Method and system for uav cluster to cooperatively cross gnss denial area
By using anti-interference positioning modules and virtual primary technology, the positioning and communication failures of UAV swarms in GNSS-denied environments were solved, enabling stable formation and mission execution of UAV swarms in complex electromagnetic environments, thus improving the robustness of the swarms and the mission success rate.
Patent Information
- Application Number
- CN202610634059.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-09
- Publication Date
- 2026-07-24
AI Technical Summary
Existing drone swarms rely heavily on satellite navigation and real-time communication in GNSS-denied environments, leading to positioning failures, communication disruptions, and formation disintegration, making it difficult to achieve stable collaborative traversal and mission execution in complex electromagnetic environments.
It employs an anti-interference positioning module (multi-element satellite navigation antenna, inertial navigation, photoelectric terrain matching module and data fusion unit) to provide continuous positioning capability, is equipped with a high-precision atomic clock to achieve microsecond-level time synchronization, and achieves stable collaboration without real-time communication by flying in formation with a virtual lead aircraft and a unified state equation, combined with a two-layer collision avoidance mechanism and a separation and dispersal algorithm.
Maintaining continuous drone positioning under strong electromagnetic interference, preventing formation disintegration, avoiding collisions between drones, and achieving stable collaborative passage and mission execution of large-scale drone swarms improves robustness and mission success rate.
Smart Images
Figure CN122450178A_ABST
Abstract
Description
Technical Field
[0002] This invention relates to the field of unmanned aerial vehicle (UAV) swarm formation flight technology, and more specifically, to a technical method and system for achieving UAV swarm anti-interference, communication-independent, autonomous collaborative formation flight and mission execution in environments with strong electromagnetic interference and GNSS denial. Background Technology
[0003] Unmanned aerial vehicle (UAV) swarm formation technology has become a core support for operations in complex scenarios. However, GNSS denial environment, as a typical severe scenario where strong electromagnetic interference causes complete failure of satellite navigation, air-to-ground communication and inter-aircraft communication, is a key bottleneck restricting the execution of UAV swarm missions. Existing UAV swarm collaboration technologies heavily rely on satellite navigation and positioning, as well as real-time communication. Once entering a GNSS-denied zone, satellite signal interruption directly leads to UAV positioning failure, while communication link disruption disrupts swarm collaboration command transmission, causing formation disintegration, inability to maintain relative positions, and difficulty in completing traversal and subsequent autonomous mission execution. Current research on swarm formation under conditions without navigation or communication attempts to break through traditional dependency models, but generally suffers from significant shortcomings: most solutions have complex collaboration logic, requiring substantial modifications to the original mature flight control logic of UAVs, resulting in insufficient compatibility and stability; some technologies have low decentralization, still requiring limited communication assistance, and have weak anti-interference capabilities; swarm formations have poor robustness, easily becoming disordered under strong electromagnetic interference, and unable to achieve stable collaboration that is unbreakable and unbreakable; at the same time, existing technologies lack efficient synchronization mechanisms adapted to large swarms, anti-interference positioning fusion schemes, and separation, disbanding, and collision avoidance safety strategies, making it difficult to meet the actual needs of UAV swarm collaboration in traversing GNSS-denied zones under complex electromagnetic environments.
[0004] Therefore, those skilled in the art are dedicated to providing a method for UAV swarm collaboration that does not rely on real-time communication, does not modify the original flight control, has strong anti-interference capabilities and high robustness, and can achieve stable formation crossing and autonomous mission execution in GNSS denied environments. Summary of the Invention
[0005] To achieve the above objectives, the present invention provides a method for unmanned aerial vehicle (UAV) swarms to collaboratively traverse GNSS-denied areas, comprising the following steps: S1: Equip the UAV with an anti-jamming positioning module to enable the UAV to maintain continuous positioning capability in GNSS denied environments; S2: Perform swarm mission planning and local mission planning for drones; S3: Send unified assembly time and formation instructions to the drones; S4: The onboard mission computer calculates the virtual lead aircraft and time synchronization data based on the aggregated information and sends it to the flight control computer; S5: The drones fly in formation based on the virtual lead drone and time synchronization data, forming a cluster formation that coordinates according to the time rhythm.
[0006] Furthermore, the anti-interference positioning module includes a multi-element satellite navigation antenna, an inertial navigation system, an optoelectronic terrain matching module, and a data fusion unit; and is equipped with three working modes: GNSS normal, GNSS weak interference, and GNSS completely disabled. When GNSS is normal, satellite navigation is the primary mode, with inertial navigation and optoelectronic terrain matching as secondary modes. When GNSS is completely disabled, inertial navigation is the primary mode, with optoelectronic terrain matching periodically calibrated.
[0007] Furthermore, in step S2, the cluster task planning uniformly sets rendezvous points, waypoints, and separation points, and the disbanding points are distinguished according to the task area; the local task planning includes primary tasks, secondary tasks, and final tasks, with primary tasks being executed first, secondary tasks being supplementary tasks, and the final task being the return landing and reserving an emergency response plan.
[0008] Furthermore, S3 also includes: sending unified assembly time and formation information to drones that subsequently join the cluster; Subsequent drones joining the cluster obtain real-time data from the virtual lead drone via the ground station, use rapid track interpolation to join the cluster, and complete a second time calibration with the ground reference station within 5 seconds of joining the network.
[0009] Furthermore, the virtual alpha aircraft possesses the characteristics of full-scale time synchronization and data sharing among task nodes; the UAV is equipped with a high-precision atomic clock with a timing accuracy of ≤10ns, calibrated with the ground reference station before takeoff, and synchronized with the inertial navigation timestamp during flight, with a data time difference of ≤50μs for the virtual alpha aircraft; all UAVs adopt a consistent parameter model, with parameters injected once or in a distributed manner, and no additional inter-aircraft data interaction during flight.
[0010] Furthermore, the position of the virtual lead aircraft at the unified synchronization moment is calculated using linear interpolation of its flight path, with the specific formula as follows:
[0011]
[0012]
[0013] in, , , The coordinates and timestamp of the virtual lead aircraft's initial flight path. , , , The coordinates and timestamp of the next waypoint. To ensure uniform synchronization, all drones use the same parameters to achieve virtual lead drone position estimation without additional inter-drone communication.
[0014] Furthermore, all UAVs employ a completely consistent three-dimensional state equation for the virtual leader, which includes:
[0015] in, For virtual primary Time and location; For the virtual primary machine at the initial moment Axis position; For the virtual primary machine at the initial moment Axis velocity components; For the virtual helm during the movement Axial acceleration components;
[0016] in, For virtual primary Time and location; For the virtual primary machine at the initial moment Axis position; For the virtual primary machine at the initial moment Axis velocity components; For the virtual helm during the movement Axial acceleration components;
[0017] in, For virtual primary Time and location; For the virtual primary machine at the initial moment Axis position; For the virtual primary machine at the initial moment Axis velocity components; For the virtual helm during the movement Axial acceleration components; The initial position, initial velocity components, and acceleration components are completely consistent across all UAVs to achieve data homogeneity.
[0018] Furthermore, the cluster splits into smaller squads upon reaching the separation point, and autonomously disbands upon reaching the disbanding point to execute its tasks; the separation and disbanding employ a detachment algorithm to prevent drones from crossing the altitude of other drones or colliding with them.
[0019] Furthermore, the cluster flight adopts a two-layer collision avoidance mechanism between aircraft. The first layer is collision avoidance based on the preset trajectory of the virtual lead aircraft, and the second layer is collision avoidance in real time by airborne sensors. When the distance between aircraft is less than the safety threshold, collision avoidance maneuvers are automatically triggered.
[0020] A system for coordinated swarming of unmanned aerial vehicles (UAVs) traversing GNSS-denied areas, the system comprising: The anti-interference positioning module is used to provide continuous positioning capability for UAVs in GNSS denied environments. It includes a multi-element satellite navigation antenna, inertial navigation, photoelectric terrain matching module and data fusion unit, and can adaptively switch between three modes: normal GNSS, weak interference and complete incompetence. The airborne mission computer is used to calculate the virtual alpha and time synchronization data based on the aggregate information and send it to the flight control computer. It also performs instruction filtering and virtual alpha status calculation. The flight control computer is used to receive virtual leader and time synchronization data, execute formation following algorithms, and control the drones to complete formation flight in a coordinated manner according to the time rhythm. The ground station cluster mission planning system is used to uniformly complete cluster mission planning and local mission planning, issue assembly time and formation instructions to UAVs, and provide ground reference station time calibration services.
[0021] The present invention has the following beneficial effects: 1. This invention employs a multi-element satellite navigation antenna, inertial navigation, and optoelectronic terrain matching fusion positioning. It features adaptive switching between three modes: normal GNSS, weak interference, and complete inoperability. When satellite signals are interrupted, inertial navigation takes precedence, with periodic optoelectronic calibration, ensuring continuous and reliable positioning of the UAV under strong electromagnetic interference, thus eliminating the sole reliance on satellite navigation.
[0022] 2. This invention uses a virtual alpha aircraft as its core and is equipped with a high-precision atomic clock to achieve microsecond-level time synchronization. All UAVs adopt the same parameter model and unified state equation. The virtual alpha aircraft's state is autonomously calculated through linear interpolation of the flight path. The formation can be maintained without inter-aircraft data interaction during flight, which fundamentally solves the problem of coordination failure caused by communication blockage. The formation cannot be broken or scattered, and stable cluster coordination without real-time communication is achieved.
[0023] 3. In this invention, the airborne mission computer and flight control computer work together in a collaborative manner. Only the virtual helm calculation and command filtering functions are added, without changing the original mature flight control logic. This reduces the difficulty of system modification, improves technical compatibility and flight stability, and allows for rapid integration with existing UAV platforms.
[0024] 4. This invention adopts a dual-layer protection mechanism of pre-set trajectory collision avoidance and real-time collision avoidance by airborne sensors, combined with a separation and disbanding algorithm, to effectively avoid inter-aircraft collisions and high-altitude crossings; it unifies the planning of cluster and local tasks, supports the splitting of large clusters, the disbanding of small squads and autonomous execution of tasks, and completes crossings and subsequent operations in complex electromagnetic environments, thereby improving the overall robustness of the cluster and the success rate of tasks.
[0025] 5. This invention supports rapid trajectory interpolation and queuing of subsequent UAVs and secondary time calibration within 5 seconds of network entry. Global time synchronization and task node data are from the same source, which can support stable collaboration of large-scale UAV clusters. It does not require complex decentralized networking, has high synchronization efficiency, flexible expansion, and is suitable for efficient synchronization and expansion of large clusters. Attached Figure Description
[0026] Figure 1 This is a schematic diagram of the air-to-ground time synchronization principle in this invention.
[0027] Figure 2 This is a schematic diagram of the ground station cluster task planning and design interface in this invention.
[0028] Figure 3 This is a schematic diagram illustrating the working principle of the virtual primary machine in this invention.
[0029] Figure 4 This is a schematic diagram of the data fusion structure of the anti-interference positioning module in this invention. Detailed Implementation
[0030] The present invention will be further described below with reference to the accompanying drawings and embodiments: In the description of this invention, it should be noted that the terms "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0031] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "setting," and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.
[0032] This invention addresses the technical problems of existing UAV swarms in GNSS-denied environments, such as high dependence on satellite navigation and real-time communication, easy formation disintegration, poor cooperative robustness, insufficient compatibility, and lack of efficient synchronization and safety protection mechanisms, through a complete technical solution including multi-source fusion of anti-interference positioning modules, virtual lead aircraft homogeneous synchronization, communication-free cooperative formation, and dual-layer collision avoidance and safety separation.
[0033] like Figures 1 to 4 As shown, a method for coordinated traversal of GNSS-denied zones by a swarm of unmanned aerial vehicles (UAVs) includes the following steps: S1: Equip the UAV with an anti-jamming positioning module to enable the UAV to maintain continuous positioning capability in GNSS denied environments; S2: Perform swarm mission planning and local mission planning for drones; S3: Send unified assembly time and formation instructions to the drones; S4: The onboard mission computer calculates the virtual lead aircraft and time synchronization data based on the aggregated information and sends it to the flight control computer; S5: The drones fly in formation based on the virtual lead drone and time synchronization data, forming a cluster formation that coordinates according to the time rhythm.
[0034] The anti-interference positioning module includes a multi-element satellite navigation antenna, inertial navigation, optoelectronic terrain matching module, and data fusion unit; and is set with three working modes: GNSS normal, GNSS weak interference, and GNSS completely disabled. When GNSS is normal, satellite navigation is the main mode, and inertial navigation and optoelectronic terrain matching are the auxiliary modes. When GNSS is completely disabled, inertial navigation is the main mode, and optoelectronic terrain matching is periodically calibrated.
[0035] In step S2, the cluster task planning uniformly sets rendezvous points, waypoints, and separation points, and disbanding points are distinguished according to the task area; the local task planning includes primary tasks, secondary tasks, and final tasks. Primary tasks are executed first, secondary tasks are supplementary tasks, and the final task is to return to base and land, and reserve emergency response plans.
[0036] S3 also includes: sending unified assembly time and formation information to drones that subsequently join the cluster; Subsequent drones joining the cluster obtain real-time data from the virtual lead drone via the ground station, use rapid track interpolation to join the cluster, and complete a second time calibration with the ground reference station within 5 seconds of joining the network.
[0037] The virtual alpha aircraft has the characteristics of full time synchronization and data from the same source for all mission nodes; the UAV is equipped with a high-precision atomic clock with a timing accuracy of ≤10ns. It is calibrated with the ground reference station before takeoff and synchronized with the inertial navigation timestamp during flight, with a data time difference of ≤50μs for the virtual alpha aircraft; all UAVs adopt a consistent parameter model, with parameters injected at once or in a distributed manner, and there is no additional inter-aircraft data interaction during flight.
[0038] The position of the virtual lead aircraft at the unified synchronization moment is calculated using linear interpolation of its flight path, with the specific formula as follows:
[0039]
[0040]
[0041] in, , , The coordinates and timestamp of the virtual lead aircraft's initial flight path. , , , The coordinates and timestamp of the next waypoint. To ensure uniform synchronization, all drones use the same parameters to achieve virtual lead drone position estimation without additional inter-drone communication.
[0042] All drones employ a completely consistent virtual leader three-dimensional state equation, which includes:
[0043] in, For virtual primary Time and location; For the virtual primary machine at the initial moment Axis position; For the virtual primary machine at the initial moment Axis velocity components; For the virtual helm during the movement Axial acceleration components;
[0044] in, For virtual primary Time and location; For the virtual primary machine at the initial moment Axis position; For the virtual primary machine at the initial moment Axis velocity components; For the virtual helm during the movement Axial acceleration components;
[0045] in, For virtual primary Time and location; For the virtual primary machine at the initial moment Axis position; For the virtual primary machine at the initial moment Axis velocity components; For the virtual helm during the movement Axial acceleration components; The initial position, initial velocity components, and acceleration components are completely consistent across all UAVs to achieve data homogeneity.
[0046] When the cluster reaches the separation point, it splits into smaller squads, and when it reaches the disbanding point, it autonomously disbands to continue its mission. Separation and disbanding employ a detachment algorithm to prevent drones from crossing the altitude of other drones or colliding with each other.
[0047] The cluster flight employs a two-layer collision avoidance mechanism. The first layer is collision avoidance based on a preset trajectory of the virtual lead aircraft, and the second layer is real-time collision avoidance using onboard sensors. Collision avoidance maneuvers are automatically triggered when the distance between aircraft is less than a safety threshold.
[0048] A system for coordinated swarming of unmanned aerial vehicles (UAVs) traversing GNSS-denied areas, the system comprising: The anti-interference positioning module is used to provide continuous positioning capability for UAVs in GNSS denied environments. It includes a multi-element satellite navigation antenna, inertial navigation, photoelectric terrain matching module and data fusion unit, and can adaptively switch between three modes: normal GNSS, weak interference and complete incompetence. The airborne mission computer is used to calculate the virtual alpha and time synchronization data based on the aggregate information and send it to the flight control computer. It also performs instruction filtering and virtual alpha status calculation. The flight control computer is used to receive virtual leader and time synchronization data, execute formation following algorithms, and control the drones to complete formation flight in a coordinated manner according to the time rhythm. The ground station cluster mission planning system is used to uniformly complete cluster mission planning and local mission planning, issue assembly time and formation instructions to UAVs, and provide ground reference station time calibration services.
[0049] Based on the method of UAV swarm collaborative traversal of GNSS-denied areas, the following two specific implementation methods are provided: Example 1: Basic GNSS Crossing Denial Example 1. Equip the UAV with an anti-interference positioning module, including a multi-element satellite navigation antenna, MEMS inertial navigation, optoelectronic terrain matching module and data fusion unit; three preset working modes: satellite navigation as the main mode and inertial navigation and optoelectronic as auxiliary modes when GNSS is normal; inertial navigation as the main mode and optoelectronic periodic calibration when GNSS is completely disabled.
[0050] 2. The ground station completes cluster mission planning, uniformly sets rendezvous points, waypoints, and separation points, and disbanding points are distinguished according to mission areas; completes local mission planning: sets priority primary missions, supplementary secondary missions, and the final mission of returning to base and landing, and reserves contingency plans.
[0051] 3. Send a unified assembly time and formation to all drones; subsequent drones joining the network obtain real-time data of the virtual lead drone through the ground station, and join the formation after rapid track interpolation. The second calibration of the ground reference station time is completed within 5 seconds of joining the network.
[0052] 4. The onboard mission computer calculates the virtual alpha aircraft and time synchronization data based on the ensemble information and sends it to the flight control computer; the UAV is equipped with a high-precision atomic clock with a timing accuracy of ≤10ns, which is calibrated before takeoff and synchronized with the inertial navigation timestamp during flight, with a virtual alpha aircraft data time difference of ≤50μs; all UAVs are loaded with the same parameter model, and there is no additional inter-aircraft communication during flight.
[0053] 5. The flight control computer executes a formation following algorithm, and the UAVs fly in formation in a coordinated manner according to the time rhythm; a dual-layer collision avoidance protection is adopted: preset trajectory collision avoidance + real-time collision avoidance by airborne sensors, and collision avoidance maneuvers are automatically triggered when the distance between the UAVs is less than the safety threshold.
[0054] 6. When the cluster reaches the separation point, it automatically splits into smaller teams. When it reaches the disbanding point, it autonomously disbands and executes single-machine tasks. The separation / disbanding process starts the detachment algorithm to prevent height crossing and collision.
[0055] Example 2: High-precision dynamic calculation crossing example 1. The UAV is equipped with an anti-interference positioning module, which adopts fiber optic inertial navigation, multi-element anti-interference antenna and photoelectric terrain matching fusion positioning, and strictly implements adaptive switching of three GNSS modes: normal / weak interference / complete disabling.
[0056] 2. The ground station uniformly plans the cluster flight path and separation / disbandment nodes, and the machine is configured with primary / secondary / final tasks according to the task priority; a global time synchronization mechanism is established, with the ground reference station as the time anchor point, and all UAV atomic clocks are synchronized before takeoff and maintain microsecond-level time consistency during flight.
[0057] 3. The onboard computer uses linear interpolation to calculate the virtual traitor's real-time position:
[0058]
[0059]
[0060] Simultaneously load the three-dimensional state equations: ; ;
[0061] All drone parameters are completely identical, enabling homogeneous and identical estimation without communication.
[0062] 4. The UAVs maintain formation based on virtual lead aircraft data, and the flight control computer compensates for errors in real time; a dual-layer collision avoidance mechanism is in effect throughout the process, with the planning layer avoiding spatial overlap and the execution layer using sensors for active collision avoidance. 5. In environments with GNSS rejection and communication failure, the UAVs autonomously maintain formation and traverse the area according to a time-based rhythm; upon reaching the separation point, they orderly group up, and upon reaching the disbandment point, they autonomously disperse to perform their tasks, with no collisions during separation and disbandment, and return to base according to the plan after completing the mission.
[0063] In this invention, Example 2 is the preferred embodiment.
[0064] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.
Claims
1. A method for coordinated traversal of GNSS-denied areas by a swarm of unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: S1: Equip the UAV with an anti-jamming positioning module to enable the UAV to maintain continuous positioning capability in GNSS denied environments; S2: Perform swarm mission planning and local mission planning for drones; S3: Send unified assembly time and formation instructions to the drones; S4: The onboard mission computer calculates the virtual lead aircraft and time synchronization data based on the aggregated information and sends it to the flight control computer; S5: The drones fly in formation based on the virtual lead drone and time synchronization data, forming a cluster formation that coordinates according to the time rhythm.
2. The method for coordinated traversal of GNSS-denied zones by a swarm of unmanned aerial vehicles according to claim 1, characterized in that: The anti-interference positioning module includes a multi-element satellite navigation antenna, inertial navigation, photoelectric terrain matching module and data fusion unit; It is equipped with three working modes: GNSS normal, GNSS weak interference, and GNSS completely disabled. When GNSS is normal, satellite navigation is the primary mode, with inertial navigation and electro-optical terrain matching as secondary modes. When GNSS is completely disabled, inertial navigation is the primary mode, with electro-optical terrain matching and periodic calibration.
3. The method for coordinated traversal of GNSS-denied zones by a swarm of unmanned aerial vehicles according to claim 1, characterized in that: In step S2, the cluster task planning uniformly sets rendezvous points, waypoints, and separation points, and disbanding points are distinguished according to the task area; the local task planning includes primary tasks, secondary tasks, and final tasks. Primary tasks are executed first, secondary tasks are supplementary tasks, and the final task is to return to base and land, and reserve emergency response plans.
4. The method for coordinated traversal of GNSS-denied zones by a swarm of unmanned aerial vehicles according to claim 1, characterized in that: S3 also includes: sending unified assembly time and formation information to drones that subsequently join the cluster; Subsequent drones joining the cluster obtain real-time data from the virtual lead drone via the ground station, use rapid track interpolation to join the cluster, and complete a second time calibration with the ground reference station within 5 seconds of joining the network.
5. The method for coordinated traversal of GNSS-denied zones by a swarm of unmanned aerial vehicles according to claim 1, characterized in that: The virtual alpha aircraft has the characteristics of full time synchronization and data from the same source for all mission nodes; the UAV is equipped with a high-precision atomic clock with a timing accuracy of ≤10ns. It is calibrated with the ground reference station before takeoff and synchronized with the inertial navigation timestamp during flight, with a data time difference of ≤50μs for the virtual alpha aircraft; all UAVs adopt a consistent parameter model, with parameters injected at once or in a distributed manner, and there is no additional inter-aircraft data interaction during flight.
6. The method for coordinated traversal of GNSS-denied zones by a swarm of unmanned aerial vehicles according to claim 1, characterized in that: The position of the virtual lead aircraft at the unified synchronization moment is calculated using linear interpolation of its flight path, with the specific formula as follows: in, , , The coordinates and timestamp of the virtual lead aircraft's initial flight path. , , , The coordinates and timestamp of the next waypoint. To ensure uniform synchronization, all drones use the same parameters to achieve virtual lead drone position estimation without additional inter-drone communication.
7. The method for coordinated traversal of GNSS-denied zones by a swarm of unmanned aerial vehicles according to claim 1, characterized in that: All drones employ a completely consistent virtual leader three-dimensional state equation, which includes: in, For virtual primary Time and location; For the virtual primary machine at the initial moment Axis position; For the virtual primary machine at the initial moment Axis velocity components; For the virtual helm during the movement Axial acceleration components; in, For virtual primary Time and location; For the virtual primary machine at the initial moment Axis position; For the virtual primary machine at the initial moment Axis velocity components; For the virtual helm during the movement Axial acceleration components; in, For virtual primary Time and location; For the virtual primary machine at the initial moment Axis position; For the virtual primary machine at the initial moment Axis velocity components; For the virtual helm during the movement Axial acceleration components; The initial position, initial velocity components, and acceleration components are completely consistent across all UAVs to achieve data homogeneity.
8. The method for coordinated traversal of GNSS-denied zones by a swarm of unmanned aerial vehicles according to claim 1, characterized in that: When the cluster reaches the separation point, it splits into smaller squads, and when it reaches the disbanding point, it autonomously disbands to continue its mission. Separation and disbanding employ a detachment algorithm to prevent drones from crossing the altitude of other drones or colliding with each other.
9. The method for coordinated traversal of GNSS-denied zones by a swarm of unmanned aerial vehicles according to claim 1, characterized in that: The cluster flight employs a two-layer collision avoidance mechanism. The first layer is collision avoidance based on a preset trajectory of the virtual lead aircraft, and the second layer is real-time collision avoidance using onboard sensors. Collision avoidance maneuvers are automatically triggered when the distance between aircraft is less than a safety threshold.
10. A system for coordinated swarming of unmanned aerial vehicles (UAVs) to traverse GNSS-denied areas, characterized in that, The system includes: The anti-interference positioning module is used to provide continuous positioning capability for UAVs in GNSS denied environments. It includes a multi-element satellite navigation antenna, inertial navigation, photoelectric terrain matching module and data fusion unit, and can adaptively switch between three modes: normal GNSS, weak interference and complete incompetence. The airborne mission computer is used to calculate the virtual alpha and time synchronization data based on the aggregate information and send it to the flight control computer. It also performs instruction filtering and virtual alpha status calculation. The flight control computer is used to receive virtual leader and time synchronization data, execute formation following algorithms, and control the drones to complete formation flight in a coordinated manner according to the time rhythm. The ground station cluster mission planning system is used to uniformly complete cluster mission planning and local mission planning, issue assembly time and formation instructions to UAVs, and provide ground reference station time calibration services.