A shipboard unmanned aerial vehicle autonomous health monitoring system and method

Abstract

The application belongs to the technical field of unmanned aerial vehicle detection, and discloses a shipborne unmanned aerial vehicle autonomous integrity monitoring system and method, which comprises a flight beacon simulator, a satellite signal simulator, a transponder comprehensive tester, a frequency hopping and spread spectrum comprehensive tester and a digital transmission and controller; the digital transmission and controller is connected with the flight beacon simulator, the satellite signal simulator, the transponder comprehensive tester, the frequency hopping and spread spectrum comprehensive tester and a to-be-tested unmanned aerial vehicle respectively; the flight beacon simulator is further connected with the satellite signal simulator; the transponder comprehensive tester and the frequency hopping and spread spectrum comprehensive tester are both connected with the to-be-tested unmanned aerial vehicle. The application solves the problem that the existing technology cannot perform autonomous integrity monitoring on the packaged unmanned aerial vehicle receiver, and has the characteristics that the autonomous integrity monitoring can be performed on the combined navigation positioning unmanned aerial vehicle receiver.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) detection technology, and more specifically, to a system and method for monitoring the autonomous integrity of shipborne UAVs. Background Technology

[0002] Shipborne unmanned aerial vehicles (UAVs) are primarily used for rapid monitoring of marine disasters and maritime targets, as well as for the calibration of radar equipment on land and at sea. Configuring shipborne UAVs improves navigation efficiency and ensures navigation safety. The autonomous integrity monitoring function in shipborne UAVs is a technology that utilizes various beacon signals and satellite signals to detect and eliminate faulty transmitters. Its purpose is to detect faulty transmitters during navigation, ensuring the validity of navigation and positioning accuracy readings. Autonomous integrity monitoring technology possesses numerous advantages, including rapid response to transmitter malfunctions, complete autonomy, no external intervention required, low cost, and global availability. This technology is particularly important for life-critical systems (such as aviation or maritime navigation). With the increasing prevalence and improvement of integrated navigation systems, the coverage and application scope of this comprehensive autonomous integrity monitoring technology are gradually expanding.

[0003] The prior art discloses an advanced receiver autonomous integrity monitoring simulation evaluation method and device, which reads configuration files, GNSS satellite data and ISM message data; through the parsed GNSS satellite data and ISM message parameters, it simulates the advanced receiver autonomous integrity monitoring (ARAIM) algorithm, calculates the aircraft's protection level and positioning error; based on the calculation results, it evaluates the accuracy index test value, availability index test value and integrity index test value of the GNSS system.

[0004] However, existing technologies have the problem of failing to effectively detect the autonomous integrity of packaged drone receivers. Therefore, how to invent a system and method that can monitor the inability of packaged receivers to perform autonomous integrity monitoring is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] To address the problem that existing technologies cannot effectively monitor the autonomous integrity of packaged UAV receivers, this invention provides a shipborne UAV autonomous integrity monitoring system and method, which is capable of providing effective detection of autonomous integrity monitoring for integrated navigation and positioning UAV receivers.

[0006] To achieve the above-mentioned objectives of this invention, the technical solution adopted is as follows:

[0007] A shipborne unmanned aerial vehicle (UAV) autonomous integrity monitoring system includes a flight beacon simulator, a satellite signal simulator, a transponder integrated tester, a hopping spread spectrum integrated tester, and a digital transmission and controller.

[0008] The digital transmission and controller are respectively connected to the flight beacon simulator, satellite signal simulator, transponder integrated tester, hopping spread spectrum integrated tester, and UAV under test; the flight beacon simulator is also connected to the satellite signal simulator; the transponder integrated tester and hopping spread spectrum integrated tester are both connected to the UAV under test;

[0009] The aforementioned flight beacon simulator is used to simulate the beacon of a drone;

[0010] The satellite signal simulator is used to simulate navigation satellite signals;

[0011] The transponder integrated tester is used to detect whether the relative position meets the set technical specifications, set the deviation of the simulated satellite signal, set the deviation of the simulated beacon, and change the amplitude of the simulated beacon;

[0012] The aforementioned hopping spread spectrum integrated tester is used to detect whether the real-time transmission and throughput of the information data output by the UAV under test meet the set technical requirements, to spread spectrum modulation and carrier modulation of the simulated beacon and forward it to the UAV under test, to detect the anti-interference performance of the UAV under test, and to change the frequency of the simulated beacon.

[0013] The digital transmission and controller is used to store detection results, generate detection datasets, set the total number of reference sources for analog beacons and satellite signals, and control data transmission between devices.

[0014] A method for monitoring the autonomous integrity of a shipborne unmanned aerial vehicle (UAV) includes the following specific steps:

[0015] S1: Simulate the beacon of the drone and input the simulated beacon into the drone under test to obtain the information data output by the drone under test; check whether the output information data meets the set technical indicators. If not, repeat step S1; if it meets the indicators, proceed to step S2.

[0016] S2: Check whether the real-time transmission and throughput of the information data output by the UAV under test in S1 meet the set technical requirements; if not, return to step S1; if yes, spread spectrum modulation and carrier modulation of the simulated beacon are performed and forwarded to the UAV under test to check the anti-interference performance of the UAV under test.

[0017] S3: Set the total number of reference sources for simulated beacons and satellite signals; simulate the beacon points and navigation satellite signals of the UAV and input the simulated beacons and navigation satellite signals into the UAV under test; detect the UAV under test's performance in rejecting anomalies in satellite signals by setting the deviation of the simulated satellite signals;

[0018] S4: Set the total number of reference sources for simulated beacons and satellite signals; simulate the beacon points and navigation satellite signals of the UAV and input the simulated beacons and navigation satellite signals into the UAV under test; detect the UAV under test's anomaly rejection performance for simulated beacons by setting the deviation of the simulated beacons;

[0019] S5: Based on step S4, change the amplitude and frequency of the simulated beacon to detect the alarm mechanism of the UAV under test;

[0020] S6: Store the detection results of steps S2-S5 and generate a detection dataset; perform autonomous integrity monitoring based on the detection dataset.

[0021] Preferably, in step S1, simulating the UAV's beacon and inputting the simulated beacon into the UAV under test to obtain the information data output by the UAV under test specifically involves: simulating the UAV's beacon, including a heading beacon and a pointing beacon, with beacon point k not less than 3; setting time difference and frequency difference parameters, performing modulation and encoding, and combining this with the simulated beacon input to the UAV under test to obtain the relative position information output by the UAV under test.

[0022]

[0023] Where, θ i ‘’ The relative angle θ between the UAV under test and the i-th beacon. i Let be the relative angle measured by the i-th beacon of the UAV under test.

[0024] Furthermore, in step S1, detecting whether the relative position meets the set technical indicators specifically involves:

[0025] Check if the relative position has been obtained;

[0026] Calculate the distance and velocity values ​​of the UAV under test based on the relative position, time difference, and frequency difference; check whether the deviations of the distance and velocity values ​​from the set standard values ​​meet the technical specifications.

[0027] Furthermore, in step S2, the simulated beacon is spread-spectrum modulated, carrier modulated, and forwarded to the UAV under test to detect its anti-interference performance. Specifically, a modulated simulated beacon signal with carrier suppression greater than 60dB, upper / lower sideband suppression greater than 50dB, upper / lower sideband output relative change less than ±0.2dB, carrier / sideband output relative change less than ±0.2dB, and radio frequency phase relative change less than ±2° is sent to the UAV under test. When detecting the anti-interference performance of the UAV under test, the carrier suppression, upper / lower sideband suppression, upper / lower sideband output relative change, carrier / sideband output relative change, radio frequency phase relative change, and alarm effect are changed sequentially.

[0028] Further, in the described step S3, the total number of reference sources for simulated beacons and satellite signals is set; the beacon points of the UAV and navigation satellite signals are simulated, and the simulated beacons and simulated navigation satellite signals are input to the UAV under test; the anomaly rejection performance of the UAV under test for satellite signals is detected by setting the deviation of the simulated satellite signals, which is specifically:

[0029] Set the total number of reference sources for simulated beacons and satellite signals to 5 to 12, set the number of valid satellites to no less than 5, set the detection time as Xmin, preset that each simulated satellite, numbered in ascending order, starts to add a phase difference of (t-15)*α / f at the 15th second within each one-minute interval, where 15s<t≤45s, f is the frequency, α≥1, and after maintaining this time delay for 10s, it jumps back to the initial value; sequentially change the error magnitude of the simulated navigation satellite signal of each simulated satellite, and save the corresponding information data output by the UAV under test; detect whether the alarm signal is activated and whether the error data is rejected when the detection error exceeds the error threshold, and detect whether the alarm signal is activated and whether the error data is rejected when the error does not exceed the error threshold, so as to detect whether the UAV under test can identify and reject the abnormal simulated navigation satellite signal.

[0030] Further, in the described step S4, the total number of reference sources for simulated beacons and satellite signals is set; the beacon points of the UAV and navigation satellite signals are simulated, and the simulated beacons and simulated navigation satellite signals are input to the UAV under test; the anomaly rejection performance of the UAV under test for simulated beacons is detected by setting the deviation of the simulated beacons, which is specifically:

[0031] Set the total number of reference sources for simulated beacons and satellite signals to 5 to 12, set the number of valid satellites to less than 5, and the point position of the simulated beacon is k; set the carrier frequency deviation to be less than 2E-5, the interval between each carrier frequency to be in the range of 5kHz to 14kHz, the carrier harmonic component to be less than 50dBc, the clutter component to be less than 60dBc, the vertical polarization component to be less than int(0.016DDM / a1), where a1 is the device category and a1=1~3, the carrier sideband signal stability to be less than 0.005DDM, and the pure sideband signal carrier suppression to be greater than 30dB; input the simulated beacons and simulated navigation satellite signals into the UAV under test, and when the UAV under test performs integrated navigation by fusing the simulated beacons and the simulated navigation satellite signals, sequentially change the carrier frequency deviation, carrier frequency interval, carrier harmonic component and clutter component, vertical polarization component, and carrier sideband signal stability to apply interference; detect whether the alarm signal is activated and whether the abnormal beacon is rejected when the interference exceeds the interference threshold, and detect whether the alarm signal is activated and whether the abnormal beacon is rejected when the interference does not exceed the interference threshold, so as to detect whether the UAV under test can identify and reject the abnormal beacon point position.

[0032] Further, in the described step S5, the simulated beacon is changed on the basis of step S4 to detect the alarm mechanism of the UAV under test, which is specifically:

[0033] The transmission amplitude A, pulse repetition period T, pulse width τ, carrier frequency f0, frequency modulation slope μ, and noise combination term m(t) of the simulated beacon are changed; the synthesized beacon signal s transmitted to the UAV under test is then analyzed. c (t): Given nT≤t≤nT+τ, n=0,1,2..., the satellite signal positioning accuracy ε is obtained. sat beacon positioning accuracy ε ext-k Accuracy ε of fusion positioning c Compared with accuracy Check whether the alarm is correct and whether fault detection and troubleshooting can be carried out.

[0034] Furthermore, when checking whether an alarm is correctly triggered and whether fault detection and troubleshooting can be performed, an accuracy attenuation factor H is obtained by fusing positioning error and ranging error. H decreases normally with the increase of the reference signal source, and a reweighted statistical quantity is selected. The system uses a reference signal source for navigation and positioning, and simultaneously filters faulty reference signal sources and compares them with the fault diagnosis results of the UAV under test; it also checks the alarm effect of the UAV under test and calculates the results of false alarms or missed alarms under this condition.

[0035] Furthermore, in step S6, a detection dataset is generated based on the detection results of steps S2-S5; autonomous integrity monitoring is performed based on the detection dataset, specifically by sorting the detection results of steps S2-S5 by time to generate a detection dataset; and during subsequent autonomous integrity monitoring, the measurement results are compared with the dataset to obtain the detection results.

[0036] The beneficial effects of this invention are as follows:

[0037] This invention addresses the testing requirements of critical functions of shipborne unmanned aerial vehicles (UAVs) by designing an autonomous integrity monitoring system. The system includes a flight beacon simulator, a satellite signal simulator, a transponder integrated tester, a hopping spread spectrum integrated tester, and a digital transmission and controller. It enables functional effectiveness testing in a simulated field environment, eliminating the need for testing at a specific actual location. The system allows for the design of required channel states based on customer usage scenarios, broadening its applicability. It also reduces the acquisition time of the digital transmission and controller and minimizes the impact of standard time-domain resolution during calibration. The transponder integrated tester's independent encoding / decoding and automatic Doppler frequency compensation functions prevent the Doppler effect accompanying frequency changes from affecting the test results of the UAV under test. Therefore, this invention solves the problem of existing technologies being unable to perform autonomous integrity monitoring and effectiveness testing on packaged UAV receivers, and it also has the characteristic of providing corresponding autonomous integrity monitoring and effectiveness testing for integrated navigation and positioning UAV receivers. Attached Figure Description

[0038] Figure 1 This is a system block diagram of an autonomous integrity monitoring system for shipborne unmanned aerial vehicles (UAVs) according to the present invention.

[0039] Figure 2 This is a flowchart illustrating the autonomous integrity monitoring method for shipborne unmanned aerial vehicles (UAVs) according to the present invention. Detailed Implementation

[0040] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0041] Example 1

[0042] like Figure 1 As shown, a shipborne unmanned aerial vehicle (UAV) autonomous integrity monitoring system includes a flight beacon simulator, a satellite signal simulator, a transponder integrated tester, a hopping spread spectrum integrated tester, and a digital transmission and controller.

[0043] The digital transmission and controller are respectively connected to the flight beacon simulator, satellite signal simulator, transponder integrated tester, hopping spread spectrum integrated tester, and UAV under test; the flight beacon simulator is also connected to the satellite signal simulator; the transponder integrated tester and hopping spread spectrum integrated tester are both connected to the UAV under test;

[0044] The aforementioned flight beacon simulator is used to simulate the beacon of a drone;

[0045] The satellite signal simulator is used to simulate navigation satellite signals;

[0046] The transponder integrated tester is used to detect whether the relative position meets the set technical specifications, set the deviation of the simulated satellite signal, set the deviation of the simulated beacon, and change the amplitude of the simulated beacon;

[0047] The aforementioned hopping spread spectrum integrated tester is used to detect whether the real-time transmission and throughput of the information data output by the UAV under test meet the set technical requirements, to spread spectrum modulation and carrier modulation of the simulated beacon and forward it to the UAV under test, to detect the anti-interference performance of the UAV under test, and to change the frequency of the simulated beacon.

[0048] The digital transmission and controller is used to store detection results, generate detection datasets, set the total number of reference sources for analog beacons and satellite signals, and control data transmission between devices.

[0049] In this embodiment, when monitoring the autonomous integrity of a shipborne UAV, the digital transmission and controller sets the flight beacon simulator to output corresponding beacon signals, including a heading beacon and a guide beacon. The beacon point k is not less than 3. The relative position measurement result of the UAV under test is as follows: θ i θ is the relative angle set between the UAV under test and the i-th beacon. i Let be the relative angle measured by the i-th beacon of the UAV under test.

[0050] In this embodiment, the transponder integrated tester and the hopping spread spectrum integrated tester do not perform additional signal processing during the communication process with the UAV under test. They check whether the UAV under test obtains the relative position result under normal conditions and whether the deviation between the relative position and the flight beacon simulator setting value meets the technical specifications.

[0051] In this embodiment, the transponder integrated tester sets time difference and frequency difference parameters according to the signal output by the flight beacon simulator, performs modulation and encoding, and outputs the signal to the UAV under test. It then checks whether the deviation between the distance measurement value and speed measurement value calculated by the UAV under test based on the time difference and frequency difference and the standard value provided by the transponder integrated tester meets the technical specifications.

[0052] In this embodiment, the real-time performance and throughput of the information data transmitted by the hopping spread spectrum integrated tester to the UAV under test are checked to ensure they meet the technical specifications. After the signal sent by the hopping spread spectrum integrated tester's flight beacon simulator completes spread spectrum modulation, carrier modulation, and forwarding, the UAV under test should maintain normal operation. Based on test data obtained from previous experiments, the carrier suppression of the signal sent by the hopping spread spectrum integrated tester to the UAV under test is greater than 60dB, the upper / lower sideband suppression is greater than 50dB, the relative change in upper / lower sideband output is less than ±0.2dB, the relative change in carrier / sideband output is less than ±0.2dB, and the relative change in RF phase is less than ±2°. When checking the anti-interference performance of the UAV under test, the carrier suppression, upper / lower sideband suppression, relative change in upper / lower sideband output, relative change in carrier / sideband output, and relative change in RF phase are changed sequentially to complete the scrambling step. The nominal alarm effect of the UAV under test is checked, and the results of false alarms or missed alarms under this condition are calculated.

[0053] In this embodiment, the digital transmission and controller sets the total number of reference sources m for the flight beacon simulator and satellite signal simulator to a value of 5 to 12. The satellite signal simulator sets the number of effective satellites to be no less than 5. The detection time is set to X min. The satellite signal simulator presets the phase difference (t-15)*α / f for each satellite, starting from the 15th second of each minute interval, with 15s < t ≤ 45s and α ≥ 1. This delay is maintained for 10s before jumping back to the initial value. Extensive preliminary testing shows that the phase stabilization time is between 10s and 15s, resulting in the highest testing efficiency. Various errors are changed sequentially on a single satellite, and the output data is saved. The relationship between the extracted errors and the threshold and alarm of the sample under test is calibrated. The system detects whether the UAV under test identifies the abnormal satellite and removes it at a certain time point, after which the positioning accuracy of the sample under test returns to normal.

[0054] In this embodiment, the digital transmission and controller sets the total number of reference sources m for the flight beacon simulator and satellite signal simulator to a value of 5 to 12, the number of effective satellites for the satellite signal simulator to be less than 5, and the beacon point for the flight beacon simulator to be k. The flight beacon simulator sets the carrier frequency deviation to be less than 2E-5, the carrier frequency interval to be between 5kHz and 14kHz, the carrier harmonic component to be less than 50dBc, the clutter component to be less than 60dBc, the vertical polarization component to be less than int(0.016DDM / a1), where a1 is the device category a1 = 1 to 3, the carrier sideband signal stability to be less than 0.005DDM, and the pure sideband signal carrier suppression to be greater than 30dB. When checking the beacon and satellite signal fusion navigation performance of the UAV under test, the carrier frequency deviation, carrier frequency interval, carrier harmonic and clutter components, vertical polarization component, and carrier sideband signal stability are changed sequentially to complete the interference step. The system checks whether the UAV under test identifies the abnormal beacon point and removes the point at a certain time point, after which the positioning accuracy of the sample under test returns to normal.

[0055] In this embodiment, the digital transmission and controller sets the transponder integrated tester to change the transmission amplitude A, pulse repetition period T, and pulse width τ of the flight beacon simulator output signal; the digital transmission and controller sets the hopping spread spectrum integrated tester to change the carrier frequency f0, frequency modulation slope μ, and noise combination term m(t) of the flight beacon simulator output signal. The beacon synthesized signal transmitted to the UAV under test is as follows: For nT≤t≤nT+τ, n=0,1,2..., the digital transmission and controller obtains the satellite signal positioning accuracy ε. sat beacon positioning accuracy ε ext-k Accuracy ε of fusion positioning c Compared with accuracy To verify whether alarms are correctly triggered (false alarm rate and missed alarm rate are below the threshold) and whether fault detection and troubleshooting can be performed, at least six reference signal sources are required for measurement. However, based on the specific geometric location of the reference signal sources, more measurements are usually needed, typically seven to twelve. The accuracy attenuation factor H is obtained by fusing positioning error and ranging error. H normally decreases with the increase of reference signal sources; a reweighted statistic is then selected. The system uses a reference signal source for navigation and positioning, and simultaneously filters faulty reference signal sources and compares them with the fault diagnosis results of the UAV under test. It also checks the nominal alarm effect of the UAV under test and calculates the results of false alarms or missed alarms under this condition.

[0056] In this embodiment, the digital transmission and controller (DTC) generates a dataset based on the data recorded during the test cycle, sorted by time, and stores it named after the UAV under test number and the test date. During the test cycle, the parameter data of the UAV under test are compared with the measured values ​​of the DTC by sorting by time. Subsequent test cycles directly compare the dataset with the dataset to shorten data processing time.

[0057] Example 2

[0058] like Figure 2 As shown, a method for monitoring the autonomous integrity of a shipborne unmanned aerial vehicle (UAV) includes the following specific steps:

[0059] S1: Simulate the beacon of the drone and input the simulated beacon into the drone under test to obtain the information data output by the drone under test; check whether the output information data meets the set technical indicators. If not, repeat step S1; if it meets the indicators, proceed to step S2.

[0060] S2: Check whether the real-time transmission and throughput of the information data output by the UAV under test in S1 meet the set technical requirements; if not, return to step S1; if yes, spread spectrum modulation and carrier modulation of the simulated beacon are performed and forwarded to the UAV under test to check the anti-interference performance of the UAV under test.

[0061] S3: Set the total number of reference sources for simulated beacons and satellite signals; simulate the beacon points and navigation satellite signals of the UAV and input the simulated beacons and navigation satellite signals into the UAV under test; detect the UAV under test's performance in rejecting anomalies in satellite signals by setting the deviation of the simulated satellite signals;

[0062] S4: Set the total number of reference sources for simulated beacons and satellite signals; simulate the beacon points and navigation satellite signals of the UAV and input the simulated beacons and navigation satellite signals into the UAV under test; detect the UAV under test's anomaly rejection performance for simulated beacons by setting the deviation of the simulated beacons;

[0063] S5: Based on step S4, change the amplitude and frequency of the simulated beacon to detect the alarm mechanism of the UAV under test;

[0064] S6: Store the detection results of steps S2-S5 and generate a detection dataset; perform autonomous integrity monitoring based on the detection dataset.

[0065] Example 3

[0066] More specifically, in one embodiment, in step S1, simulating the UAV's beacon and inputting the simulated beacon into the UAV under test to obtain the information data output by the UAV under test, specifically: simulating the UAV's beacon, including a heading beacon and a pointing beacon, with beacon point k not less than 3; setting time difference and frequency difference parameters, performing modulation and encoding, and combining this with the simulated beacon input to the UAV under test to obtain the relative position information output by the UAV under test.

[0067]

[0068] Where, θ i ‘’ The relative angle θ between the UAV under test and the i-th beacon. i Let be the relative angle measured by the i-th beacon of the UAV under test.

[0069] In one specific embodiment, step S1, detecting whether the relative position meets the set technical indicators, specifically involves:

[0070] Check if the relative position has been obtained;

[0071] Calculate the distance and velocity values ​​of the UAV under test based on the relative position, time difference, and frequency difference; check whether the deviations of the distance and velocity values ​​from the set standard values ​​meet the technical specifications.

[0072] In one specific embodiment, in step S2, the simulated beacon is spread-spectrum modulated, carrier modulated, and forwarded to the UAV under test to detect the anti-interference performance of the UAV under test. Specifically, the simulated beacon signal with a carrier suppression greater than 60dB, upper / lower sideband suppression greater than 50dB, upper / lower sideband output relative change less than ±0.2dB, carrier / sideband output relative change less than ±0.2dB, and radio frequency phase relative change less than ±2° is sent to the UAV under test. When detecting the anti-interference performance of the UAV under test, the carrier suppression, upper / lower sideband suppression, upper / lower sideband output relative change, carrier / sideband output relative change, radio frequency phase relative change, and alarm effect are changed sequentially.

[0073] In one specific embodiment, step S3 involves setting the total number of reference sources for simulated beacons and satellite signals; simulating the beacon points and navigation satellite signals of the UAV and inputting the simulated beacons and navigation satellite signals into the UAV under test; and detecting the UAV under test's anomaly removal performance for satellite signals by setting the deviation of the simulated satellite signals, specifically as follows:

[0074] Set the total number of reference sources for simulated beacons and satellite signals to 5 to 12, set the number of valid satellites to be no less than 5, set the detection time Xmin, preset that the phase difference (t-15)*α / f starts to increase at the 15th second within each minute interval for each simulated satellite with numbers in ascending order, where 15s<t≤45s, f is the frequency, α≥1, the phase difference is maintained for 10s and then jumps back to the initial value after the corresponding time delay; sequentially change the error magnitude of the simulated navigation satellite signal of each simulated satellite, and save the corresponding information data output by the drone under test; detect whether the alarm signal is activated when the error exceeds the error threshold, whether the error data is rejected, and whether the alarm signal is activated and whether the error data is rejected when the error does not exceed the error threshold, so as to detect whether the drone under test can identify and reject the abnormal simulated navigation satellite signal.

[0075] In a specific embodiment, in the described step S4, the total number of reference sources for simulated beacons and satellite signals is set; the beacon points and navigation satellite signals of the drone are simulated, and the simulated beacons and simulated navigation satellite signals are input into the drone under test; the anomaly rejection performance of the drone under test for the simulated beacons is detected by setting the deviation of the simulated beacons, which is specifically as follows:

[0076] Set the total number of reference sources for simulated beacons and satellite signals to 5 to 12, set the number of valid satellites to be less than 5, and the point position of the simulated beacon is k; set the carrier frequency deviation to be less than 2E-5, the interval range of each carrier frequency is between 5kHz and 14kHz, the carrier harmonic component is less than 50dBc, the clutter component is less than 60dBc, the vertical polarization component is less than int(0.016DDM / a1), a1 is the device category, a1=1 to 3, the carrier sideband signal stability is less than 0.005DDM, and the pure sideband signal carrier suppression is greater than 30dB; input the simulated beacons and simulated navigation satellite signals into the drone under test, when the drone under test performs integrated navigation through the simulated beacons and the simulated navigation satellite signals, sequentially change the carrier frequency deviation, carrier frequency interval, carrier harmonic component and clutter component, vertical polarization component, and carrier sideband signal stability to generate interference; detect whether the alarm signal is activated when the interference exceeds the interference threshold, whether the abnormal beacon is rejected, and whether the alarm signal is activated and whether the abnormal beacon is rejected when the interference does not exceed the interference threshold, so as to detect whether the drone under test can identify and reject the abnormal beacon point position.

[0077] In a specific embodiment, in the described step S5, the simulated beacon is changed on the basis of step S4 to detect the alarm mechanism of the drone under test, which is specifically as follows:

[0078] Change the transmission amplitude A, pulse repetition period T, pulse width τ, carrier frequency f0, frequency modulation slope μ, and noise combination term m(t) of the simulated beacon; the beacon composite signal s transmitted to the drone under test c (t): Given nT≤t≤nT+τ, n=0,1,2..., the satellite signal positioning accuracy ε is obtained. sat beacon positioning accuracy ε ext-k Accuracy ε of fusion positioning c Compared with accuracy Check whether the alarm is correct and whether fault detection and troubleshooting can be carried out.

[0079] In one specific embodiment, at least six reference signal sources are required for measurement when detecting whether an alarm is correct and whether fault detection and troubleshooting can be performed. However, based on the specific geometric location of the reference signal sources, more measurements are usually needed. Therefore, this invention uses an accuracy attenuation factor H obtained by fusing positioning error and ranging error. H decreases normally with the increase of reference signal sources, and a reweighted statistical quantity is selected. The system uses a reference signal source for navigation and positioning, and simultaneously filters faulty reference signal sources and compares them with the fault diagnosis results of the UAV under test; it also checks the alarm effect of the UAV under test and calculates the results of false alarms or missed alarms under this condition.

[0080] In one specific embodiment, in step S6, a detection dataset is generated based on the detection results of steps S2-S5; autonomous integrity monitoring is performed based on the detection dataset, specifically by sorting the detection results of steps S2-S5 by time to generate a detection dataset; when performing autonomous integrity monitoring subsequently, the measurement results are compared with the dataset to obtain the detection results.

[0081] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A shipborne unmanned aerial vehicle (UAV) autonomous integrity monitoring system, characterized in that: This includes flight beacon simulators, satellite signal simulators, transponder integrated testers, hopping spread spectrum integrated testers, and digital transmission and control systems. The digital transmission and controller are respectively connected to the flight beacon simulator, satellite signal simulator, transponder integrated tester, hopping spread spectrum integrated tester, and UAV under test; the flight beacon simulator is also connected to the satellite signal simulator; the transponder integrated tester and the hopping spread spectrum integrated tester are both connected to the UAV under test. The aforementioned flight beacon simulator is used to simulate the beacon of a drone; The satellite signal simulator is used to simulate navigation satellite signals; The transponder integrated tester is used to detect whether the relative position meets the set technical specifications, set the deviation of the simulated satellite signal, set the deviation of the simulated beacon, and change the amplitude of the simulated beacon; The aforementioned hopping spread spectrum integrated tester is used to detect whether the real-time transmission and throughput of the information data output by the UAV under test meet the set technical requirements, to spread spectrum modulation and carrier modulation of the simulated beacon and forward it to the UAV under test, to detect the anti-interference performance of the UAV under test, and to change the frequency of the simulated beacon. The digital transmission and controller is used to store detection results, generate detection datasets, set the total number of reference sources for analog beacons and satellite signals, and control data transmission between devices.

2. A method for monitoring the autonomous integrity of a shipborne unmanned aerial vehicle (UAV), characterized in that: The specific steps include the following: S1: Simulate the beacon of the drone and input the simulated beacon into the drone under test to obtain the information data output by the drone under test; check whether the output information data meets the set technical indicators. If not, repeat step S1; if it meets the indicators, proceed to step S2. S2: Check whether the real-time transmission and throughput of the information data output by the UAV under test in S1 meet the set technical requirements; if not, return to step S1; if yes, spread spectrum modulation and carrier modulation of the simulated beacon are performed and forwarded to the UAV under test to check the anti-interference performance of the UAV under test. S3: Set the total number of reference sources for simulated beacons and satellite signals; simulate the beacon points and navigation satellite signals of the UAV and input the simulated beacon and navigation satellite signals into the UAV under test; The performance of the UAV under test in eliminating anomalies in satellite signals was tested by setting deviations in simulated satellite signals. S4: Set the total number of reference sources for simulated beacons and satellite signals; simulate the beacon points and navigation satellite signals of the UAV and input the simulated beacon and navigation satellite signals into the UAV under test; The performance of the UAV under test in eliminating anomalies of the simulated beacon was tested by setting deviations of the simulated beacon. S5: Based on step S4, change the amplitude and frequency of the simulated beacon to detect the alarm mechanism of the UAV under test; S6: Store the detection results of steps S2-S5 and generate a detection dataset; perform autonomous integrity monitoring based on the detection dataset.

3. The method for monitoring the autonomous integrity of shipborne unmanned aerial vehicles according to claim 2, characterized in that: In step S1, a simulated UAV beacon is used and input into the UAV under test to obtain the information data output by the UAV under test. Specifically, the simulated UAV beacon includes a heading beacon and a pointing beacon, with a beacon point k of no less than 3. Time difference and frequency difference parameters are set, and modulation and encoding are performed. Combined with the simulated beacon input to the UAV under test, the relative position information output by the UAV under test is obtained. in, The relative angle set for the UAV under test to the i-th beacon. The relative angle is measured by the i-th beacon of the UAV under test.

4. The method for monitoring the autonomous integrity of shipborne unmanned aerial vehicles according to claim 3, characterized in that: In step S1, detecting whether the relative position meets the set technical specifications specifically involves: Check if the relative position has been obtained; Calculate the distance and velocity values ​​of the UAV under test based on the relative position, time difference, and frequency difference; check whether the deviations of the distance and velocity values ​​from the set standard values ​​meet the technical specifications.

5. The method for monitoring the autonomous integrity of a shipborne unmanned aerial vehicle according to claim 2, characterized in that: In step S2, the simulated beacon is spread-spectrum modulated, carrier modulated, and forwarded to the UAV under test to detect its anti-interference performance. Specifically: A modulated analog beacon signal with carrier suppression greater than 60dB, upper / lower sideband suppression greater than 50dB, upper / lower sideband output relative variation within ±0.2dB, carrier / sideband output relative variation within ±0.2dB, and radio frequency phase relative variation within ±2° is sent to the UAV under test. When testing the anti-interference performance of the UAV under test, the carrier suppression, upper / lower sideband suppression, upper / lower sideband output relative variation, carrier / sideband output relative variation, radio frequency phase relative variation, and alarm effect are changed sequentially.

6. The method for monitoring the autonomous integrity of a shipborne unmanned aerial vehicle according to claim 2, characterized in that: In step S3, the total number of reference sources for simulated beacons and satellite signals is set; the beacon points and navigation satellite signals of the simulated UAV are simulated and input into the UAV under test; the performance of the UAV under test in eliminating anomalies in satellite signals is detected by setting the deviation of the simulated satellite signals, specifically: Set the total number of reference sources for simulated beacons and satellite signals to 5 to 12, set the number of valid satellites to be no less than 5, set the detection time Xmin, pre-set the number of each simulated satellite in ascending order, and start to increase the phase difference (t-15) at the 15th second within each one-minute interval α / f, where 15s<t ≤ 45s, f is the frequency, α ≥ 1, and jump to the initial value after lasting for 10s; sequentially change the error magnitude of the simulated navigation satellite signal of each simulated satellite, and save the corresponding information data output by the drone under test; When the detection error exceeds the error threshold, whether the alarm signal is activated and whether the error data is removed; when the error does not exceed the error threshold, whether the alarm signal is activated and whether the error data is removed, thereby detecting whether the UAV under test can identify and remove abnormal simulated navigation satellite signals.

7. The method for monitoring the autonomous integrity of a shipborne unmanned aerial vehicle according to claim 2, characterized in that: In step S4, the total number of reference sources for simulated beacons and satellite signals is set; the beacon points and navigation satellite signals of the simulated UAV are simulated and input into the UAV under test; the anomaly rejection performance of the UAV under test for simulated beacons is detected by setting the deviation of the simulated beacons, specifically as follows: Set the total number of reference sources for the simulated beacon and satellite signals to 5-12, the number of effective satellites to less than 5, and the position of the simulated beacon to k. Set the carrier frequency deviation to less than 2E-5, the carrier frequency interval to be between 5kHz and 14kHz, the carrier harmonic component to less than 50dBc, the clutter component to less than 60dBc, the vertical polarization component to less than int(0.016DDM / a1), where a1 is the equipment category and a1=1~3, the carrier sideband signal stability to less than 0.005DDM, and the pure sideband signal carrier suppression to greater than 30dB. Input the simulated beacon and simulated navigation satellite signals into the UAV under test. When the UAV under test is navigating by fusing the simulated beacon and simulated navigation satellite signals, sequentially change the carrier frequency deviation, carrier frequency interval, carrier harmonic and clutter components, vertical polarization component, and carrier sideband signal stability to interfere with it. When the interference exceeds the interference threshold, the alarm signal is activated and abnormal beacons are removed. When the interference does not exceed the interference threshold, the alarm signal is activated and abnormal beacons are removed, thereby detecting whether the UAV under test identifies and removes abnormal beacon points.

8. The method for monitoring the autonomous integrity of a shipborne unmanned aerial vehicle according to claim 2, characterized in that: In step S5, based on step S4, the alarm mechanism for detecting the drone under test is changed by modifying the simulated beacon, specifically as follows: Change the transmission amplitude A, pulse repetition period T, and pulse width of the analog beacon. carrier frequency FM slope Noise merging term ; beacon synthesized signal transmitted to the UAV under test : To obtain the satellite signal positioning accuracy beacon positioning accuracy Accuracy of fusion positioning Compared with accuracy The system checks whether alarms are issued correctly and whether fault detection and troubleshooting can be performed.

9. The method for monitoring the autonomous integrity of a shipborne unmanned aerial vehicle according to claim 8, characterized in that: When checking whether an alarm is correctly triggered and whether fault detection and troubleshooting can be performed, the accuracy attenuation factor H is obtained by fusing the positioning error and ranging error. H decreases normally as the reference signal source increases, and the reweighted statistic is selected. Navigation and positioning are based on a reference signal source at that time. Simultaneously, the screening of fault reference signal sources was completed and compared with the fault diagnosis results of the UAV under test; the alarm effect of the UAV under test was checked, and the results of false alarms or missed alarms under this condition were calculated.

10. The method for monitoring the autonomous integrity of a shipborne unmanned aerial vehicle according to claim 2, characterized in that: In step S6, a detection dataset is generated based on the detection results of steps S2-S5; autonomous integrity monitoring is performed based on the detection dataset, specifically by sorting the detection results of steps S2-S5 by time to generate a detection dataset; when performing autonomous integrity monitoring subsequently, the measurement results are compared with the dataset to obtain the detection results.

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