Anti-interference cluster unmanned aerial vehicle system and communication and positioning method thereof

Abstract

The application discloses an anti-interference cluster unmanned aerial vehicle system and a communication and positioning method thereof, and belongs to the technical field of unmanned aerial vehicle management. The system comprises an integrated waveform signal transmitted and received by each unmanned aerial vehicle, realizes the communication and positioning ranging information, establishes a communication positioning quality evaluation matrix, and monitors the interference signal characteristics of the communication frequency band of the unmanned aerial vehicle. When the interference is detected, the interference source coordinates and the interference coverage range are calculated in combination with the communication positioning quality evaluation matrix. The communication interference link is determined based on the interference source coordinates and the interference coverage range, the maximum cluster task optimization decision is constructed, the state optimization space is established according to the maximum cluster task optimization decision, the unmanned aerial vehicle distributed optimization strategy is generated based on the state optimization space, the topology self-healing and formation reconstruction of the unmanned aerial vehicle are carried out based on the unmanned aerial vehicle distributed optimization strategy, the resource utilization rate and the communication and positioning collaborative performance can be improved, and the anti-interference adaptability of the cluster in a complex electromagnetic environment is improved.

Description

Technical Field

[0001] This application belongs to the field of unmanned aerial vehicle (UAV) management technology, specifically, it relates to an anti-interference swarm UAV system and its communication and positioning methods. Background Technology

[0002] With the rapid expansion of the low-altitude economy, swarm drones, with their advantages of flexible deployment and collaborative operation, are widely used in various fields such as power line inspection, disaster relief, environmental monitoring, and military reconnaissance, becoming an important tool for performing tasks in complex scenarios. The efficient operation of swarm drones highly depends on stable communication links and accurate positioning capabilities, which are the core foundation for ensuring swarm collaborative control, mission command transmission, and data interaction.

[0003] Currently, most swarm drones adopt a communication and navigation coordination design approach, attempting to integrate communication and positioning functions to improve resource utilization and operational reliability. However, in complex electromagnetic environments, drone swarms are susceptible to electromagnetic interference and signal blockage, leading to communication link interruptions and positioning accuracy drift, which seriously affects the normal execution of swarm tasks. Especially in strong interference scenarios, the communication and positioning systems of traditional swarm drones have weak anti-interference capabilities and cannot meet the requirements of high-precision and high-reliability operations. Therefore, developing a swarm drone technology solution with strong anti-interference capabilities, high-precision collaborative positioning, and stable communication has become a research hotspot and an urgent need. Summary of the Invention

[0004] To address the aforementioned problems and technical deficiencies, this application adopts the following technical solution: a method for communication and positioning of anti-interference swarm drones, comprising the following steps: Each drone transmits and receives integrated waveform signals to achieve communication and positioning / ranging information. A communication positioning quality assessment matrix is ​​established. The UAV monitors the characteristics of interference signals in the communication frequency band. When interference is detected, the coordinates of the interference source and the interference coverage area are calculated by combining the communication positioning quality assessment matrix. Based on the coordinates of the interference source and the interference coverage area, the communication interference link is determined, and a decision-making mechanism is constructed to maximize the cluster task optimization. A state optimization space is established based on maximizing cluster task optimization decisions, and a distributed optimization strategy for UAVs is generated based on the state optimization space. Topology self-healing and formation reconstruction of UAVs based on distributed optimization strategies.

[0005] Preferably, the integrated waveform signal is a communication and navigation integrated orthogonal frequency division multiplexing waveform, which divides the orthogonal frequency division multiplexing symbols into communication subframes and positioning pilot subframes; The communication subframes use high-order modulation to transmit control commands, sensing data, and service data required for trunking coordination. The positioning pilot subframe adopts an orthogonal spread spectrum sequence design and uses pseudo-random codes for high-precision time synchronization and ranging. The UAV receiver completes the detection of communication subframes and the extraction of pilot subframes by combining time-frequency synchronization and channel estimation.

[0006] Preferably, the communication positioning quality evaluation matrix includes a communication quality dimension and a positioning quality dimension; Communication quality dimensions include: signal-to-interference-plus-noise ratio (SINR), packet error rate (PIN), and link capacity. The dimensions of positioning quality include: positioning error covariance matrix, geometric accuracy factor, and contribution of cooperative positioning information.

[0007] Furthermore, the collaborative positioning information contribution is obtained by introducing an information credibility assessment mechanism to obtain the credibility score of each UAV maintaining neighboring nodes. The credibility score is used as the information fusion weight, with high credibility nodes contributing a larger weight and low credibility nodes contributing a suppressive weight. The scoring criteria include: Obtain the prediction residuals of the target node's historical location information and the deviation between the target node's location information and the cluster consistency estimate; By combining the physical layer security characteristics of the target node's communication signals, a distributed consensus algorithm is used to calculate and obtain a trust score.

[0008] Furthermore, the calculation of the interference source coordinates and interference coverage area includes: Each drone will share its own location, the time of arrival of the jamming signal, and the angle of arrival through the anti-jamming reserved channel; The calculation is performed using a distributed weighted least squares algorithm combined with a communication positioning quality assessment matrix and shared data. The calculation results are then integrated with multi-machine measurement values ​​to calculate the two-dimensional or three-dimensional spatial coordinates of the interference source and the interference coverage area.

[0009] Preferably, the decision variables for maximizing cluster task optimization include communication and location dimensions; Communication dimensions include: transmit power, modulation and coding scheme, spectrum allocation, and routing path; The positioning dimensions include: ranging update frequency, cooperative positioning partner selection, and sensor fusion weights; When the communication quality of the target link deteriorates, a positioning compensation strategy is triggered to increase the inter-UAV ranging frequency associated with the target link, while also increasing the weight of visual and inertial navigation fusion. When the evaluation matrix shows that the positioning uncertainty of the target UAV exceeds the standard, a communication enhancement strategy is triggered to increase the transmission power of the relevant links or switch to a more reliable transmission mode.

[0010] Preferably, the distributed optimization strategy for the UAV is generated based on a cluster topology generation algorithm using an electromagnetic environment map. The location of the interference source is used as a constraint condition for topology optimization, and the positions of the cluster head node and relay node are dynamically adjusted to avoid key nodes being located in areas of strong interference. The optimal communication and positioning joint response strategy is selected collaboratively. The distributed optimization strategy for UAVs includes: frequency hopping pattern selection, transmit power adjustment, beamforming direction, formation reconstruction scheme, and cooperative positioning mode switching.

[0011] Preferably, the topology self-healing and formation reconfiguration include: Identify disturbed nodes by heartbeat message timeout or location discrepancy abnormalities; Based on graph neural networks, the optimal communication link reconnection scheme is predicted, and the interfered nodes are isolated from the critical routing path. Based on the spatial distribution of the remaining healthy nodes in the state optimization space, the formation configuration is dynamically adjusted to minimize the impact of the interference airspace on the cluster. At the same time, healthy nodes add ranging support for affected nodes, enabling them to restore their positioning capabilities.

[0012] An anti-jamming swarm drone system includes: The communication and navigation transmission module enables each UAV to transmit and receive integrated waveform signals, achieving communication and positioning ranging information. The interference localization module establishes a communication localization quality assessment matrix. The UAV monitors the characteristics of interference signals in the communication frequency band. When interference is detected, it calculates the coordinates of the interference source and the interference coverage area by combining the communication localization quality assessment matrix. The link determination module determines the communication interference link based on the coordinates of the interference source and the interference coverage area, and constructs a decision-making mechanism to maximize cluster task optimization. The strategy generation module establishes a state optimization space based on maximizing cluster task optimization decisions, and generates distributed optimization strategies for UAVs based on the state optimization space. The cluster reconstruction module performs topology self-healing and formation reconstruction of UAVs based on the distributed optimization strategy for UAVs.

[0013] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the content of the anti-interference swarm drone communication and positioning method described above.

[0014] Compared to existing technologies, the beneficial effects of this application are as follows: (1) This application adopts an integrated communication and navigation waveform, which is divided into communication subframes and positioning pilot subframes. The communication subframes use high-order modulation to realize high-speed service data transmission, and the positioning pilot subframes use orthogonal spread spectrum sequences to realize high-precision time synchronization and ranging. At the same time, by combining time-frequency synchronization and channel estimation, the accuracy of communication subframe detection and positioning pilot subframe extraction is ensured, thereby improving resource utilization and communication and navigation coordination performance. (2) This application establishes an evaluation matrix that includes both communication quality and positioning quality to comprehensively monitor the communication and positioning status. Combined with the distributed weighted least squares algorithm, it uses the location, interference signal arrival time and angle of arrival data shared by multiple machines to calculate the coordinates and coverage of the interference source. At the same time, it constructs a cluster task optimization decision to maximize the two-way compensation of communication and positioning. When the communication quality deteriorates, positioning compensation is triggered. When the positioning uncertainty exceeds the standard, communication enhancement is triggered to improve the anti-interference adaptability of the cluster in complex electromagnetic environment. (3) Based on electromagnetic environment map and cluster topology generation algorithm, this application uses the location of interference source as the constraint condition for topology optimization, dynamically adjusts the position of cluster head node and relay node to avoid key nodes being in strong interference area, and identifies the interfered node by heartbeat message timeout and abnormal positioning residual, and combines graph neural network to predict the optimal link reconnection scheme to achieve topology self-healing, and dynamically adjusts the formation configuration to reduce the impact of interference airspace, and provides ranging support for the interfered node through healthy nodes to help it quickly restore positioning capability and improve the stability and resilience of the cluster. Attached Figure Description

[0015] In the attached diagram: Figure 1 This is a schematic diagram of the method steps in an embodiment of this application; Figure 2 This is a schematic diagram of the method flow of an embodiment of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of this application, but not all embodiments. Generally, the components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations.

[0017] Example 1, as Figure 1 As shown, an anti-interference swarm drone communication and positioning method includes the following steps: Each drone transmits and receives integrated waveform signals to achieve communication and positioning / ranging information. The integrated waveform signal is an integrated communication and navigation orthogonal frequency division multiplexing waveform, which divides the orthogonal frequency division multiplexing symbols into communication subframes and positioning pilot subframes; The communication subframes use high-order modulation to transmit control commands, sensing data, and service data required for trunking coordination. The positioning pilot subframe adopts an orthogonal spread spectrum sequence design and uses pseudo-random codes for high-precision time synchronization and ranging. The UAV receiver completes the detection of communication subframes and the extraction of pilot subframes by combining time-frequency synchronization and channel estimation.

[0018] A communication positioning quality assessment matrix is ​​established. The UAV monitors the characteristics of interference signals in the communication frequency band. When interference is detected, the coordinates of the interference source and the interference coverage area are calculated by combining the communication positioning quality assessment matrix. The communication and positioning quality assessment matrix includes communication quality dimensions and positioning quality dimensions; Communication quality dimensions include: signal-to-interference-plus-noise ratio (SINR), packet error rate (PIN), and link capacity. The dimensions of positioning quality include: positioning error covariance matrix, geometric accuracy factor, and contribution of cooperative positioning information.

[0019] The contribution of collaborative positioning information is obtained by introducing an information credibility assessment mechanism to obtain the credibility score of each UAV maintaining neighboring nodes. The credibility score is used as the information fusion weight, with high credibility nodes contributing a larger weight and low credibility nodes contributing a suppressive weight. The scoring criteria include: Obtain the prediction residuals of the target node's historical location information and the deviation between the target node's location information and the cluster consistency estimate; By combining the physical layer security characteristics of the target node's communication signals, a distributed consensus algorithm is used to calculate and obtain a trust score.

[0020] The calculation of the interference source coordinates and interference coverage area includes: Each drone will share its own location, the time of arrival of the jamming signal, and the angle of arrival through the anti-jamming reserved channel; The calculation is performed using a distributed weighted least squares algorithm combined with a communication positioning quality assessment matrix and shared data. The calculation results are then integrated with multi-machine measurement values ​​to calculate the two-dimensional or three-dimensional spatial coordinates of the interference source and the interference coverage area.

[0021] Based on the coordinates of the interference source and the interference coverage area, the communication interference link is determined, and a decision-making mechanism is constructed to maximize the cluster task optimization. The decision variables for maximizing cluster task optimization include communication and location dimensions; Communication dimensions include: transmit power, modulation and coding scheme, spectrum allocation, and routing path; The positioning dimensions include: ranging update frequency, cooperative positioning partner selection, and sensor fusion weights; When the communication quality of the target link deteriorates, a positioning compensation strategy is triggered to increase the inter-UAV ranging frequency associated with the target link, while also increasing the weight of visual and inertial navigation fusion. When the evaluation matrix shows that the positioning uncertainty of the target UAV exceeds the standard, a communication enhancement strategy is triggered to increase the transmission power of the relevant links or switch to a more reliable transmission mode.

[0022] A state optimization space is established based on maximizing cluster task optimization decisions, and a distributed optimization strategy for UAVs is generated based on the state optimization space. The generation of distributed optimization strategies for UAVs is based on a cluster topology generation algorithm using an electromagnetic environment map. The location of interference sources is used as a constraint for topology optimization, and the positions of the cluster head node and relay nodes are dynamically adjusted to avoid key nodes being located in areas with strong interference. The optimal communication and positioning joint response strategy is selected collaboratively. The distributed optimization strategy for UAVs includes: frequency hopping pattern selection, transmit power adjustment, beamforming direction, formation reconstruction scheme, and cooperative positioning mode switching.

[0023] Topology self-healing and formation reconstruction of UAVs based on distributed optimization strategies.

[0024] Topology self-healing and formation reconfiguration include: Identify disturbed nodes by heartbeat message timeout or location discrepancy abnormalities; Based on graph neural networks, the optimal communication link reconnection scheme is predicted, and the interfered nodes are isolated from the critical routing path. Based on the spatial distribution of the remaining healthy nodes in the state optimization space, the formation configuration is dynamically adjusted to minimize the impact of the interference airspace on the cluster. At the same time, healthy nodes add ranging support for affected nodes, enabling them to restore their positioning capabilities.

[0025] Example 2, as Figure 2 As shown, an anti-jamming swarm drone system includes: The communication and navigation transmission module enables each UAV to transmit and receive integrated waveform signals, achieving communication and positioning ranging information. The interference localization module establishes a communication localization quality assessment matrix. The UAV monitors the characteristics of interference signals in the communication frequency band. When interference is detected, it calculates the coordinates of the interference source and the interference coverage area by combining the communication localization quality assessment matrix. The link determination module determines the communication interference link based on the coordinates of the interference source and the interference coverage area, and constructs a decision-making mechanism to maximize cluster task optimization. The strategy generation module establishes a state optimization space based on maximizing cluster task optimization decisions, and generates distributed optimization strategies for UAVs based on the state optimization space. The cluster reconstruction module performs topology self-healing and formation reconstruction of UAVs based on the distributed optimization strategy for UAVs.

[0026] Example 3, from a hardware perspective, this application provides an embodiment of an electronic device containing all or part of an anti-interference swarm drone communication and positioning method. The electronic device includes a service processor and a distributed memory. The service processor is connected to the memory. The distributed memory stores a service self-management program configured to store machine-readable instructions. The service processor executes the service self-management program. When the instructions are executed by the processor, an anti-interference swarm drone communication and positioning method as described above can be implemented.

[0027] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications, improvements, and substitutions without departing from the concept of this application, and these all fall within the protection scope of this application.

Claims

1. A method for communication and positioning of anti-interference swarm drones, characterized in that, Includes the following steps: Each drone transmits and receives integrated waveform signals to achieve communication and positioning / ranging information. A communication positioning quality assessment matrix is ​​established. The UAV monitors the characteristics of interference signals in the communication frequency band. When interference is detected, the coordinates of the interference source and the interference coverage area are calculated by combining the communication positioning quality assessment matrix. Based on the coordinates of the interference source and the interference coverage area, the communication interference link is determined, and a decision-making mechanism is constructed to maximize the cluster task optimization. A state optimization space is established based on maximizing cluster task optimization decisions, and a distributed optimization strategy for UAVs is generated based on the state optimization space. Topology self-healing and formation reconstruction of UAVs based on distributed optimization strategies.

2. The anti-interference swarm UAV communication and positioning method according to claim 1, characterized in that, The integrated waveform signal is a communication and navigation integrated orthogonal frequency division multiplexing waveform, which divides the orthogonal frequency division multiplexing symbols into communication subframes and positioning pilot subframes; The communication subframes use high-order modulation to transmit control commands, sensing data, and service data required for trunking coordination. The positioning pilot subframe adopts an orthogonal spread spectrum sequence design and uses pseudo-random codes for high-precision time synchronization and ranging. The UAV receiver completes the detection of communication subframes and the extraction of pilot subframes by combining time-frequency synchronization and channel estimation.

3. The anti-interference swarm UAV communication and positioning method according to claim 1, characterized in that, The communication and positioning quality assessment matrix includes a communication quality dimension and a positioning quality dimension; Communication quality dimensions include: signal-to-interference-plus-noise ratio (SINR), packet error rate (PIN), and link capacity. The dimensions of positioning quality include: positioning error covariance matrix, geometric accuracy factor, and contribution of cooperative positioning information.

4. The anti-interference swarm UAV communication and positioning method according to claim 3, characterized in that, The contribution of the collaborative positioning information is obtained by introducing an information credibility assessment mechanism to obtain the credibility score of each UAV maintaining neighboring nodes. The credibility score is used as the information fusion weight, with high credibility nodes contributing a larger weight and low credibility nodes contributing a suppressive weight. The scoring criteria include: Obtain the prediction residuals of the target node's historical location information and the deviation between the target node's location information and the cluster consistency estimate; By combining the physical layer security characteristics of the target node's communication signals, a distributed consensus algorithm is used to calculate and obtain a trust score.

5. The anti-interference swarm UAV communication and positioning method according to claim 4, characterized in that, The calculation of the interference source coordinates and interference coverage area includes: Each drone will share its own location, the time of arrival of the jamming signal, and the angle of arrival through the anti-jamming reserved channel; The calculation is performed using a distributed weighted least squares algorithm combined with a communication positioning quality assessment matrix and shared data. The calculation results are then integrated with multi-machine measurement values ​​to calculate the two-dimensional or three-dimensional spatial coordinates of the interference source and the interference coverage area.

6. The anti-interference swarm UAV communication and positioning method according to claim 1, characterized in that, The decision variables for maximizing cluster task optimization include communication and location dimensions; Communication dimensions include: transmit power, modulation and coding scheme, spectrum allocation, and routing path; The positioning dimensions include: ranging update frequency, cooperative positioning partner selection, and sensor fusion weights; When the communication quality of the target link deteriorates, a positioning compensation strategy is triggered to increase the inter-UAV ranging frequency associated with the target link, while also increasing the weight of visual and inertial navigation fusion. When the evaluation matrix shows that the positioning uncertainty of the target UAV exceeds the standard, a communication enhancement strategy is triggered to increase the transmission power of the relevant links or switch to a more reliable transmission mode.

7. The anti-interference swarm UAV communication and positioning method according to claim 1, characterized in that, The distributed optimization strategy for UAVs is generated based on a cluster topology generation algorithm using an electromagnetic environment map. The location of the interference source is used as a constraint for topology optimization, and the positions of the cluster head node and relay node are dynamically adjusted to avoid key nodes being located in areas with strong interference. The optimal communication and positioning joint response strategy is selected collaboratively. The distributed optimization strategy for UAVs includes: frequency hopping pattern selection, transmit power adjustment, beamforming direction, formation reconstruction scheme, and cooperative positioning mode switching.

8. The anti-interference swarm UAV communication and positioning method according to claim 1, characterized in that, The topology self-healing and formation reconfiguration include: Identify disturbed nodes by heartbeat message timeout or location discrepancy abnormalities; Based on graph neural networks, the optimal communication link reconnection scheme is predicted, and the interfered nodes are isolated from the critical routing path. Based on the spatial distribution of the remaining healthy nodes in the state optimization space, the formation configuration is dynamically adjusted to minimize the impact of the interference airspace on the cluster. At the same time, healthy nodes add ranging support for affected nodes, enabling them to restore their positioning capabilities.

9. An anti-interference swarm unmanned aerial vehicle (UAV) system, characterized in that, include: The communication and navigation transmission module enables each UAV to transmit and receive integrated waveform signals, achieving communication and positioning ranging information. The interference localization module establishes a communication localization quality assessment matrix. The UAV monitors the characteristics of interference signals in the communication frequency band. When interference is detected, it calculates the coordinates of the interference source and the interference coverage area by combining the communication localization quality assessment matrix. The link determination module determines the communication interference link based on the coordinates of the interference source and the interference coverage area, and constructs a decision-making mechanism to maximize cluster task optimization. The strategy generation module establishes a state optimization space based on maximizing cluster task optimization decisions, and generates distributed optimization strategies for UAVs based on the state optimization space. The cluster reconstruction module performs topology self-healing and formation reconstruction of UAVs based on the distributed optimization strategy for UAVs.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the content of the anti-interference cluster drone communication and positioning method as described in claim 1.

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