A cross-validated drone anti-decoy method, device, and medium

Abstract

The application provides a cross-verification unmanned aerial vehicle anti-decoy method, device and medium. The method according to the application comprises the following steps: obtaining a local timestamp T1 based on a high-precision local crystal oscillator integrated in the unmanned aerial vehicle, obtaining a standard time T2 provided by a GNSS navigation system, and calculating a time deviation AT of the local timestamp T1 and the standard time T2; obtaining an autonomous positioning result P1 of the unmanned aerial vehicle through a visual SLAM module, simultaneously obtaining a real-time positioning result P2 of the GNSS system, and calculating a spatial deviation AP of the positioning results P1 and P2; comparing the time deviation AT with a preset time deviation threshold T thr Comparing the spatial deviation AP with a preset spatial deviation threshold P thr Comparing the spatial deviation AP with a preset spatial deviation threshold P thr According to the comparison result, it is determined whether the unmanned aerial vehicle is decoyed; and according to the determination result, a preset safety response mechanism is triggered. The technical scheme provided by the application can improve the navigation safety and control reliability in a high-risk environment.

Description

TECHNICAL FIELD

[0001] The present document relates to the technical field of UAV anti-deception, and particularly relates to a cross-verification UAV anti-deception method, device and medium. BACKGROUND

[0002] At present, the navigation and positioning of a UAV highly depends on a GNSS system, which plays an important role in fields such as civilian aerial photography, logistics transportation, surveying and mapping, and the like. However, in special scenarios such as border control and military operations, GNSS signals are extremely vulnerable to attacks and are easily interfered with or deceived. Once the GNSS signals are tampered with, the UAV may deviate from the flight path, enter a forbidden area, or even be controlled by the enemy, which may result in serious consequences such as mission failure, and may also cause major risks such as political crises and military secrets leakage. To address the GNSS signal security problem, there are various anti-deception means in the prior art, such as encryption processing of GNSS signals, use of frequency hopping communication technology to avoid interference, use of an AI anomaly recognition model to detect signal anomalies, and use of a geographic fence to limit the flight range. However, the prior art has the following problems:

[0003] 1. Most anti-deception methods only detect GNSS position drift or trajectory anomalies, without introducing time consistency verification;

[0004] 2. Although visual SLAM has independent positioning capability, if the UAV mapping environment is tampered with, it may still cause recognition errors;

[0005] 3. Most navigation and positioning schemes rely on electromagnetic signal features for anomaly detection, but it is difficult to play a role in the face of position simulators and directional antennas, and lacks a mechanism to counteract false signals;

[0006] Therefore, the prior art is difficult to build a comprehensive and highly reliable UAV anti-deception system, and there is an urgent need for a multi-dimensional verification method that integrates local time sources, GNSS space-time features, and visual SLAM autonomous positioning capabilities to improve the navigation safety and control reliability of the UAV in high-risk environments. SUMMARY

[0007] According to the embodiments of the present application, a cross-verification UAV anti-deception method, device and medium are provided, which solve the problem that the time dimension verification, visual SLAM and GNSS are not effectively integrated in the prior art anti-deception scheme.

[0008] According to the embodiments of the present application, a cross-verification UAV anti-deception method is provided, which includes:

[0009] acquire a local timestamp T1 based on a high-precision local crystal oscillator integrated inside the UAV, acquire a standard time T2 provided by a GNSS navigation system, and calculate a time deviation AT between the local timestamp T1 and the standard time T2;

[0010] acquire an autonomous positioning result P1 of the UAV through a visual SLAM module, simultaneously acquire a real-time positioning result P2 of the GNSS system, and calculate a spatial deviation AP between the positioning results P1 and P2;

[0011] compare the time deviation AT with a preset time deviation threshold T thr compare the spatial deviation AP with a preset spatial deviation threshold P thr compare, and determine whether the UAV is deceived according to a comparison result;

[0012] trigger a preset safety response mechanism according to a determination result.

[0013] According to an embodiment of the present application, an electronic device is provided, comprising:

[0014] a processor; and

[0015] a memory arranged to store computer-executable instructions that, when executed, cause the processor to perform the steps of the cross-verification UAV deception prevention method described above.

[0016] According to an embodiment of the present application, a storage medium is provided for storing computer-executable instructions that, when executed, implement the steps of the cross-verification UAV deception prevention method described above.

[0017] By adopting the embodiments of the present application, a complete technical system that integrates a time and space two-dimensional determination mechanism is proposed, overcoming the limitations brought by the current single determination method that only relies on GNSS position anomaly detection or signal feature analysis. First, the present application integrates a local high-precision crystal oscillator at the UAV end to construct an independent time reference system, which can verify the credibility of GNSS time in real time and fundamentally improve the recognition ability of time synchronization type deception attacks. Second, a visual SLAM module is introduced as a non-GNSS dependent autonomous positioning means, and through coordinate comparison with the GNSS position, spatial dimension anomaly detection is realized, which is particularly suitable for robust flight requirements in complex border environments and enemy electromagnetic interference scenarios. In addition, the present application constructs a standardized cross-determination logic, allowing time anomalies and spatial anomalies as parallel inputs to trigger the deception recognition mechanism. Finally, the system also designs a safety response strategy based on the determination level, from early warning, flight restriction, return to the self-destruction mechanism when necessary, with a complete safety side. Therefore, the present application is superior to the prior art in deception recognition ability, safety response strategy, etc., and has significant innovation and practical value. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a cross-validation method for preventing drone deception according to an embodiment of the present invention;

[0020] Figure 2 This is a specific implementation process of the cross-validation drone anti-deception method according to an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0022] Method Implementation Examples

[0023] According to embodiments of the present invention, a cross-validation method for preventing drone deception is provided. Figure 1 The flowchart of the cross-validation drone anti-spoofing method of this invention is based on... Figure 1 As shown, the cross-validation method for preventing drone deception in this embodiment of the invention specifically includes:

[0024] S1. Obtain the local timestamp T1 based on the high-precision local crystal oscillator integrated inside the UAV, obtain the standard time T2 provided by the GNSS navigation system, and calculate the time deviation ΔT between the local timestamp T1 and the standard time T2.

[0025] like Figure 2 The diagram illustrates a specific implementation flow of this invention. A high-precision local crystal oscillator integrated within the UAV serves as an independent time reference source, acquiring a local timestamp T1 in real time and comparing it with the standard time T2 provided by the GNSS navigation system. A time deviation threshold of T is set between the two. thr, if the difference ΔT between GNSS time and local crystal oscillator time exceeds the threshold value, it is judged that the GNSS time is abnormal. Such deviation may be caused by the slight error in time synchronization of the fake GNSS signal. Through this mechanism, even if the GNSS position information still remains reasonable on the surface, the potential deception behavior can be identified in time in the time dimension, which supplements the traditional method of relying only on position information to judge whether it is deceived. Using the local high-precision crystal oscillator time as an independent reference, compare it with the GNSS system time, when the deviation between the two exceeds the set threshold T thr , it is determined that the GNSS time is abnormal, which constitutes the time dimension identification ability of the deception signal.

[0026] S2, obtain the autonomous positioning result P1 of the unmanned aerial vehicle through the visual SLAM module, and obtain the real-time positioning result P2 of the GNSS system, and calculate the spatial deviation ΔP of the positioning results P1 and P2;

[0027] The visual SLAM module constructs a local map and completes autonomous positioning through camera images and inertial measurement unit information, and does not rely on external electromagnetic signals; wherein a dynamic feature point screening algorithm is used in the local map construction process to eliminate suspected artificially laid interference feature points.

[0028] The positioning result P1 obtained by the unmanned aerial vehicle through the visual SLAM module is compared with the real-time positioning result P2 of the GNSS system, and the spatial coordinate error ΔP between the two is calculated to evaluate the position credibility. The visual SLAM constructs a local map and completes its own positioning through camera images and inertial navigation information, and does not rely on any external electromagnetic signal, and has strong anti-interference ability. The system presets a spatial deviation tolerance threshold P thr , when the deviation ΔP between the GNSS positioning and the SLAM positioning exceeds the threshold value, it is considered that the GNSS position may be tampered with or deceived.

[0029] The autonomous positioning result is obtained through the visual SLAM module, and the real-time coordinate error is calculated with the GNSS positioning, if the error exceeds the threshold P thr , it is determined that the GNSS positioning is not reliable, and the anti-deception judgment is enhanced from the spatial dimension.

[0030] S3, compare the time deviation ΔT with the preset time deviation threshold T thr , compare the spatial deviation ΔP with the preset spatial deviation threshold P thr , compare the spatial deviation ΔP with the preset spatial deviation threshold P thr , and determine whether the unmanned aerial vehicle is deceived according to the comparison result;

[0031] The determination of whether the unmanned aerial vehicle is deceived specifically includes:

[0032] If ΔT T thr , and ΔP P thr , determine as trusted navigation, otherwise determine as spoofed, wherein when ΔT ≥ T thr or ΔP ≥ P thr , further combine the magnitude of ΔT exceeding T thr , the magnitude of ΔP exceeding P thr and the duration of both to classify the spoofing level.

[0033] In one specific implementation, the specific level classification is as follows:

[0034] Low-level spoofing: single-dimension anomaly, i.e. only ΔT exceeds or only ΔP exceeds, and the exceeding magnitude < 50% threshold, duration < 5 seconds;

[0035] Medium-level spoofing: single-dimension anomaly and exceeding magnitude ≥ 50% threshold, or duration ≥ 5 seconds;

[0036] High-level spoofing: double-dimension anomaly, i.e. both ΔT and ΔP exceed, or either dimension anomaly exceeding magnitude ≥ 100% threshold.

[0037] After determining as spoofed, the system returns the flight data and positioning results to the ground control center through an encrypted channel, and performs log recording. The returned data includes ΔT, ΔP, GNSS raw observation values, SLAM key frame images and local crystal oscillator timestamps at the time of anomaly, uses AES-256 encryption algorithm to ensure transmission safety, and the log file is named in the format of "anomaly type + timestamp + device ID" and stored in read-only memory (ROM).

[0038] Specifically, the UAV spoofing determination is based on Table 1:

[0039] Table 1 Cross-validation logic table

[0040]

[0041] The time deviation threshold T thr and the spatial deviation threshold P thr are adjusted according to the task type of the UAV and the flight airspace situation, and a dynamic threshold model constructed by historical flight data is introduced in the adjustment process, so that the threshold is self-adaptively corrected according to the complexity of the flight environment. The dynamic threshold model uses a random forest algorithm, the input features include flight height, terrain complexity, GNSS signal-to-noise ratio (SNR) and historical spoofing event occurrence rate, and the output is the real-time corrected T thr and P thr , for example, in a high-rise dense area, Pthr can be temporarily relaxed by 20% to reduce the false judgment caused by SLAM positioning error.

[0042] S3 builds a cross-validation mechanism running in parallel with time deviation and space deviation, respectively based on the difference between GNSS time and local crystal oscillator time ΔT, and the spatial difference between GNSS and visual SLAM positioning results ΔP. This mechanism avoids the misjudgment of single-dimensional criteria, and improves the overall recognition accuracy and anti-interference robustness of the system.

[0043] S4 triggers the preset safety response mechanism according to the judgment result.

[0044] The safety response mechanism includes at least one of the following:

[0045] Hover, return, limit flight radius, lock control, power off, and self-destruction, and the safety response mechanism is adjusted according to the deception level. After recognizing the deception, the system will trigger the corresponding safety response mechanism according to the preset strategy level. The specific response strategy is as follows:

[0046] Low-level deception: execute "hover + warning", the UAV keeps hovering at the current position, sends sound and light alarm to the ground station through 4G / 5G link, and starts SLAM map reconstruction to verify the environment authenticity;

[0047] Medium-level deception: execute "return + lock control", the UAV immediately switches to SLAM autonomous navigation mode, returns to the nearest safe take-off point along the original flight line, and prohibits receiving flight path modification instructions from the ground station during the return;

[0048] High-level deception: execute "self-destruction + data backhaul", the self-destruction process includes: (1) cutting off the power system to make the UAV powerless gliding; (2) destroying the EEPROM chip storing sensitive data; (3) ejecting an emergency recovery capsule carrying a positioning beacon for subsequent debris recovery.

[0049] For low and medium level deception, such as space deviation tolerance but time anomaly, the system will prefer to take conservative strategies such as return, hover or limit flight radius, and send warning information to the ground station in real time; while for high-level deception, such as double anomaly or obvious rapid deviation trend, the self-destruction mechanism can be triggered, including disconnecting the power system, cutting off the power, shedding key components, etc., to ensure that the UAV is not induced to fly out of the safe area or captured by the enemy. According to the task type, flight air space situation, etc., the response level and execution action can be flexibly adjusted, so as to improve the system availability under the premise of maximum safety guarantee.

[0050] The method further comprises: when comparing and judging the time deviation ΔT and the space deviation ΔP, the change rates of ΔT and ΔP are introduced, the change amount ΔT' of ΔT and the change amount ΔP' of ΔP in unit time are calculated, and when ΔT' or ΔP' exceeds the preset change rate threshold, even if ΔT does not reach T thr or ΔP does not reach P thr, still determines as potential spoofing risk and triggers the early warning mechanism to identify rapidly changing spoofing behavior.

[0051] The method further comprises a multi-source data fusion verification step: in addition to the visual SLAM module and the GNSS system, barometer height data and geomagnetic sensor data are introduced as auxiliary verification sources, and when ΔT or ΔP exceeds the threshold value, secondary verification is performed in combination with the auxiliary verification source data to improve the accuracy of spoofing determination.

[0052] In order to explain the specific application of the embodiments of the application more specifically, three scenarios are used for specific description as follows:

[0053] Scenario one: GNSS time overrun triggers return

[0054] On a UAV performing a patrol task in a certain high-risk border area, a GNSS receiving module, a local crystal oscillator module and a visual SLAM positioning module are integrated. During flight, the system periodically collects local crystal oscillator time T1 and GNSS timestamp T2 for comparison, and the set time deviation threshold Tthr=10 ms; at the same time, the SLAM mapping and positioning result P1 is obtained through the camera and the IMU, and the position deviation is calculated with the GNSS position information P2, and the set spatial deviation threshold Pthr=5 m. When the UAV flies to a certain position, the system detects that the GNSS time and the local crystal oscillator time have a deviation of 17 ms, which exceeds Tthr, although the SLAM and GNSS positions do not exceed the spatial deviation threshold Pthr, but the system still determines that the current GNSS signal has a spoofing risk, triggers the return mode, and reports the related information to the ground control center, realizing the safety control closed loop. thr thr thr thr

[0055] Scenario two: spatial deviation overrun triggers emergency landing

[0056] On a UAV performing a path tracking task at the edge of a certain combat no-fly area, the system continuously enables the GNSS positioning and SLAM positioning modules for comparison. After flying to a certain area, the system detects that the spatial deviation ΔP between the SLAM positioning result and the GNSS positioning result reaches 12 m, which exceeds the preset spatial deviation threshold Pthr=5 m; at the same time, the GNSS system time and the local crystal oscillator time deviation is only 3 ms, which does not exceed the time threshold Tthr=10 ms. Although the GNSS time is normal, the system determines that the UAV is spoofed according to the spatial anomaly, immediately triggers the safety response mechanism, and controls the UAV to leave the current route and perform emergency landing operation, and limits the communication link, only retaining the abnormal log back transmission and position reporting function. The disposal process can effectively guarantee the flight control safety, while retaining the flight data for post-analysis and safety evaluation. ​​​​

[0057] Scenario three: dual anomaly recognition, triggering self-destruction program

[0058] The unmanned aerial vehicle performing a special monitoring task in a high alert level area at a border carries the system of the present application, and continuously runs GNSS positioning, visual SLAM positioning and time comparison modules. During the task flight, the system detects two anomalies at the same time: one is that the time deviation between the GNSS system time and the local crystal oscillator time reaches 26 milliseconds, exceeding the preset time deviation threshold T thr = 10 milliseconds; the other is that the spatial deviation between the GNSS positioning result and the SLAM positioning result reaches 9.8 meters, far exceeding the spatial deviation threshold P thr = 5 meters. The system determines that the current GNSS signal is suspected to encounter a high-level spoofing attack, there is a dual tampering behavior of time synchronization and position simulation, and it is extremely likely to be guided to the enemy control area. According to the preset high-level response strategy, the system immediately triggers the self-destruction program, interrupts the power system, cuts off the flight control connection, and ejects the key hardware modules to prevent the aircraft from crossing the border or being acquired by the enemy. Before self-destruction, the system returns the last set of flight data and positioning results to the ground control center through an encrypted channel for battlefield situation analysis and subsequent trace evidence. This scenario reflects the rapid response and task safety closed loop capability of the present application under high-risk conditions.

[0059] By adopting the embodiment of the present application, the following beneficial effects are achieved:

[0060] The present application proposes a complete technical system that integrates time and space dual-dimension judgment mechanism in the field of unmanned aerial vehicle anti-spoofing, overcoming the limitations brought by the current single judgment method which only relies on GNSS position anomaly detection or signal feature analysis. First, the present application integrates a local high-precision crystal oscillator at the unmanned aerial vehicle end to construct an independent time reference system, which can verify the credibility of GNSS time in real time, fundamentally improving the recognition ability of time synchronization type spoofing attack. Second, the visual SLAM module is introduced as a non-GNSS dependent autonomous positioning means, and the abnormality detection in the spatial dimension is realized through the coordinate comparison between GNSS position and SLAM position, which is particularly suitable for stable flight requirements in complex border environments and enemy electromagnetic interference scenarios. In addition, the present application constructs a standardized cross-judgment logic, allowing time anomaly and space anomaly as parallel inputs to trigger the spoofing recognition mechanism. Finally, the system also designs a safety response strategy based on judgment level, from early warning, flight restriction, return to self-destruction mechanism when necessary, with a complete safety strategy. Therefore, the present application is superior to the prior art in terms of spoofing recognition ability, safety response strategy, etc., and has significant innovation and practical value.

[0061] Device embodiment one

[0062] According to the embodiment of the present application, an electronic device is provided, which comprises:

[0063] a processor; and

[0064] a memory arranged to store computer-executable instructions that, when executed, cause the processor to perform the steps of the method embodiments described above.

[0065] Device embodiment two

[0066] According to an embodiment of the present application, a storage medium is provided for storing computer-executable instructions that, when executed, implement the steps of the method embodiments described above.

[0067] Finally, it should be noted that the above-described embodiments are merely intended to illustrate the technical solutions of the present application, and are not intended to limit the present application; even though the above-described embodiments of the present application have been described in detail, those skilled in the art should understand that they can still modify the technical solutions recorded in the above-described embodiments, or equivalently replace some or all of the technical features thereof; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A cross-validated drone anti-decoy method, characterized in that The method comprises: acquiring a local timestamp T1 based on a high-precision local crystal oscillator integrated inside the UAV, acquiring a standard time T2 provided by a GNSS navigation system, and calculating a time deviation ΔT between the local timestamp T1 and the standard time T2; acquiring a self-positioning result P1 of the UAV through a visual SLAM module, simultaneously acquiring a real-time positioning result P2 of the GNSS system, and calculating a spatial deviation ΔP between the positioning results P1 and P2; comparing the time deviation AT with a preset time deviation threshold T thr In comparison, the spatial deviation AP is compared with a preset spatial deviation threshold P thr In comparison, whether the UAV is decoyed is determined according to the comparison result. triggering a preset safety response mechanism according to the determination result; the determination of whether the UAV is decoyed specifically comprises: if ΔT T thr and ΔP P thr , then the navigation is trusted, otherwise it is spoofed, wherein when ΔT≥T thr or ΔP≥P thr , the spoofing level is determined by the magnitude of ΔT exceeding T thr , the magnitude of ΔP exceeding P thr , and the duration of both. The time deviation threshold T thr And the space deviation threshold P thr According to the task type of the unmanned aerial vehicle and the flight airspace situation, and a dynamic threshold model constructed by introducing historical flight data in the adjustment process, the threshold is self-adaptively corrected according to the complexity of the flight environment. In the comparison and determination of the time deviation ΔT and the space deviation ΔP, the change rates of the two are introduced for monitoring, the change amount ΔT' of ΔT and the change amount ΔP' of ΔP in a unit of time are calculated, and when ΔT' or ΔP' exceeds a preset change rate threshold, even if ΔT does not reach T thr or ΔP does not reach P thr , it is still determined as a potential spoofing risk and a warning mechanism is triggered to identify spoofing behavior.

2. The method of claim 1, wherein, the safety response mechanism comprises at least one of the following: hovering, returning, limiting a flight radius, locking control, power-off, and self-destruction, and the safety response mechanism is adjusted according to a decoy level.

3. The method of claim 1, wherein, The visual SLAM module constructs a local map and completes self-positioning through camera images and inertial measurement unit information, and does not rely on external electromagnetic signals; wherein a dynamic feature point screening algorithm is used in the local map construction process to eliminate suspected artificially laid interference feature points.

4. The method of claim 1, wherein, After determining that the UAV is decoyed, the system returns flight data and positioning results to a ground control center through an encrypted channel, and performs log recording.

5. The method of claim 1, wherein, The method further comprises a multi-source data fusion verification step: in addition to the visual SLAM module and the GNSS system, barometer height data and geomagnetic sensor data are introduced as auxiliary verification sources, when ΔT or ΔP exceeds a threshold value, secondary verification is performed in combination with the data of the auxiliary verification sources, so as to improve the accuracy of decoy determination.

6. An electronic device comprising: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the steps of the cross-verified UAV anti-decoy method of any one of claims 1-5.

7. A storage medium for storing computer executable instructions that, when executed, implement the steps of the cross-verified UAV anti-decoy method of any one of claims 1-5.

Images