to transmission delay or packet loss in the flight control system. Attached Figure Description

[0008] Figure 1 This is an application scenario diagram of a deceptive electromagnetic interference identification system based on collaborative sensing, as shown in some embodiments of this specification.

[0009] Figure 2 This is an exemplary module diagram of a deceptive electromagnetic interference identification system based on cooperative sensing, as shown in some embodiments of this specification.

[0010] Figure 3 This is an exemplary flowchart of a deceptive electromagnetic interference identification method based on cooperative sensing, as shown in some embodiments of this specification.

[0011] Figure 4This is an exemplary schematic diagram illustrating a pre-reconstructed preset flight path according to some embodiments of this specification. Detailed Implementation

[0012] The accompanying drawings used in the description of the embodiments will be briefly introduced below. The drawings do not represent all embodiments.

[0013] The terms “system,” “device,” “unit,” and / or “module” as used herein are one method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0014] As indicated in this specification, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0015] In some embodiments, the deceptive electromagnetic interference identification method and system based on cooperative sensing provided in this specification can meet the anti-deceptive interference requirements in various low-altitude flight application scenarios such as power line inspection, logistics distribution, urban security, and infrastructure monitoring, and has a wide range of applications. By deeply integrating UAV onboard sensing with ground base station collaborative verification, the confidence level of deceptive signals can be calculated based on the radio characteristics synchronously collected by the UAV and the ground base station, thereby achieving accurate identification and rapid physical switching of highly convincing deceptive interference signals. Under complex low-altitude electromagnetic environments, dynamic channel fading, and clock asynchrony conditions, it can efficiently and robustly identify deceptive interference and ensure UAV flight safety, effectively improving the survivability and mission reliability of UAVs in highly contested environments.

[0016] Figure 1 This is an application scenario diagram of a deceptive electromagnetic interference identification system based on cooperative sensing, as shown in some embodiments of this specification.

[0017] In some embodiments, such as Figure 1 As shown, the application scenario 100 of the deceptive electromagnetic interference identification system based on collaborative perception includes drones 110, ground base stations 120, processors 130, networks 140, etc.

[0018] Unmanned Aerial Vehicle (UAV) 110 refers to an aerial mobile device that performs flight missions. UAV 110 may be equipped with a Global Navigation Satellite System (GNSS) receiver, an Inertial Navigation System (IMU), a Visual Odometry (VO) camera, a wireless communication module, and an onboard edge computing unit (e.g., a processor, PLC, etc.). UAV 110 is used to receive and transmit radio signals in real time and to analyze the radio signals through the onboard edge computing unit to determine first signal characteristics. UAV 110 can send the analysis results to processor 130 and receive data transmitted by processor 130.

[0019] Ground base station 120 refers to a fixed or mobile monitoring station deployed at a known coordinate location. Ground base station 120 is equipped with a multi-array antenna and a radio processing platform. Ground base station 120 is used to synchronously acquire radio signals from the same signal source as UAV 110 and analyze and process them to determine second signal characteristics. Ground base station 120 can transmit radio signals and analysis and processing results to processor 130, and receive data transmitted by processor 130.

[0020] In some embodiments, such as Figure 1 As shown, in application scenario 100 of the deceptive electromagnetic interference identification system based on collaborative sensing, multiple ground base stations 120 are deployed. These multiple ground base stations 120 are distributed at different ground nodes to form a collaborative monitoring network.

[0021] Processor 130 is used to process data and / or signals obtained from other devices (e.g., drone 110 or ground base station 120) or system components. Processor 130 can execute program instructions based on this data, signals, and / or processing results to perform one or more functions described in some embodiments of this specification. For example, processor 130 can process radio signals transmitted by drone 110 and ground base station 120 to determine first signal characteristics and second signal characteristics. For example, processor 130 can assign confidence weights to different feature points based on reprojection Doppler bias and signal-to-noise ratio in the first and second signal characteristics, and construct a spatiotemporal consistency check matrix based on the confidence weights; based on the analysis results of the spatiotemporal consistency check matrix, determine the confidence level of the presence of deceptive interference signals.

[0022] In some embodiments, the processor 130 may issue control commands to control the drone 110 to temporarily switch navigation sources.

[0023] In some embodiments, processor 130 may include one or more sub-processing devices (e.g., a single-core processing device or a multi-core multi-chip processing device). By way of example only, processor 130 may include a central processing unit, an application-specific integrated circuit, a microprocessor, a digital signal processor (DSP), a field-programmable gate array (FPGA), or any combination thereof.

[0024] In some embodiments, the processor 130 may be deployed at a ground base station or partially deployed on the airborne edge computing unit of the drone 110 to form a distributed processing architecture.

[0025] In some embodiments, the processor 130 may be deployed on a cloud platform.

[0026] Network 140 may include any suitable network capable of facilitating information and / or data exchange. In some embodiments, at least one component (e.g., drone 110, ground base station 120, processor 130, etc.) in application scenario 100 of the cooperative sensing-based deceptive electromagnetic interference identification system can exchange information and / or data with at least one other component via network 140.

[0027] In some embodiments, network 140 can be any one or more of wired or wireless networks. For example, network 140 may include wireless data links such as LoRa, WiFi, 4G / 5G, and point-to-point microwave links. In some embodiments, network 140 may be various topologies or combinations of multiple topologies, such as point-to-point, shared, and centralized. In some embodiments, network 140 may include one or more network access points.

[0028] In some embodiments, taking a power line inspection scenario as an example, the UAV 110 autonomously flies along a preset high-voltage transmission line route. The UAV 110 collects carrier phase, Doppler frequency shift, and signal angle of arrival from GNSS messages in real time as first signal features; the ground base station 120 deployed near the transmission tower synchronously collects second signal features from the same signal source. After acquiring the first and second signal features, the processor 130 calculates the reprojection Doppler deviation of each feature point and assigns a confidence weight to each feature point. The processor 130 constructs a spatiotemporal consistency verification matrix based on the confidence weight, and obtains the confidence level of the presence of deceptive interference signals after solving for the deviation. If the confidence level is higher than a preset threshold, the processor 130 sends a hardware interrupt command to the UAV 110 through the network 140 to control the UAV 110 to disconnect the current navigation source. In addition, the processor 130 also synchronizes the calculated spatial location of the deceptive source to the three-dimensional geographic information system to generate the interference range in the digital twin map. When the ground dispatch system plans new routes for other UAVs, it automatically eliminates dangerous flight segments that cross the interference range, thereby achieving closed-loop protection of single-aircraft anti-spoofing and swarm collaborative avoidance.

[0029] For further explanation of the above content, please refer to Figures 2 to 4 And its related descriptions.

[0030] It should be noted that the above description of the application scenario 100 of the deceptive electromagnetic interference identification system based on collaborative perception is for ease of description only and should not limit this specification to the scope of the embodiments described.

[0031] Figure 2 This is an exemplary module diagram of a deceptive electromagnetic interference identification system based on cooperative sensing, as shown in some embodiments of this specification.

[0032] In some embodiments, the deceptive electromagnetic interference identification system 200 based on collaborative perception includes a signal processing module 210, a weight allocation module 220, a construction module 230, a determination module 240, and a control module 250.

[0033] The signal processing module 210 is a functional unit used to analyze and process radio signals.

[0034] In some embodiments, the signal processing module 210 is configured to acquire a first signal feature and a second signal feature. The first signal feature is a radio feature associated with the three-dimensional motion and antenna reception angle of the UAV during flight, and the second signal feature is a radio feature collected by a ground base station. The acquisition time and signal source corresponding to the second signal feature are the same as those of the first signal feature.

[0035] In some embodiments, the signal processing module 210 is further configured to: acquire the flight speed of the UAV; in response to the flight speed being greater than a preset speed threshold, determine the reference Doppler frequency shift of multiple feature points in the first signal feature based on the three-dimensional motion of the UAV and the antenna receiving angle; based on the deviation between the reference Doppler frequency shift and the measured Doppler frequency shift, remove feature points whose deviation exceeds a preset dynamic outlier threshold; and in response to the flight speed not being greater than the preset speed threshold, perform sliding filtering processing on the data within a preset time window in the first signal feature and the second signal feature.

[0036] The weight allocation module 220 is a functional unit used to set the weight values ​​of different feature points in the first signal feature and the second signal feature.

[0037] In some embodiments, the weight allocation module 220 is configured to: allocate confidence weights to different feature points based on the reprojection Doppler bias of different feature points in the first signal feature and the second signal feature, as well as the signal-to-noise ratio at the antenna ends of the UAV and the ground base station.

[0038] Module 230 is a functional unit used to build datasets.

[0039] In some embodiments, the construction module 230 is configured to construct a spatiotemporal consistency verification matrix based on confidence weights.

[0040] The determination module 240 is a functional unit used to determine the probability of the existence of a deceptive interference signal.

[0041] In some embodiments, the determining module 240 is configured to: determine the confidence level of the presence of deceptive interference signals at different feature points based on the analysis results of the spatiotemporal consistency check matrix.

[0042] In some embodiments, the determining module 240 is further configured to: determine deception interference parameters based on the analysis results of the spatiotemporal consistency check matrix, the deception interference parameters including false position drift, Doppler bias value and signal angle of arrival bias; when it is determined that the UAV is within the deception interference range based on the deception interference parameters, determine the interference vector set based on the false position drift, remove contaminated demodulation symbols or pseudorange observations from the navigation data whose matching degree with the interference vector set is higher than a preset matching degree threshold, and the control module is further configured to perform time base locking on the UAV's local clock.

[0043] Control module 250 is a functional unit used to control the UAV to perform navigation source interruption and switching.

[0044] In some embodiments, the control module 250 is configured to control the UAV to perform a temporary switching of navigation sources in response to a confidence level meeting a preset condition.

[0045] In some embodiments, the control module 250 is further configured to: in response to a confidence level higher than a preset confidence threshold, control the UAV to disconnect the Global Positioning System (GNSS) data link and switch to a dead reckoning mode based on the fusion of inertial navigation system and visual odometry within a preset time period, so that the deviation of the UAV's subsequent flight trajectory from the preset flight route is kept within a preset deviation range, wherein the length of the preset time period is negatively correlated with the complexity of the UAV's three-dimensional motion, and the complexity of the three-dimensional motion is determined based on the magnitude of the UAV's instantaneous velocity, angular velocity, and maneuvering acceleration.

[0046] In some embodiments, the control module 250 is further configured to: determine the spatial orientation information of the deceptive interference signal source based on the direction-finding data of the multi-antenna array of the ground base station, and synchronize it to the three-dimensional geographic information system; determine the radiation range of the deceptive interference signal source based on the spatial orientation information and false position drift of the deceptive interference signal source; predict the rate of change of distance between the UAV and the radiation range based on the subsequent flight trajectory and the radiation range; and control the UAV to pre-reconstruct the preset flight path in response to the distance change rate being less than a preset change rate threshold.

[0047] For further explanation of the above content, please refer to Figures 3-4 And its related descriptions.

[0048] It should be understood that Figure 2The system and its modules shown can be implemented in various ways.

[0049] It should be noted that the above description of the deceptive electromagnetic interference identification system and its modules based on collaborative sensing is for ease of description only and should not be construed as limiting this specification to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principle of the system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from this principle. In some embodiments, Figure 2 The signal processing module 210, weight allocation module 220, construction module 230, determination module 240, and control module 250 disclosed herein can be different modules within a single system, or a single module can implement the functions of two or more of the aforementioned modules. For example, the modules can share a single storage module, or each module can have its own separate storage module. Such variations are all within the scope of protection of this specification.

[0050] In some embodiments, the processor can acquire a first signal feature and a second signal feature; the first signal feature is a radio feature associated with the three-dimensional motion and antenna reception angle of the UAV during flight, and the second signal feature is a radio feature collected by a ground base station, wherein the acquisition time and signal source corresponding to the second signal feature are the same as those of the first signal feature; based on the reprojection Doppler bias of different feature points in the first and second signal features, and the signal-to-noise ratio at the antenna ends of the UAV and the ground base station, confidence weights are assigned to different feature points respectively; based on the confidence weights, a spatiotemporal consistency verification matrix is ​​constructed; based on the analysis results of the spatiotemporal consistency verification matrix, the confidence level of the existence of deceptive interference signals at different feature points is determined; in response to the confidence level meeting a preset condition, the UAV is controlled to perform a temporary switching of navigation sources.

[0051] Figure 3 This is an exemplary flowchart illustrating a deceptive electromagnetic interference identification method based on cooperative sensing, according to some embodiments of this specification. Figure 3 As shown, process 300 includes steps 310-350 as described below. In some embodiments, process 300 may be executed by a processor.

[0052] Step 310: Obtain the first signal feature and the second signal feature. The first signal feature is a radio feature associated with the three-dimensional motion of the UAV during flight and the antenna reception angle. The second signal feature is a radio feature collected by the ground base station. The acquisition time and signal source corresponding to the second signal feature are the same as those of the first signal feature.

[0053] For more information on drones and ground base stations, please see [link to relevant information]. Figure 1 And its related descriptions.

[0054] In some embodiments, the first signal feature includes, but is not limited to, carrier phase observations, signal angle of arrival vector, and Doppler shift observations. The second signal feature also includes the aforementioned physical parameters.

[0055] In some embodiments, the first signal feature is obtained through collaborative acquisition by a Global Navigation Satellite System (GNSS) receiver, a multi-antenna array, and an Inertial Navigation System (IMU) mounted on the UAV. For example, the carrier phase and Doppler frequency shift can be output by the GNSS receiver mounted on the UAV, the signal arrival angle vector can be calculated by the multi-antenna array through phase interferometry, and the current three-dimensional motion velocity, angular velocity, and attitude information of the UAV can be provided by the IMU.

[0056] The antenna reception angle refers to the direction vector of the signal source relative to the antenna phase center of the UAV and / or ground base station. The signal source can include signal transmission equipment or signal relay equipment such as satellites, communication base stations, and signal towers.

[0057] In some embodiments, the second signal features are acquired through a multi-array antenna and radio platform deployed at a ground base station at a known coordinate location. For example, the ground base station samples the radio signal from the same signal source using the same time reference as the UAV, and extracts features such as carrier phase, angle of arrival vector, and Doppler shift as the second signal features.

[0058] In some embodiments, the same acquisition time and signal source means that the first signal feature and the second signal feature correspond to the same signal source and are acquired at the same timestamp (e.g., at the exact second), so as to ensure that subsequent comparisons are performed under the premise of one-to-one spatiotemporal correspondence.

[0059] In some embodiments, the first signal feature and the second signal feature may be sequence data containing feature information of multiple consecutive time points or sampling points.

[0060] Step 320: Based on the reprojection Doppler bias of different feature points in the first signal feature and the second signal feature, as well as the signal-to-noise ratio of the antenna ends of the UAV and the ground base station, assign confidence weights to different feature points respectively.

[0061] Feature points refer to the feature information corresponding to the same time point or sampling point in the first signal feature and the second signal feature.

[0062] For example, feature points can be characteristic information collected and analyzed by a UAV and a ground base station respectively for a specific signal source within a single sampling period (e.g., the carrier phase value, Doppler shift value, and angle of arrival vector value at the same time point). Different feature points can refer to the characteristic information of the same signal source at different sampling points.

[0063] Reprojection Doppler bias refers to the deviation between the theoretical and measured values ​​of the Doppler frequency shift of the UAV and / or ground base station corresponding to the same feature point.

[0064] For example, theoretical values ​​can be calculated based on the three-dimensional motion speed of the UAV and / or the coordinate position of the ground base station.

[0065] For example, reprojection Doppler bias can be the deviation between the reference Doppler frequency shift and the measured Doppler frequency shift of the UAV and / or ground base station corresponding to the same feature point.

[0066] In some embodiments, the processor is further configured to: determine the reprojection Doppler bias of different feature points in the first signal feature based on the reference Doppler frequency shift of multiple feature points in the first signal feature and the measured Doppler frequency shift of the UAV.

[0067] The reference Doppler shift refers to the Doppler shift of a radio signal under ideal conditions (such as when there is no external interference).

[0068] In some embodiments, the reference Doppler frequency shift of the UAV can be calculated based on the UAV's three-dimensional motion, antenna reception angle, and / or the moving speed of the signal source. In some embodiments, the reference Doppler frequency shift of the ground base station can be calculated based on the known fixed position coordinates of the ground base station, the antenna reception angle of the ground base station, and the moving speed of the signal source.

[0069] For example, the processor can determine the reference Doppler frequency shift of the UAV using the following formula (1), which is shown below:

[0070] (1)

[0071] In formula (1), As the reference Doppler frequency shift for UAVs, For the flight speed of the drone,

[0072] The moving speed of the signal source. This refers to the antenna receiving angle of the drone. The wavelength of the radio signal.

[0073] For example, the processor can determine the reference Doppler frequency shift of the ground base station using the following formula (2), which is shown below:

[0074] (2)

[0075] In formula (2), The reference Doppler frequency shift for ground base stations, The moving speed of the signal source.

[0076] This refers to the antenna reception angle of the ground base station. The wavelength of the radio signal.

[0077] The measured Doppler frequency shift refers to the observed value of the Doppler frequency shift in the first and second signal characteristics obtained through actual monitoring and analysis.

[0078] For example, the processor can control the acquisition of the phase difference and frequency error of radio signals at adjacent moments, drive the numerically controlled oscillator of the UAV or ground base station to adjust the frequency of the local reproducible carrier, so that the frequency difference between the local carrier and the radio signal approaches zero, thereby determining the measured Doppler frequency shift of the radio signal.

[0079] In some embodiments, the processor can determine the reprojection Doppler bias in a variety of feasible ways.

[0080] In some embodiments, the processor can determine the reprojection Doppler bias of the UAV and the reprojection Doppler bias of the ground base station, respectively, and perform a weighted summation of the two reprojection Doppler biases to determine the final reprojection Doppler bias.

[0081] For example, the processor can preset the weighted sum to a value of 0.5.

[0082] In some embodiments, the processor can determine and adjust the weight of the UAV's reprojection Doppler bias based on the complexity of the UAV's three-dimensional motion. For example, the processor can set the weight of the UAV's reprojection Doppler bias to be negatively correlated with the complexity of the UAV's three-dimensional motion.

[0083] In some embodiments of this specification, by fusing the dual-end reprojection Doppler bias of the UAV and the ground base station, the source of deceptive interference signals can be assessed more comprehensively. Weighted summation effectively balances the respective advantages of airborne maneuvering noise and ground static reference, preserving the UAV's sensitivity to dynamic deception while utilizing the reference stability of the base station's true value. This significantly improves the rationality of the confidence weights and the robustness of deception identification, reducing the risk of false alarms and missed alarms.

[0084] In some embodiments of this specification, by adaptively adjusting the weights, the weight of the UAV's own Doppler bias is reduced when the UAV is highly maneuvering, effectively suppressing the interference of maneuvering noise on confidence assessment and avoiding misjudgment; the weights are increased when the UAV is low maneuvering, making full use of dynamic information, thereby improving the accuracy and robustness of deception identification under different motion states.

[0085] For more information on the complexity of the three-dimensional motion of drones, see [link to relevant documentation]. Figure 3 The following is a related explanation.

[0086] In some embodiments, the processor can determine the larger of the reprojection Doppler bias of the UAV and the reprojection Doppler bias of the ground base station as the final reprojection Doppler bias.

[0087] In some embodiments, the processor is further configured to: remove feature points whose reprojection Doppler bias exceeds a preset dynamic outlier threshold; and, in response to the UAV's flight speed not exceeding a preset speed threshold, perform sliding filtering on the data of feature points within a preset time window in the first and second signal features.

[0088] The preset dynamic outlier threshold is a critical threshold used to determine whether the reprojection Doppler bias of feature points is abnormal. The preset dynamic outlier threshold can be a fixed value (e.g., 10Hz) or dynamically determined based on the statistical distribution structure of the reprojection Doppler bias of all feature points. For example, the preset dynamic outlier threshold can be a preset multiple of the standard deviation of the reprojection Doppler bias of all feature points. For example, the preset multiple can be 1.5, 2, 2.3, etc.

[0089] In some embodiments, the processor can compare the reprojection Doppler bias of different feature points with a preset dynamic outlier threshold. If the reprojection Doppler bias is greater than the threshold, the feature point is marked as an outlier and removed. Removal includes deleting a feature point from the first signal features.

[0090] In some embodiments, when a feature point in the first signal feature is removed, the processor may simultaneously remove the feature point in the second signal feature corresponding to that feature point to ensure the spatiotemporal alignment consistency of the data.

[0091] A preset time window refers to a pre-defined sliding time period related to data smoothing. In some embodiments, the length of the preset time window can be adaptively adjusted according to the drone's flight speed. For example, the length of the preset time window is positively correlated with the drone's flight speed. For instance, when the flight speed is between 0 and 0.2 times the preset speed threshold, the preset time window is set to 1 second; when the flight speed is between 0.8 times and the preset speed threshold, the preset time window is set to 0.2 seconds, to balance the smoothing effect and real-time performance.

[0092] The preset speed threshold is a critical speed value used to distinguish the degree of dynamic changes in the drone's flight. For example, the preset speed threshold can range from 8 m / s to 15 m / s. When the drone's flight speed is not greater than the preset speed threshold, the processor performs sliding filtering on the data corresponding to the feature points.

[0093] Flight speed refers to the speed at which a drone flies at a specific moment or over a period of time. For example, flight speed can be the instantaneous speed of a drone at a particular moment. Alternatively, it can be the average flight speed of a drone over a preset time window.

[0094] For further explanation of instantaneous velocity, please refer to the relevant description below.

[0095] Sliding filtering is a process of smoothing and reducing noise in the relevant data (such as carrier phase, angle of arrival, Doppler shift, etc.) of feature points within a preset time window. The processor can take the mean, median, or weighted average of the relevant data of feature points within the preset time window, and gradually shift the preset time window forward to obtain the smoothed relevant data of feature points.

[0096] In some embodiments, the processor can replace the original monitoring values ​​(e.g., feature point correlation data determined based on acquired radio features) with the correlation data of feature points processed by sliding filtering, and then assign confidence weights to different feature points respectively.

[0097] In some embodiments, in response to a flight speed greater than a preset speed threshold, the processor does not perform sliding filter processing and assigns confidence weights to the feature points after the removal process is completed.

[0098] In some embodiments of this specification, random thermal noise and multipath jitter can be effectively suppressed by performing sliding filtering on feature points.

[0099] Signal-to-noise ratio (SNR) is the ratio of the effective signal power received at the antenna to the background noise power. Both UAVs and GNSS receivers at ground base stations can output the SNR at the antenna in real time. The antenna refers to the port of the GNSS receiver used for transmitting and receiving signals.

[0100] In some embodiments, when the drone and ground base station have multiple antenna ends (e.g., multiple antenna ends of a multi-antenna array), the processor can determine the average signal-to-noise ratio of the multiple antenna ends.

[0101] Confidence weight is a numerical value used to quantify the reliability of the data for each feature point. The value of the confidence weight can range from 0 to 1. The higher the confidence weight, the less the feature point is affected by multipath propagation, noise, or maneuvering errors.

[0102] In some embodiments, the confidence weight of a feature point is negatively correlated with the reprojection Doppler bias and signal-to-noise ratio corresponding to that feature point.

[0103] For example, the greater the reprojection Doppler bias and / or the lower the signal-to-noise ratio, the closer the confidence weight is to 0; conversely, the confidence weight is closer to 1.

[0104] In some embodiments, the processor can calculate the confidence weight using the attenuation formula (3), which is shown below:

[0105] (3)

[0106] In formula (3), Let the confidence weight be the i-th feature point. β is a preset adjustment coefficient. The values ​​of β and β range from [0,1]. The reprojection Doppler bias of the i-th feature point. Let be the signal-to-noise ratio of the i-th feature point.

[0107] In some embodiments, the processor can calculate confidence weights for different feature points separately, generating a confidence weight vector composed of confidence weights. For example, the confidence weight vector can be represented as (feature point 1, confidence 1; feature point 2, confidence 2; ...; feature point N, confidence N).

[0108] In some embodiments, the processor is further configured to: calculate a double-difference carrier phase sequence between the UAV and a ground base station. Within a preset time window, the processor calculates the coherence function value between the double-difference carrier phase sequence and a theoretical carrier phase sequence derived from the movement data of the real signal source and the three-dimensional motion of the UAV. When the coherence function value is lower than a preset coherence threshold, the data within the current sliding time window is marked as unreliable data, and the confidence weight of the feature points corresponding to the unreliable data is reduced.

[0109] A double-difference carrier phase sequence is a data sequence obtained by performing double-difference processing on carrier phase observations of the same signal source (e.g., the same satellite signal) simultaneously observed by an UAV and a ground base station. The double-difference process includes subtracting the carrier phase observations from different signal sources by the UAV to eliminate clock differences in the GNSS receiver, and further subtracting the carrier phase observations from the ground base station and the UAV to eliminate clock differences and atmospheric delay errors. The double-difference carrier phase sequence reflects the geometric distance difference between the UAV and the ground base station, as well as the abnormal phase offset differences caused by deceptive interference signals. The double-difference carrier phase sequence is composed of data from multiple consecutive sampling points that have undergone double-difference processing, arranged in chronological order.

[0110] Theoretical carrier phase sequence: refers to the error-free theoretical sequence of double-difference carrier phase values ​​calculated using geometric distances based on the movement data of the actual signal source (such as satellite ephemeris), the three-dimensional motion of the UAV, and the known location of the ground base station. The theoretical carrier phase sequence characterizes the double-difference phase variation pattern that should be observed under ideal conditions of no deceptive interference signals, no multipath effects, and no noise effects.

[0111] The coherence function value is a dimensionless numerical value used to measure the degree of linear correlation between two sequences (e.g., a double-difference carrier phase sequence and a theoretical carrier phase sequence) in the frequency domain. The coherence function value ranges from 0 to 1. The closer the coherence function value is to 1, the more consistent the phase change patterns of the two sequences are; the closer it is to 0, the lower the correlation between the two sequences in the frequency domain. The coherence function value is used to determine whether a radio signal is subject to deceptive interference or multipath interference.

[0112] In some embodiments, the processor performs Fourier transforms on the measured double-difference carrier phase sequence and the theoretical carrier phase sequence respectively, and calculates the ratio of their cross-power spectral density to their respective independent power spectral density as the coherence function value. In some embodiments, the processor may calculate the average coherence coefficient only for the main frequency band of carrier phase change (e.g., 0-10Hz, corresponding to the typical frequencies of UAV and signal source movement), and use the average coherence coefficient as the coherence function value within a preset time window.

[0113] A preset coherence threshold is a critical value of the coherence function used to determine whether data is reliable. For example, the preset coherence threshold can be set to 0.75-0.9. The preset coherence threshold can be preset by technical personnel.

[0114] In some embodiments, if the coherence function value is greater than or equal to a preset coherence threshold, it indicates that the data within the preset time window is consistent with theoretical expectations and the data is reliable, and the original confidence weight of the feature point remains unchanged.

[0115] In some embodiments, if the coherence function value is less than a preset coherence threshold, it indicates that the data within the preset time window is severely contaminated (e.g., affected by deceptive interference signals, severe multipath effects, or GNSS receiver failure), and the processor marks all feature points within the preset time window as untrusted data.

[0116] In some embodiments, the processor reduces the confidence weights of feature points marked as untrusted data. For example, the processor may multiply the original confidence weights by a decay factor less than 1 (e.g., 0.3), or directly force the feature point weights within the window to a very low value (e.g., 0.1).

[0117] In some embodiments of this specification, frequency domain quality assessment of feature point-related data is achieved by introducing coherence analysis of double-difference carrier phase sequences. When the coherence function value of the measured phase and the theoretical phase is below a threshold, the system can accurately identify carrier phase distortion caused by deceptive interference or complex multipath propagation, promptly mark the data within the corresponding window as unreliable and reduce its confidence weight, effectively suppressing the contamination of the spatiotemporal consistency check matrix by deceptive interference signals, avoiding misjudgment problems caused by low-quality data participating in weighted least squares calculations, and enhancing the robustness of deceptive interference signal identification.

[0118] Step 330: Construct a spatiotemporal consistency verification matrix based on confidence weights.

[0119] The spatiotemporal consistency verification matrix is ​​used to quantify the degree of consistency between the first signal characteristics received by the UAV and the second signal characteristics of the same signal source received by the ground base station in the temporal and spatial dimensions.

[0120] When the signal received by the UAV is a real signal emitted by the signal source (such as a satellite signal), the signal characteristics observed by the UAV and the ground base station should meet certain physical laws (e.g., geometric constraints formed by satellite position, UAV / base station position, signal propagation delay, etc.). When there is a deceptive interference signal, the spatiotemporal consistency check matrix may become ill-conditioned or have an abnormally large deviation.

[0121] In some embodiments, the processor can construct a weighted least squares matrix based on the confidence weights and the spatiotemporal consistency verification matrix, as shown in formula (4):

[0122] (4)

[0123] in, This is the spatiotemporal consistency verification matrix. A represents the deviation between observed and theoretical values ​​of multiple feature points in the first and / or second signal features (e.g., reprojection Doppler bias, etc.). A characterizes the parameter to be estimated for different deviations. The linearization sensitivity (e.g., partial derivatives), where W is a matrix of confidence weights. Let A be the transpose of A. Deviations caused by deceptive interference signals (e.g., position offset, clock offset, etc.).

[0124] In some embodiments of this specification, by setting confidence weights, the contribution of low-quality feature points (such as feature point data affected by multipath, non-line-of-sight transmission, or severe maneuvering) to the spatiotemporal consistency check matrix can be reduced, thereby effectively avoiding misjudgments caused by environmental noise and channel fading.

[0125] Step 340: Based on the analysis results of the spatiotemporal consistency verification matrix, determine the confidence level of the existence of deceptive interference signals at different feature points.

[0126] The analysis results of the spatiotemporal consistency verification matrix refer to the output obtained by the processor after performing numerical calculations and statistical tests on the weighted least squares matrix. For example, the analysis results include, but are not limited to, the weighted residual sum of squares (reflecting the degree of fit between observed and theoretical values), and the parameters to be estimated. (For example, position offset and clock deviation). The analysis results reflect the degree of spatiotemporal logical consistency between the observation data of the UAV and the ground base station, and serve as the basis for determining whether deceptive interference signals exist.

[0127] The confidence level of the presence of deceptive interference signals is a normalized quantitative indicator used to characterize the probability that the signal currently received by the drone originates from a deceptive source rather than a genuine signal source. The confidence level ranges from 0 to 1 (or 0% to 100%), with higher confidence levels indicating a greater likelihood of deceptive interference.

[0128] In some embodiments, the processor can determine the parameters to be estimated using the weighted least squares equation in step 330. and based on Determine the confidence level of the existence of a deceptive interference signal. For example, the confidence level of the existence of a deceptive interference signal and the parameter to be estimated. The magnitudes are positively correlated. Parameter to be estimated A sequence containing data representing positional offsets and clock deviations of different feature points.

[0129] In some embodiments, the processor is based on the parameters to be estimated. The confidence level of the presence of a deceptive interference signal is determined by weighting the ratios of the position offsets of different feature points to the reference offset, the ratios of the clock deviations to the reference clock deviations, and the comparison values. The reference offset and reference clock deviation are determined by the processor based on the average values ​​of the position offsets and clock deviations when there are no deceptive interference signals.

[0130] In some embodiments, the processor can estimate the parameters. In the confidence level, the maximum value of the ratio of the positional offset of different feature points to the reference offset, and the ratio of the clock deviation to the reference clock deviation, is used as the confidence level.

[0131] In some embodiments, in order to reduce the false alarm rate, the processor may employ a time smoothing strategy to take a weighted average of the confidence level at the current moment and the confidence level at historical moments as the confidence level of the finally determined deceptive interference signal.

[0132] In some embodiments, the processor is further configured to: determine spoofing interference parameters based on the analysis results of the spatiotemporal consistency check matrix. The spoofing interference parameters include false position drift and signal angle of arrival deviation. When it is determined that the UAV is within the spoofing interference range based on the spoofing interference parameters, an interference vector set is determined based on the false position drift and signal angle of arrival deviation; contaminated demodulated symbols or pseudorange observations with a matching degree higher than a preset matching degree threshold are removed from the navigation data; and the UAV's local clock is time-base locked.

[0133] False position drift refers to the vector difference between the spatial position of a UAV calculated from radio signals and the spatial position calculated from dead reckoning when it is affected by deceptive interference. The magnitude and direction of the false position drift reflect the degree of influence of the deceptive interference signal on the UAV's positioning.

[0134] Signal angle of arrival deviation refers to the angle difference between the measured signal angle of arrival (including azimuth and pitch) of the UAV and the theoretical angle of arrival calculated based on the signal source and the position of the UAV.

[0135] The deception interference range refers to the area affected by deceptive interference signals. For example, a processor can determine whether a drone is within the deception interference range based on whether the magnitude of the deception interference parameters exceeds a corresponding threshold. For instance, when the magnitude of the false position drift is greater than a preset drift threshold (e.g., 5 meters), or the signal angle of arrival deviation is greater than a preset angle threshold (e.g., 5 degrees), the processor determines that the drone is within the deception interference range.

[0136] In some embodiments, the area of ​​the deception interference range is less than or equal to the radiation range. For further explanation of the radiation range, see [link to relevant documentation]. Figure 4 And related descriptions.

[0137] The interference vector set refers to a multi-dimensional feature set constructed based on the spurious position drift vector and the angle of arrival deviation vector. The spurious position drift vector indicates the direction of the spatial offset induced by deception, while the angle of arrival deviation vector points to the possible direction of the deceptive interference signal source.

[0138] Demodulated symbols refer to the binary navigation message data demodulated from radio signals by GNSS receivers of UAVs and / or ground base stations. For example, demodulated symbols include satellite ephemeris, almanacs, and time information. It is understandable that deceptive interference signals maliciously modulate forged navigation messages, causing the demodulated symbols to differ from the actual signals.

[0139] Pseudorange observations refer to the distance observations obtained by multiplying the time delay of radio signals from transmission to reception by the GNSS receiver of UAVs and / or ground base stations by the speed of light. They include the true geometric distance as well as errors such as clock deviation and atmospheric delay.

[0140] Matching degree refers to the degree of similarity between the demodulated symbols or pseudorange observations currently received by the UAV and / or ground base station and the relevant data in the interference vector set. For example, the matching degree can be a value between 0 and 1. The higher the matching degree, the greater the probability that the data has been contaminated by deceptive interference signals. In some embodiments, the processor can use the correlation coefficient between the demodulated symbols or pseudorange observations and the relevant data in the interference vector set as the matching degree.

[0141] The preset matching threshold is a critical matching value used to determine whether demodulated symbols or pseudorange observations should be discarded. For example, the preset matching threshold ranges from 0.7 to 0.9. When the matching degree of a data point is higher than the preset matching threshold, the data is considered severely contaminated and is discarded.

[0142] Local clock: refers to the high-stability crystal oscillator or atomic clock of the drone, used to provide a time reference for navigation calculations, data sampling, communication protocols, etc.

[0143] Time base locking refers to the process where, upon detecting a deceptive jamming signal, the processor stops using external GNSS signals to correct the local clock and instead relies solely on the local clock's own oscillator to maintain the time reference. Simultaneously, the last reliable timestamp before locking is recorded as a time reference for subsequent dead reckoning. Time base locking prevents deceptive jamming signals from injecting false time information and causing deviations in the UAV's local clock.

[0144] As an example, assuming the drone is in flight, the processor determines the presence of a deceptive interference signal with a confidence level of 0.95 and extracts the deceptive interference parameters: false position drift (50 meters east, 10 meters north, 2 meters upward), Doppler deviation of 25 Hz, and angle of arrival deviation of 8 degrees, indicating it is within the deceptive interference range. The processor constructs an interference vector set and then iterates through the pseudorange observations in the navigation data. It finds that the pseudorange residual of a certain signal source consistently deviates from the theoretical value by approximately 150 meters, and this deviation direction is consistent with the drift vector, with a matching degree of 0.85. The processor removes this pseudorange observation from the positioning calculation. Simultaneously, it sends a command to lock the local clock.

[0145] In some embodiments of this specification, an interference vector set is constructed using multi-dimensional deception parameters to accurately remove contaminated demodulated symbols and pseudorange observations, achieving underlying purification of navigation data. Simultaneously, by locking the local clock, deceptive interference signals are prevented from manipulating the system time, effectively blocking deception penetration and ensuring the positioning integrity and time base security of the UAV in interference environments.

[0146] Step 350: In response to the confidence level meeting the preset conditions, control the UAV to perform a temporary switch of navigation source.

[0147] Preset conditions refer to the confidence criteria that trigger a temporary switch of navigation sources. Preset conditions can be composed of one or more sub-conditions to ensure the reliability and robustness of the switch decision.

[0148] In some embodiments, the preset conditions include, but are not limited to: a confidence level higher than a preset confidence threshold, or a confidence level greater than a preset confidence threshold for multiple sampling periods. The preset confidence threshold can be constructed by the processor based on historical deception interference data or set by technicians based on experience.

[0149] Temporary navigation source switching refers to the process by which the processor controls the UAV to disconnect from the Global Positioning System (GPS) within a preset time period after determining that there is a deceptive interference signal in the current radio signal, forcibly switch to the backup autonomous navigation mode, and decide whether to restore GNSS after the end of the time period based on the signal quality.

[0150] In some embodiments, after determining that there is a deceptive interference signal in the current radio signal, the processor can control the UAV to switch to a fusion dead reckoning system of Inertial Measurement Unit (IMU) and Visual Odometry (VO), and control the UAV to perform directional flight maneuvers so that the UAV's flight trajectory matches the preset flight path.

[0151] In some embodiments, the temporary switching of the navigation source includes: in response to a confidence level higher than a preset confidence threshold, the processor controls the UAV to disconnect the Global Positioning System (GNSS) data link and switch to a dead reckoning mode based on the fusion of inertial navigation system and visual odometry within a preset time period, so that the deviation of the UAV's subsequent flight trajectory from the preset flight route is kept within a preset deviation range, wherein the length of the preset time period is negatively correlated with the complexity of the UAV's three-dimensional motion, and the complexity of the three-dimensional motion is determined based on the magnitude of the UAV's instantaneous velocity, angular velocity, and maneuvering acceleration.

[0152] A data link refers to the data transmission channel between a UAV's GNSS receiver and processor. For example, a data link can be a collective term for the physical layer (e.g., data bus, RF front-end), the driver layer (device driver, DMA channel, etc.), and the logic layer (data protocol, message parsing, etc.). Disconnecting the data link means blocking the reception of radio signals from one or more of these layers.

[0153] Dead reckoning is a navigation method that does not rely on external signals (such as radio signals received by the UAV) but only on motion parameters measured by the UAV's own sensors (such as IMU, odometer, vision camera, etc.) to recursively deduce the current position from a known initial position. In some embodiments, dead reckoning includes a navigation mode that integrates IMU and VO, where the IMU provides acceleration and angular velocity measurements at a preset frequency (for short-time accurate integration), and the VO provides lower-frequency but drift-free position / velocity corrections (to suppress long-term IMU drift).

[0154] The subsequent flight path refers to the flight path of the UAV at points in time after the current moment. The subsequent flight path includes the flight path predicted by the UAV after a temporary switch in navigation sources, based on a dead reckoning model fused with inertial navigation and visual odometry. The subsequent flight path can consist of a series of spatially discrete points with timestamps, each point containing three-dimensional coordinates and an estimated time of arrival.

[0155] A preset flight path refers to a three-dimensional path that a UAV sets in advance before performing a mission. A preset flight path can contain a series of ordered waypoint coordinates.

[0156] Deviation refers to the degree of spatial deviation between the drone's flight trajectory and the preset flight path. Deviation can be determined using various measurement methods. For example, it can be based on the maximum lateral deviation (the shortest vertical distance from any point on the flight trajectory to the preset flight path), the root mean square deviation (the root mean square value of the shortest vertical distance from each sampling point on the flight trajectory to the preset flight path), and / or the heading angle deviation (the angular difference between the drone's current heading and the direction of the preset flight path). Deviation is positively correlated with any one of the maximum lateral deviation, root mean square deviation, and heading angle deviation.

[0157] For example, the processor can perform weighted summation and normalization of the maximum lateral deviation, root mean square deviation, and heading angle deviation to determine the degree of deviation.

[0158] The preset deviation range refers to the preset maximum deviation threshold. For example, the preset deviation range may include a lateral deviation of no more than 5 meters or a root mean square deviation of no more than 3 meters. The preset deviation range can be predetermined by the mission requirements of the UAV. For example, for high-precision missions (such as power line inspection requiring close proximity to cables), the preset deviation range can be set to a smaller value (such as 1 meter); for ordinary flight missions, it can be set to a larger value (such as 10 meters).

[0159] The preset time period refers to the duration for which the UAV maintains dead reckoning mode after disconnecting from GNSS.

[0160] The complexity of three-dimensional motion is a dimensionless indicator that characterizes the intensity of a drone's current flight motion. For example, the complexity value ranges from 0 to 1.

[0161] In some embodiments, the processor can determine the complexity of the three-dimensional motion of the UAV through various methods such as table lookup, data retrieval, and statistical analysis.

[0162] For example, the greater the instantaneous speed, angular velocity, and acceleration of a drone during flight, the higher the complexity of setting up its three-dimensional motion. High complexity means that the drone is in a high-speed maneuvering state, at which point the drift accumulation rate of pure inertial navigation is fast, so a shorter preset time period needs to be set to control position error.

[0163] Instantaneous velocity refers to the flight speed of the UAV at a specific moment or sampling point. Angular velocity refers to the velocity related to the UAV's change of direction angle or rotation, which can be extracted from the three-axis gyroscope measurements of the UAV's IMU. Maneuvering acceleration refers to the acceleration of the UAV at a specific moment or sampling point, which can be extracted from the three-axis accelerometer measurements of the IMU.

[0164] In some embodiments, the processor can perform weighted summation and normalization on the instantaneous velocity, angular velocity, and maneuvering acceleration of the UAV to determine the complexity. The weighting coefficients used in the weighted summation can be preset by an expert.

[0165] In some embodiments, the processor can determine the preset time period based on complexity through various methods such as table lookup, database retrieval, and calculation.

[0166] For example, the preset time period = baseline time period + dynamic time period * (1 - complexity / baseline complexity). Here, the baseline complexity is a preset dimensionless value, corresponding to the baseline time period. The baseline time period and dynamic time period are preset values. For example, the baseline time period is 20-30 seconds, and the dynamic time period is 10-15 seconds, etc.

[0167] In some embodiments of this specification, an adaptive duration control for temporary switching of the UAV's navigation source is achieved by introducing a preset time period that is negatively correlated with the complexity of three-dimensional motion. When the UAV is maneuvering at high speed, the preset time period is automatically shortened to prevent the cumulative drift of the fusion of inertial navigation and visual odometry from exceeding the threshold. When the UAV is flying at low speed or hovering, the preset time period is extended to wait for the deception interference to subside or for the UAV to fly out of the threat area, ensuring that the flight trajectory after switching always stays within the preset deviation range from the preset route. This effectively blocks the penetration of deception signals into navigation and maximizes the continuity of the mission, significantly improving the survivability and autonomous flight safety of the UAV in complex electromagnetic environments.

[0168] In some embodiments of this specification, by introducing confidence weights determined based on reprojection Doppler bias and signal-to-noise ratio, and constructing a spatiotemporal consistency verification matrix, interference from low-altitude maneuvers, multipath propagation, and non-line-of-sight transmissions can be effectively suppressed, false alarms and missed alarms can be reduced, and the identification accuracy against highly deceptive jamming signals can be improved. Temporary switching can balance flight safety and mission continuity, providing data support for subsequent deception source localization, flight path reconstruction, and group coordinated countermeasures.

[0169] In some embodiments, the processor is configured to: determine the spatial orientation information of the deceptive interference signal source based on the direction-finding data of the multi-antenna array of the ground base station, and synchronize it to the three-dimensional geographic information system; determine the radiation range of the deceptive interference signal source based on the spatial orientation information and false position drift of the deceptive interference signal source; predict the rate of change of distance between the UAV and the radiation range based on the subsequent flight trajectory and the radiation range; and control the UAV to pre-reconstruct a preset flight path in response to the distance change rate being less than a preset change rate threshold.

[0170] Figure 4 This is an exemplary flowchart illustrating the pre-reconstruction of a preset flight path according to some embodiments of this specification.

[0171] In some embodiments, such as Figure 4 As shown, process 400 includes steps 410 to 440. In some embodiments, process 400 may be executed by a processor.

[0172] Step 410: Based on the direction finding data of the multi-antenna array of the ground base station, determine the spatial orientation information of the deceptive interference signal source and synchronize it to the three-dimensional geographic information system.

[0173] A multi-antenna array refers to an array of antenna elements (e.g., three or more) arranged in a specific geometric layout (such as a rectangular array, circular array, or L-shaped array) equipped on a ground base station. In a multi-antenna array, each antenna element independently receives radio signals from the same signal source. By processing the signals received by each antenna element, the angle of arrival of the received signal can be calculated. Multi-antenna arrays have spatial resolution capabilities, allowing them to distinguish signals from different directions.

[0174] Direction finding data refers to the angle of arrival measurement based on deceptive interference signals received by a multi-antenna array (e.g., fake GNSS signals with the same frequency as real satellite signals). Direction finding data includes azimuth and elevation angles.

[0175] Spatial orientation information of a deceptive jamming signal source refers to the location coordinates (e.g., longitude, latitude, and / or altitude) of the deceptive jamming signal source in three-dimensional space. Spatial orientation information may also include the direction vector of the deceptive jamming signal source relative to different ground base stations. In some embodiments, spatial orientation information can be absolute coordinates (e.g., geographic coordinates) or relative positions (e.g., estimated distance and direction relative to a ground base station).

[0176] In some embodiments, the processor can determine the spatial orientation information of the deceptive interference signal source based on direction-finding data from multiple antenna arrays of multiple ground base stations through various methods (e.g., statistical induction, calculation, linear fitting, etc.). For example, the processor can determine the spatial orientation information of the deceptive interference signal source through cross-location. For example, the processor can use the known geographical coordinates of the ground base stations and the direction-finding information monitored by the ground base stations to take the intersection of the geographical coordinates of the ground base stations and the corresponding straight lines / rays as the spatial orientation information of the deceptive interference signal source.

[0177] In some embodiments, due to factors such as measurement noise and multipath interference from ground base stations, the directions of each line / ray will not strictly intersect. The processor can use the least squares method to estimate the spatial orientation information of the deceptive interference signal source, minimizing the sum of the squared weighted distances of all lines / rays to this spatial orientation information. The processor can output the spatial coordinates of the deceptive source and its orientation vector relative to each base station.

[0178] In some embodiments, if only one ground base station can detect the spoofing signal (e.g., other ground base stations are blocked or too far away), cross-location cannot be achieved. In this case, the processor can combine an orientation estimation model to estimate the approximate orientation and distance range of the spoofing interference signal source. In some embodiments, when using a single ground base station or relying solely on a drone for locating the spoofing interference signal source, the processor marks the location result as low-precision and includes the corresponding uncertainty when synchronizing it to the 3D geographic information system.

[0179] In some embodiments, after determining the spatial orientation information, the processor encapsulates the spatial orientation information, uncertainty, timestamp, and other information of the deceptive interference signal source, and sends it to the three-dimensional geographic information system via wired or wireless network to achieve real-time synchronization and visualization updates.

[0180] Azimuth estimation models are used to estimate the spatial orientation information of deceptive interference signal sources. Azimuth estimation models can be machine learning models. For example, azimuth estimation model can be a Recurrent Neural Network (RNN) model, a Long Short Term Memory (LSTM) model, or a combination thereof.

[0181] In some embodiments, the inputs to the azimuth estimation model include the received signal strength, carrier frequency, and antenna loss, and the outputs of the azimuth estimation model are the estimated distance, the estimated azimuth, and the corresponding uncertainty.

[0182] Received signal strength refers to the signal power of deceptive interference signals received by ground base stations and / or drones. Antenna loss refers to the attenuation of signal power at the antenna end of the ground base station or drone, which can be determined through simulation testing.

[0183] Uncertainty refers to the quantitative representation of the confidence interval or error range of the estimated distance and azimuth. Uncertainty reflects the degree of deviation between the output of the azimuth estimation model and the true value caused by factors such as signal strength fluctuations, multipath effects, antenna parameter errors, and environmental noise.

[0184] The greater the uncertainty, the greater the fluctuation range of the azimuth estimation model's output. For example, an uncertainty of (-10%, +20%) means that the estimated distance output by the azimuth estimation model will fluctuate between 0.9 times and 1.2 times the estimated distance. Similarly, an uncertainty of 10% means that the azimuth angle output by the azimuth estimation model will fluctuate within 10%.

[0185] In some embodiments, the orientation estimation model can be determined through model training based on training samples with training labels. During training, training samples are input into the initial orientation estimation model, a loss function is constructed based on the output of the initial orientation estimation model and the training labels, and the parameters of the initial orientation estimation model are iteratively updated based on the loss function until preset training conditions are met, at which point training ends and the trained orientation estimation model is obtained. The preset training conditions may include, but are not limited to, loss function convergence, reaching a threshold training period, etc., and the iterative update methods may include gradient descent or simulated annealing algorithms.

[0186] In some embodiments, the processor can use the estimated distance, estimated azimuth, and corresponding uncertainty output by the azimuth estimation model as spatial azimuth information.

[0187] Step 420: Determine the radiation range of the deceptive interference signal source based on its spatial orientation information and false position drift.

[0188] For more information on spurious position drift, see [link to documentation]. Figure 3 And related descriptions.

[0189] The radiation range refers to the spatial area within which a deceptive jamming signal emitted by a deceptive jamming source can effectively affect a drone. Within the radiation range, the signal power of the deceptive jamming signal is sufficient to suppress or overwhelm the real signal (e.g., satellite signals, base station signals, etc.), or the phase and carrier frequency of the deceptive jamming signal can be successfully tracked by the drone's GNSS receiver, causing the drone to calculate incorrect positioning results. In some embodiments, the radiation range is represented by a spatial geometry centered on the deceptive source, such as a spatial region composed of a sphere, ellipsoid, or an isoelectric surface.

[0190] In some embodiments, the processor can determine the radiation range of the deceptive interference signal source based on its spatial orientation information and false position drift, using various methods (e.g., statistical induction, fitting, linear estimation, etc.).

[0191] In some embodiments, the processor determines the amount of false position drift at different locations along the drone's flight path, identifies locations where the false position drift changes from below a preset drift threshold to above a preset drift threshold, and locations where the false position drift falls back from above the preset drift threshold to below the preset drift threshold. The processor can calculate the distances from these locations to the spatial coordinates of the deceptive jamming signal source, and determine the radiation range of the deceptive jamming signal based on these distances.

[0192] In some embodiments, the processor can combine the distances from multiple locations to the spatial coordinates of the deceptive interference signal source, and take the maximum or average distance as the radius of the radiation range, thereby determining a spherical radiation range centered on the deceptive interference signal source and with the radius as its length.

[0193] In some embodiments, when only the azimuth angle of the deceptive interference signal source is available, the processor can use the gradient direction of the false position drift amount and the geometric distribution of multiple false position drift amounts from below a preset drift threshold to above a preset drift threshold and from above a preset drift threshold back to below a preset drift threshold to estimate the location and radiation range of the deceptive interference signal source through cross-location or least squares fitting.

[0194] Step 430: Based on the subsequent flight trajectory and radiation range, predict the rate of change of distance between the UAV and the radiation range.

[0195] For an explanation of the subsequent flight path, please refer to [link / reference]. Figure 3 And related descriptions.

[0196] The rate of change of distance refers to the degree to which a drone moves closer to or further away from the radiation range. A positive value of the rate of change of distance indicates that the drone is moving away from the radiation range, while a negative value indicates that the drone is moving closer to the radiation range.

[0197] In some embodiments, the processor can predict the rate of change of distance using various methods (e.g., statistical analysis, mathematical fitting, calculation, etc.). For example, the processor can acquire the current position and velocity vector of the drone and determine the shortest distance from the drone's current position to the boundary of the radiation range based on the shape of the radiation range boundary (e.g., a sphere). The processor can estimate the drone's subsequent position after flying along the current velocity direction for a short time interval (e.g., 1 second) based on its subsequent flight trajectory and calculate the shortest distance from that subsequent position to the boundary of the radiation range. When the drone is inside the radiation range, the distance is negative (or defined as a negative distance), and a negative rate of change of distance indicates further penetration into the radiation range.

[0198] In some embodiments, the processor determines the distance change rate by projecting the UAV's velocity vector onto the normal direction of the radiation range boundary based on the subsequent flight trajectory. For example, for a radiation range shaped like a sphere, the distance from the UAV's current position to the center of the sphere is first calculated. Then, the projection component of the velocity vector in the radial direction from the center of the sphere towards the UAV is determined, and this projection component is used as the distance change rate of the UAV relative to the radiation range boundary. A positive distance change rate indicates moving away from the radiation range, and a negative value indicates moving closer to the radiation range. When the UAV is inside the sphere, a positive distance change rate indicates that the UAV is moving closer to the radiation range boundary, and a negative rate indicates that the UAV is moving closer to the center of the radiation range.

[0199] In some embodiments, when the radiation range is not a regular sphere (e.g., an ellipsoid or an irregular shape), the processor can find the point closest to the UAV on the boundary of the radiation range, calculate the unit normal vector of the boundary of the radiation range corresponding to that point, and then project the UAV velocity vector onto the normal vector to obtain the distance change rate.

[0200] In some embodiments, the processor repeats the prediction process described above at a preset frequency and updates the predicted distance change rate in real time. When the distance change rate is negative and its absolute value is large, it indicates that the drone is approaching the center of the deception and interference area, requiring timely evasive action. The preset frequency can be pre-set by a technician and input into the processor.

[0201] Step 440: In response to the distance change rate being less than a preset change rate threshold, control the UAV to pre-reconstruct the preset flight path.

[0202] The preset rate of change threshold is a critical distance change rate value used to determine whether a drone is approaching the radiation range of a deceptive jamming signal source. The preset rate of change threshold is negative (e.g., -5 m / s). When the distance change rate is less than the preset rate of change threshold, it indicates that the drone is approaching the radiation range at a speed exceeding the permissible limit. The preset rate of change threshold can be preset by the processor based on the drone's braking capability, flight path adjustment response time, and mission safety level, or it can be adaptively learned by the processor based on historical flight data.

[0203] Pre-reconstruction refers to proactively modifying or replacing a preset flight path in advance. Unlike the temporary switching of the navigation source in step 350, pre-reconstruction focuses on spatial planning adjustments of the flight path, rather than switching the navigation source. In some embodiments, the processor can be set to trigger pre-reconstruction immediately when the distance change rate is abnormal, controlling the UAV to perform maneuvers such as detours or avoidance maneuvers.

[0204] In some embodiments, the processor can pre-reconstruct a preset flight path in a variety of ways.

[0205] In some embodiments, the processor can use the boundary of the radiation range as an obstacle, and based on the UAV's maximum turning radius and maximum climb rate, generate an avoidance path in three-dimensional space from the UAV's current position to the coordinates of a waypoint on the other side of the original preset flight path, thereby pre-reconstructing the preset flight path. For example, the processor can determine the avoidance path based on an avoidance path generation algorithm (e.g., fast expanding random tree, gradient descent based on potential field method, etc.). The maximum climb rate refers to the maximum climb altitude of the UAV per unit time.

[0206] In some embodiments, when the radiation range is large or the distance change rate is high (e.g., below -15 m / s), the processor sets the radiation range as a no-fly zone based on the UAV's current position, remaining mission target points, and all known radiation range information (e.g., radiation range radius, radiation range boundary coordinates, etc.), and regenerates a flight path that completely avoids the influence of the radiation range, thus pre-reconstructing the preset flight path. For example, the processor can generate multiple candidate flight paths and select the candidate flight path with the shortest total flight distance as the final determined preset flight path to reduce energy consumption.

[0207] In some embodiments, when the distance change rate is negative but the absolute value is small (e.g., -2m / s) and the radius of the radiation range is less than a preset radius threshold, the processor can control the drone to decelerate or hover and wait until the deceptive interference signal source moves or the mission command changes before continuing to fly.

[0208] In some embodiments of this specification, by real-time monitoring of the distance change rate, the flight path is proactively reconstructed before the UAV is detected approaching the deceptive radiation range, thereby achieving preventative avoidance and effectively preventing the UAV from entering or penetrating the radiation area, ensuring flight safety. At the same time, by partially detouring or adjusting altitude, the original mission flight path is preserved to the greatest extent possible, balancing safety and mission continuity.

[0209] In some embodiments of this specification, the deceptive interference signal source is accurately located by direction finding through multiple base stations and / or multiple antenna arrays. The radiation range is dynamically defined by combining the false position drift amount, thereby predicting the rate of change of distance between the UAV and the interference zone, realizing the active reconstruction of the flight path, effectively preventing the UAV from being continuously affected by the deceptive interference signal source, and ensuring flight safety and mission continuity.

Claims

1. A method for identifying deceptive electromagnetic interference based on collaborative sensing, characterized in that, The method is executed by a processor and includes: Acquire a first signal feature and a second signal feature; the first signal feature is a radio feature associated with the three-dimensional motion and antenna reception angle of the UAV during flight, and the second signal feature is a radio feature collected by a ground base station, wherein the acquisition time and signal source corresponding to the second signal feature are the same as those of the first signal feature; Based on the reprojection Doppler bias of different feature points in the first and second signal features, and the signal-to-noise ratio at the antenna ends of the UAV and the ground base station, confidence weights are assigned to the different feature points respectively. Based on the confidence weights, a spatiotemporal consistency verification matrix is ​​constructed; Based on the analysis results of the spatiotemporal consistency verification matrix, the confidence level of the existence of deceptive interference signals at the different feature points is determined; In response to the confidence level meeting a preset condition, the drone is controlled to temporarily switch its navigation source.

2. The method according to claim 1, characterized in that, The temporary switching of the navigation source includes: In response to the confidence level exceeding a preset confidence threshold, the UAV is controlled to disconnect the Global Positioning System (GNSS) data link within a preset time period and switch to a dead reckoning mode based on the fusion of inertial navigation system and visual odometry, so that the deviation of the UAV's subsequent flight trajectory from the preset flight path remains within a preset deviation range. The length of the preset time period is negatively correlated with the complexity of the UAV's three-dimensional motion, which is determined based on the magnitude of the UAV's instantaneous velocity, angular velocity, and maneuvering acceleration.

3. The method according to claim 2, characterized in that, Also includes: Based on the direction-finding data of the multi-antenna array of the ground base station, the spatial orientation information of the deceptive interference signal source is determined and synchronized to the three-dimensional geographic information system; Based on the spatial orientation information and false position drift of the deceptive interference signal source, the radiation range of the deceptive interference signal source is determined; Based on the subsequent flight trajectory and the radiation range, predict the rate of change of distance between the UAV and the radiation range; In response to the distance change rate being less than a preset change rate threshold, the drone is controlled to pre-reconstruct the preset flight path.

4. The method according to claim 1, characterized in that, The method further includes: Based on the three-dimensional motion of the UAV and the antenna receiving angle, the reference Doppler frequency shift of multiple feature points in the first signal feature is determined; Based on the reference Doppler frequency shift of multiple feature points in the first signal feature and the measured Doppler frequency shift of the UAV, the reprojection Doppler deviation of different feature points in the first signal feature is determined, and feature points whose reprojection Doppler deviation exceeds a preset dynamic outlier threshold are removed. In response to the fact that the flight speed of the UAV is not greater than a preset speed threshold, sliding filtering is performed on the feature points that have been removed within a preset time window in the first signal feature and the second signal feature.

5. The method according to claim 1, characterized in that, Also includes: Based on the analysis results of the spatiotemporal consistency verification matrix, the deception interference parameters are determined, including the false position drift and the signal angle of arrival deviation. When the UAV is determined to be within the deception interference range based on the deception interference parameters, an interference vector set is determined based on the false position drift and / or the signal angle of arrival deviation. Contaminated demodulated symbols or pseudorange observations with a matching degree higher than a preset matching degree threshold are removed from the navigation data, and the local clock of the UAV is time-base locked.

6. A deceptive electromagnetic interference identification system based on cooperative sensing, characterized in that, include: The signal processing module is configured to acquire a first signal feature and a second signal feature; The first signal feature is a radio feature associated with the three-dimensional motion and antenna reception angle of the UAV during flight, and the second signal feature is a radio feature collected by the ground base station. The collection time and signal source corresponding to the second signal feature are the same as those of the first signal feature. The weight allocation module is configured to assign confidence weights to the different feature points based on the reprojection Doppler bias of different feature points in the first signal feature and the second signal feature, as well as the signal-to-noise ratio at the antenna end of the UAV and the ground base station. The construction module is configured to construct a spatiotemporal consistency verification matrix based on the confidence weights; The determination module is configured to determine the confidence level of the existence of deceptive interference signals at different feature points based on the analysis results of the spatiotemporal consistency verification matrix. The control module is configured to control the UAV to perform a temporary switching of navigation sources in response to the confidence level meeting a preset condition.

7. The system according to claim 6, characterized in that, The control module is further configured to: In response to the confidence level exceeding a preset confidence threshold, the UAV is controlled to disconnect the Global Positioning System (GNSS) data link within a preset time period and switch to a dead reckoning mode based on the fusion of inertial navigation system and visual odometry, so that the deviation of the UAV's subsequent flight trajectory from the preset flight path remains within a preset deviation range. The length of the preset time period is negatively correlated with the complexity of the UAV's three-dimensional motion, which is determined based on the magnitude of the UAV's instantaneous velocity, angular velocity, and maneuvering acceleration.

8. The system according to claim 7, characterized in that, The control module is further configured to: Based on the direction-finding data of the multi-antenna array of the ground base station, the spatial orientation information of the deceptive interference signal source is determined and synchronized to the three-dimensional geographic information system; Based on the spatial orientation information and false position drift of the deceptive interference signal source, the radiation range of the deceptive interference signal source is determined; Based on the subsequent flight trajectory and the radiation range, predict the rate of change of distance between the UAV and the radiation range; In response to the distance change rate being less than a preset change rate threshold, the drone is controlled to pre-reconstruct the preset flight path.

9. The system according to claim 6, characterized in that, The signal processing module is further configured to: Based on the three-dimensional motion of the UAV and the antenna receiving angle, the reference Doppler frequency shift of multiple feature points in the first signal feature is determined; Based on the reference Doppler frequency shift of multiple feature points in the first signal feature and the measured Doppler frequency shift of the UAV, the reprojection Doppler deviation of different feature points in the first signal feature is determined, and feature points whose reprojection Doppler deviation exceeds a preset dynamic outlier threshold are removed. In response to the fact that the flight speed of the UAV is not greater than a preset speed threshold, sliding filtering is performed on the feature points that have been removed within a preset time window in the first signal feature and the second signal feature.

10. The system according to claim 6, characterized in that, The determining module is further configured as follows: Based on the analysis results of the spatiotemporal consistency verification matrix, the deception interference parameters are determined, including the false position drift and the signal angle of arrival deviation. When the UAV is determined to be within the deception interference range based on the deception interference parameters, an interference vector set is determined based on the false position drift and / or the signal angle of arrival deviation. Contaminated demodulated symbols or pseudorange observations with a matching degree higher than a preset matching degree threshold are removed from the navigation data. The control module is further configured to perform time base locking on the local clock of the UAV.

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