Manned aerial vehicle collision avoidance method and system based on unmanned aerial vehicle channel spectrum detection, and medium

Abstract

The invention discloses a manned vehicle collision avoidance method and system based on unmanned aerial vehicle channel spectrum detection and a medium, and belongs to the technical field of unmanned aerial vehicle detection. The method comprises the following steps: an unmanned aerial vehicle positioning stage: installing a plurality of receiving antennas at different positions on a manned aerial vehicle, receiving a signal sent by the unmanned aerial vehicle through an unmanned aerial vehicle positioning method, and determining the accurate position of the unmanned aerial vehicle through resolving a nonlinear equation set to obtain unmanned aerial vehicle positioning data; in a conflict early warning and risk avoiding stage, reading real-time position information, speed information and course information of a manned aerial vehicle provided by an ADS-B system to obtain ADS-B data; the unmanned aerial vehicle positioning data and the ADS-B data are combined, feature-level deep processing is carried out through a data fusion algorithm, the relative position and distance of the unmanned aerial vehicle and the manned vehicle are evaluated, and finally conflict early warning is carried out and a risk avoiding strategy is generated. And effective early warning is carried out, so that unnecessary course adjustment caused by avoiding the unmanned aerial vehicle is reduced, and the flight efficiency of the civil aircraft and the punctuality rate of the flight are improved.

Description

Technical Field

[0001] The present invention relates to the field of drone detection technology, and in particular to a manned-aircraft collision avoidance method, system and medium based on drone channel spectrum detection. Background Art

[0002] The rapid development of drone technology has brought about a wide range of applications, but it has also posed new challenges to civil aviation safety. Drones and civil aircraft are frequently approaching each other, increasing the risk of mid-air collisions. To avoid such risks, countries have implemented drone flight regulations and no-fly zone policies, restricting drone flights in specific areas through electronic fence systems to protect passengers and aviation safety.

[0003] As a key technology to improve air traffic monitoring capabilities, ADS-B technology allows aircraft to automatically broadcast their position and flight data on the 1090MHz channel, enhancing the real-time monitoring capabilities of air traffic control. However, ADS-B has limitations in detecting non-cooperative targets such as drones, and the detection and early warning of drones is still an issue that needs to be addressed in the field of aviation safety.

[0004] There are some obvious limitations of existing technologies in the field of drone management and civil aviation safety. First, although the setting of no-fly zones is intended to restrict the flight of drones in specific areas, these restrictions are not always effective for some modified drones. There are some merchants on the market that provide drone modification services. They can install GPS cracking modules on drones, allowing drones to break through the restrictions of no-fly zones. This modification behavior makes it difficult to regulate drones in no-fly zones and increases the risk of conflicts between civil aircraft and drones. Secondly, although ADS-B technology provides an effective means of air-to-air surveillance between civil aircraft, it is powerless in detecting drones. The ADS-B system relies on aircraft to automatically broadcast their location information, but most drones do not have this broadcasting capability and therefore cannot be effectively detected by the ADS-B system. This results in civil aircraft being unable to detect nearby drones in time through the ADS-B system during flight, increasing the risk of mid-air collisions.

[0005] Existing technologies cannot achieve high-precision positioning of drones near civil aircraft, especially in complex airspace environments, and cannot detect drones at a distance far enough to provide sufficient reaction time for evasive maneuvers. There is a lack of effective systems to warn of potential conflicts between drones and civil aircraft and guide pilots to evade risks. Existing technologies cannot provide dynamic evasive warnings based on the real-time location of drones and the flight status of civil aircraft. Summary of the invention

[0006] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a manned-aircraft collision avoidance method, system and medium based on UAV channel spectrum detection.

[0007] The objective of the present invention is achieved through the following technical solutions: The first aspect of the present invention provides: a manned-aircraft collision avoidance method based on UAV channel spectrum detection, comprising the following steps: In the UAV positioning stage, multiple receiving antennas at different positions are installed on the manned aircraft, and the signals sent by the UAV are received through the UAV positioning method. The time difference between the same signal sent from the UAV and reaching different receiving antennas is measured. Based on the principle of constant signal propagation speed and the known positions of each receiving antenna, the precise position of the UAV is determined by solving the nonlinear equation group to obtain the UAV positioning data; During the conflict warning and risk avoidance stage, the real-time position, speed, and heading information of manned aircraft provided by the ADS-B system is read to obtain ADS-B data. The UAV positioning data and ADS-B data are combined, and feature-level in-depth processing is performed through a data fusion algorithm to evaluate the relative position and distance between the UAV and the manned aircraft, and finally a conflict warning is issued and a risk avoidance strategy is generated.

[0008] Preferably, the receiving antenna is installed in front of the belly, front and rear of the belly, on the left side of the wing, and on the right side of the wing of the manned aircraft.

[0009] Preferably, when the drone positioning data is fused with the ADS-B data, the following steps are included: First, the drone positioning data and ADS-B data are preprocessed; Then, the trajectory quality is quantitatively evaluated by analyzing the local track accuracy, data update times and sensor measurement error, and the quality weighting factor of the local track is calculated. The quality weighting factor is used to adjust the weights of different sensor data in the data fusion process.

[0010] Preferably, the UAV positioning method is a TDOA positioning method.

[0011] Preferably, the manned aircraft is a civil aircraft or a helicopter.

[0012] The second aspect of the present invention provides: a manned-aircraft collision avoidance system based on UAV channel spectrum detection, which is used to implement any of the above-mentioned manned-aircraft collision avoidance methods based on UAV channel spectrum detection, comprising: The UAV positioning module is used to install multiple receiving antennas at different positions on the manned aircraft, receive the signal sent by the UAV through the UAV positioning method, measure the time difference of the same signal from the UAV to different receiving antennas, and determine the precise position of the UAV by solving the nonlinear equation group based on the principle of constant signal propagation speed and the known position of each receiving antenna to obtain the UAV positioning data; The conflict warning and risk avoidance module is used to read the real-time position information, speed information, and heading information of manned aircraft provided by the ADS-B system to obtain ADS-B data; combine the UAV positioning data with the ADS-B data, perform deep feature-level processing through the data fusion algorithm, evaluate the relative position and distance between the UAV and the manned aircraft, and finally issue a conflict warning and generate a risk avoidance strategy.

[0013] The third aspect of the present invention provides: a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by a processor, any of the above-mentioned manned-aircraft collision avoidance methods based on UAV channel spectrum detection is implemented.

[0014] The beneficial effects of the present invention are: 1) TDOA technology can achieve UAV positioning within an error range of at least 500 meters, significantly improving the accuracy of UAV positioning and providing more reliable UAV location information for civil aircraft.

[0015] 2) Effective early warning reduces unnecessary route adjustments to avoid drones, thereby improving the flight efficiency of civil aircraft and the punctuality of flights.

[0016] 3) It is compatible with existing civil aviation aircraft systems, does not require large-scale modifications, has good scalability, and can be quickly deployed to the existing civil aviation fleet. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of UAV positioning; Figure 2 Schematic diagram of early warning processing of UAV positioning data and ADS-B data fusion. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1-Figure 2 The first aspect of the present invention provides: a manned-aircraft collision avoidance method based on UAV channel spectrum detection, comprising the following steps: In the UAV positioning stage, multiple receiving antennas at different positions are installed on the manned aircraft, and the signals sent by the UAV are received through the UAV positioning method. The time difference between the same signal sent from the UAV and reaching different receiving antennas is measured. Based on the principle of constant signal propagation speed and the known positions of each receiving antenna, the precise position of the UAV is determined by solving the nonlinear equation group to obtain the UAV positioning data; During the conflict warning and risk avoidance stage, the real-time position, speed, and heading information of manned aircraft provided by the ADS-B system is read to obtain ADS-B data. The UAV positioning data and ADS-B data are combined, and feature-level in-depth processing is performed through a data fusion algorithm to evaluate the relative position and distance between the UAV and the manned aircraft, and finally a conflict warning is issued and a risk avoidance strategy is generated.

[0020] In this embodiment, a solution combining TDOA (Time Difference of Arrival) drone positioning technology and the ADS-B system of civil aircraft is used. This solution can achieve drone directional ranging with a distance error of at least 500 meters, detect drones and manned aircraft in the air as a whole, provide early warnings for civil aviation avoidance distances, and make avoidance prompts.

[0021] By installing four receiving antennas on civil aircraft (front and back of the belly and left and right of the wings), using TDOA technology to receive signals from drones, measuring the time difference between the same signal sent from the drone and reaching different receiving antennas, using the principle of constant signal propagation speed, combined with the known positions of each receiving antenna, and solving nonlinear equations to determine the precise position of the drone, high-precision positioning of the drone can be achieved. TDOA technology does not rely on the cooperation of drones and can be used to passively locate drones. This method can control the positioning error of drones within 500 meters, providing sufficient warning time for civil aircraft. At the same time, the ADS-B system provides real-time position, speed, heading and other information of civil aircraft. These data are combined with the drone position information obtained by the TDOA system, and the relative position and distance between the drone and the civil aircraft are evaluated through data fusion algorithms. This fusion includes not only direct merging at the data level, but also deep processing at the feature level, and ultimately provides conflict warning and risk avoidance strategies at the decision level.

[0022] In some embodiments, the receiving antenna is installed in front of the belly, front and rear of the belly, left side of the wing, and right side of the wing of the manned aircraft.

[0023] In some embodiments, when the drone positioning data is fused with the ADS-B data, the following steps are included: First, the drone positioning data and ADS-B data are preprocessed; Then, the trajectory quality is quantitatively evaluated by analyzing the local track accuracy, data update times and sensor measurement error, and the quality weighting factor of the local track is calculated. The quality weighting factor is used to adjust the weights of different sensor data in the data fusion process.

[0024] In this embodiment, during the data fusion process, TDOA and ADS-B data are first preprocessed to ensure data consistency and accuracy. Then, by analyzing influencing factors such as local track accuracy, data update times, and sensor measurement errors, the track quality is quantitatively evaluated and the quality weighting factor of the local track is calculated. These weighting factors are used to adjust the weights of different sensor data in the fusion process to reflect their respective tracking performance and reliability. Through this method based on track quality assessment, the fusion tracking accuracy can be effectively improved and the error can be reduced even when the sensor performance or data accuracy fluctuates.

[0025] In some embodiments, the UAV positioning method is a TDOA positioning method.

[0026] In this embodiment, the UAV positioning method is not limited to the TDOA positioning method. In terms of design, the TDO UAV positioning technology can be replaced with other UAV detection and positioning technologies.

[0027] In some embodiments, the manned aircraft is a civil aircraft or a helicopter.

[0028] In this embodiment, the manned aircraft is not limited to civil aircraft or helicopters, and any aircraft that needs to carry people can use the method disclosed in the present invention.

[0029] The second aspect of the present invention provides: a manned-aircraft collision avoidance system based on UAV channel spectrum detection, which is used to implement any of the above-mentioned manned-aircraft collision avoidance methods based on UAV channel spectrum detection, comprising: The UAV positioning module is used to install multiple receiving antennas at different positions on the manned aircraft, receive the signal sent by the UAV through the UAV positioning method, measure the time difference of the same signal from the UAV to different receiving antennas, and determine the precise position of the UAV by solving the nonlinear equation group based on the principle of constant signal propagation speed and the known position of each receiving antenna to obtain the UAV positioning data; The conflict warning and risk avoidance module is used to read the real-time position information, speed information, and heading information of manned aircraft provided by the ADS-B system to obtain ADS-B data; combine the UAV positioning data with the ADS-B data, perform deep feature-level processing through the data fusion algorithm, evaluate the relative position and distance between the UAV and the manned aircraft, and finally issue a conflict warning and generate a risk avoidance strategy.

[0030] The third aspect of the present invention provides: a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by a processor, any of the above-mentioned manned-aircraft collision avoidance methods based on UAV channel spectrum detection is implemented.

[0031] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art do not deviate from the spirit and scope of the present invention, and should be within the scope of protection of the claims attached to the present invention.

Claims

1. A manned-aircraft collision avoidance method based on UAV channel spectrum detection is characterized by: The following steps are involved: In the UAV positioning stage, multiple receiving antennas at different positions are installed on the manned aircraft, and the signals sent by the UAV are received through the UAV positioning method. The time difference between the same signal sent from the UAV and reaching different receiving antennas is measured. Based on the principle of constant signal propagation speed and the known positions of each receiving antenna, the precise position of the UAV is determined by solving the nonlinear equation group to obtain the UAV positioning data; During the conflict warning and risk avoidance stage, the real-time position, speed, and heading information of manned aircraft provided by the ADS-B system is read to obtain ADS-B data. The UAV positioning data and ADS-B data are combined, and feature-level in-depth processing is performed through a data fusion algorithm to evaluate the relative position and distance between the UAV and the manned aircraft, and finally a conflict warning is issued and a risk avoidance strategy is generated.

2. The method for avoiding collision between manned aircraft and drone based on drone channel spectrum detection according to claim 1 is characterized in that: The receiving antennas are installed in front of the belly, front and rear of the belly, on the left side of the wing and on the right side of the wing of the manned aircraft.

3. The method for avoiding collision between manned aircraft and drone based on drone channel spectrum detection according to claim 1 is characterized in that: When fusing drone positioning data with ADS-B data, the following steps are included: First, the drone positioning data and ADS-B data are preprocessed; Then, the trajectory quality is quantitatively evaluated by analyzing the local track accuracy, data update times and sensor measurement error, and the quality weighting factor of the local track is calculated. The quality weighting factor is used to adjust the weights of different sensor data in the data fusion process.

4. The method for avoiding collision between manned aircraft and drone based on UAV channel spectrum detection according to any one of claims 1 to 3, characterized in that: The UAV positioning method described is a TDOA positioning method.

5. The method for avoiding collision between manned aircraft and drone based on UAV channel spectrum detection according to any one of claims 1 to 3, characterized in that: The manned aircraft is a civil aircraft or a helicopter.

6. The manned-aircraft collision avoidance system based on UAV channel spectrum detection is characterized by: The method for avoiding collision between a manned aircraft and a drone based on the spectrum detection of a drone channel as claimed in any one of claims 1 to 5 comprises: The UAV positioning module is used to install multiple receiving antennas at different positions on the manned aircraft, receive the signal sent by the UAV through the UAV positioning method, measure the time difference of the same signal from the UAV to different receiving antennas, and determine the precise position of the UAV by solving the nonlinear equation group based on the principle of constant signal propagation speed and the known position of each receiving antenna to obtain the UAV positioning data; The conflict warning and risk avoidance module is used to read the real-time position information, speed information, and heading information of manned aircraft provided by the ADS-B system to obtain ADS-B data; combine the UAV positioning data with the ADS-B data, perform deep feature-level processing through the data fusion algorithm, evaluate the relative position and distance between the UAV and the manned aircraft, and finally issue a conflict warning and generate a risk avoidance strategy.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are loaded and executed by the processor, the manned-aircraft collision avoidance method based on UAV channel spectrum detection as described in any one of claims 1 to 5 is implemented.

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