Multi-unmanned aerial vehicle cooperative anti-interference method

Abstract

The invention provides a multi-unmanned-aerial-vehicle cooperative anti-interference method, and the method comprises the steps: obtaining the time domain, frequency domain, spatial domain and other multi-dimensional comprehensive information of an interference source through a virtual array composed of a plurality of unmanned aerial vehicles, carrying out the classification of interference signals according to the time-frequency information, carrying out the positioning of interference through the spatial domain information, and carrying out the positioning of the interference. And estimating the effective radius of interference according to a signal attenuation model and the anti-interference capability of the unmanned aerial vehicle, updating a threat area by using the information, and realizing anti-interference of the cluster unmanned aerial vehicle by adjusting the route of the cluster unmanned aerial vehicle in real time. The method is suitable for air-ground integrated cooperative networking, unmanned aerial vehicle clustering and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of UAV swarm communication, and particularly relates to a multi-UAV collaborative anti-jamming method. Background Art

[0002] The UAV data link faces many challenges such as shortage of spectrum resources, complex spectrum environment, serious environmental interference and human interference, which puts forward higher requirements for its safety, reliability and adaptability in complex environments. To ensure the interactive sharing of UAV swarm situation information, etc., the UAV communication link generally requires anti-jamming and anti-interception capabilities.

[0003] According to whether the interference information is estimated, the anti-jamming methods are divided into passive anti-jamming and active anti-jamming. For example, spread spectrum, frequency hopping, etc. belong to passive anti-jamming, and using spectrum detection for frequency agility, array antenna anti-jamming, etc. belong to active anti-jamming. The more effective anti-jamming is active anti-jamming, which requires aiming anti-jamming after estimating the time domain, frequency domain and spatial domain characteristics of the interference.

[0004] Simply using time domain or frequency domain means cannot accurately estimate the interference. Generally, an array antenna can be used to estimate the direction of the interference source and obtain the interference spatial domain information. However, due to the limited airborne space, the airborne nodes in the UAV swarm collaborative network need to communicate with multiple nodes in the UAV swarm network. Generally, omnidirectional antennas are used, and omnidirectional antennas cannot accurately estimate the direction of the incoming interference wave. Currently, the UAV swarm anti-jamming relies on a single node and does not give full play to the multi-UAV collaborative effect. Summary of the Invention

[0005] In order to overcome the problem of insufficient anti-jamming performance of the existing UAV swarm communication link, the present invention provides a multi-UAV collaborative anti-jamming method. The multi-dimensional comprehensive information such as the time domain, frequency domain and spatial domain of the interference source is obtained by using a virtual array composed of multiple UAVs, and the flight routes of the swarm UAVs are updated to achieve multi-UAV collaborative anti-jamming. The present invention is applicable to air-ground integrated collaborative networking, UAV swarms, etc.

[0006] A multi-UAV collaborative anti-jamming method, characterized in that the steps are as follows:

[0007] Step 1: Each UAV in the swarm powers on, and the network enters the access stage;

[0008] Step 2: The cluster head UAV M establishes a network time reference and broadcasts an access polling frame;

[0009] Step 3: After receiving the access polling frame, the non-cluster head UAVs randomly send access request frames, and the access request frames include the node MAC address, UAV type and UAV location;

[0010] Step 4: After the cluster head UAV M receives the access request frame, it sends an access confirmation frame, which contains the IDs, MAC addresses, and timing allocations of each UAV.

[0011] Step 5: After a non-cluster head UAV receives the access confirmation frame, if it finds its own ID in it, it adjusts its own clock and proceeds to Step 6; otherwise, it proceeds to Step 3.

[0012] Step 6: According to the timing regulations, the network access phase is completed, the network switches to the transmission phase, and proceeds to Step 7; otherwise, returns to Step 2.

[0013] Step 7: All UAVs receive the mission planning data bound by the ground TT&C station.

[0014] Step 8: Each UAV flies according to the planned flight route in the mission planning data, extracts the time-domain and frequency-domain information of the received abnormal signals, and sends the extracted information to the cluster head UAV M.

[0015] Step 9: The cluster head UAV M receives the information sent by other UAVs, and extracts the time-domain, frequency-domain information of the abnormal signals therein and the positions of each UAV.

[0016] Step 10: According to the time-frequency characteristics of the received abnormal signals, the cluster head UAV M divides the UAVs that detect the same abnormal signal into a group, and sorts the UAVs in descending order according to the signal strength they detect. Each UAV gets an identifier. Among them, n is the number of abnormal signals, and m is the total number of UAVs in the same group.

[0017] Step 11: Let i = 1, and select the 4 UAVs with the largest signal strength from where t represents the total number of UAVs included in E i including.

[0018] Step 12: Calculate the position dilution of precision PDOP value.

[0019] Step 13: If the PDOP value is less than the threshold δ, then select these 4 UAVs as the UAVs participating in the solution, and proceed to Step 14; otherwise, remove these 4 UAVs from E i and select the 4 UAVs with the largest signal strength from the remaining UAVs, and proceed to Step 12.

[0020] Step 14: According to the positions and time differences of the UAVs selected in Step 13, use the passive time difference positioning method to determine the position of the interference source corresponding to the abnormal signals in the i-th group, and estimate its effective interference radius according to the signal attenuation model and the anti-interference ability of the UAVs.

[0021] Step 15: Let \(i = i + 1\), and repeat Steps 11 to 14 until \(i = n\) to obtain the positions and interference radii of all interference sources;

[0022] Step 16: Update the interference source data in the mission planning data according to the positions and interference radii of all interference sources obtained in Step 15, and re - plan the flight path;

[0023] Step 17: The cluster - head UAV sends the re - planned data to each UAV;

[0024] Step 18: Each UAV flies according to the new planned flight path.

[0025] Specifically, the calculation formula for the position dilution of precision PDOP value in Step 12 is where \(H\) represents the vector matrix of the interference signal to the observing UAV.

[0026] Specifically, the value range of the threshold \(\delta\) in Step 13 is \([4, 10]\).

[0027] Further, the vector matrix \(H\) of the interference signal to the observing UAV is calculated according to the following formula:

[0028]

[0029] where \(e\) xy represents the unit vector from UAV \(x\) to UAV \(y\) among the selected 4 UAVs, \(x = 1, 2, 3, 4\), \(y = 1, 2, 3\).

[0030] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of a multi - UAV collaborative anti - interference method disclosed in the present invention.

[0031] A program product includes a computer program. When the computer program is run, it is used to execute the steps of a multi - UAV collaborative anti - interference method disclosed in the present invention.

[0032] A storage medium stores a computer program. When the computer program is run, it is used to execute the steps of a multi - UAV collaborative anti - interference method disclosed in the present invention.

[0033] The beneficial effects of the present invention are as follows: a virtual array composed of multiple drones is used to obtain multi-dimensional comprehensive information such as the time domain, frequency domain, and spatial domain of the interference source. The interference signals are classified according to the time-frequency information, the interference is located using the spatial domain information, and the effective radius of the interference is estimated based on the signal attenuation model and the anti-interference ability of the drones. The threat area can be updated using the above information, and the flight routes of the swarm drones can be adjusted in real time, thereby realizing the anti-interference of the swarm drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flowchart of a method for multi-drone collaborative anti-interference of the present invention;

[0035] Figure 2 is a diagram of the time slot division of the drone swarm of the present invention;

[0036] Figure 3 is a geometric schematic diagram of the drones and the interference of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0037] The present invention will be further described below in conjunction with the drawings and embodiments. The present invention includes but is not limited to the following embodiments.

[0038] The present invention provides a method for multi-drone collaborative anti-interference, as Figure 1 shown, and its specific implementation process is as follows:

[0039] Step 1: Each drone in the swarm powers on, and the network enters the access stage.

[0040] Step 2: The cluster head drone M establishes a network time reference and broadcasts an access polling frame; the cluster head drone refers to the first drone to access the network, and its ID is 1.

[0041] Step 3: After receiving the access polling frame, the drones to be accessed (non-cluster head drones, with IDs 2, 3,...) randomly send access request frames, and the access request frames include the node MAC address, drone type, and drone location.

[0042] Step 4: If the cluster head drone M does not receive the access request frame from the drone to be accessed, it sends an access confirmation frame, and the access confirmation frame contains the ID, MAC address, and timing allocation of each drone.

[0043] Step 5: After receiving the access confirmation frame, if the non-cluster head drone finds its own ID in it, it adjusts its own clock and proceeds to Step 6; otherwise, it returns to Step 3.

[0044] Step 6: According to the timing regulations, when the network access stage is completed, the network switches to the transmission stage and proceeds to Step 7; otherwise, it returns to Step 2.

[0045] Step 7: All the UAVs receive the mission planning data loaded by the ground TT&C station.

[0046] Step 8: Each UAV flies according to the planned flight route in the mission planning data, extracts the time-domain and frequency-domain information of the received abnormal signals, and sends the extracted information to the cluster head UAV M.

[0047] Step 9: The cluster head UAV M receives the information sent by other UAVs, and extracts the time-domain, frequency-domain information of the abnormal signals therein and the positions of each UAV.

[0048] Step 10: According to the time-frequency characteristics of the received abnormal signals, the cluster head UAV M divides the UAVs that detect the same abnormal signals into a group, sorts the UAVs in descending order according to the signal strength they detect, and each UAV gets an identifier. Among them, n is the number of abnormal signals, and m is the total number of UAVs in the same group.

[0049] Step 11: Let i = 1, and select the 4 UAVs with the largest signal strength from , where t represents the total number of UAVs included in E i including.

[0050] Step 12: Calculate the position dilution of precision PDOP value according to , where H represents the vector matrix of the interference signal to the observed UAV, and is calculated according to the following formula:

[0051]

[0052] Among them, e xy represents the unit vector from UAV x to UAV y among the 4 selected UAVs, x = 1, 2, 3, 4, y = 1, 2, 3.

[0053] Step 13: If the PDOP value is less than the threshold δ, select these 4 UAVs as the UAVs participating in the solution, and go to Step 14; otherwise, remove these 4 UAVs from E i , and select the 4 UAVs with the largest signal strength from the remaining UAVs, and go to Step 12. Among them, the value range of the threshold δ is [4, 10].

[0054] Step 14: According to the positions and time differences of the UAVs selected in Step 13, use the passive time difference positioning method to determine the position of the interference source corresponding to the abnormal signals in the i-th group, and estimate its effective interference radius according to the signal attenuation model and the anti-interference ability of the UAV.

[0055] Step 15: Let i = i + 1, repeat Steps 11 to 14 until i = n, and obtain the positions and interference radii of all interference sources.

[0056] Step 16: Update the interference source data in the mission planning data according to the positions and interference radii of all the interference sources obtained in Step 15, and re-plan the flight path.

[0057] Step 17: The cluster head UAV sends the re-planned data to each UAV.

[0058] Step 18: Each UAV flies according to the newly planned flight path.

[0059] Appendix Figure 2 The figure shows the time slot division diagram of the UAV cluster of the present invention. The specific process is as follows: 1) Network access phase: The cluster head sends an access polling frame. After the node to be accessed receives the access polling frame, it randomly sends an access request frame, and the cluster head node sends an access confirmation frame; 2) Network transmission phase, each access node sends service data frames in turn according to the time slot allocation.

[0060] Appendix Figure 3 The figure shows the geometric schematic diagram of the UAV and interference of the present invention. Among them, e i represents the unit vector from no interference to UAV i, and e x,y represents the unit vector from UAV x to UAV y.

[0061] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of a multi-UAV collaborative anti-interference method disclosed by the present invention.

[0062] A program product includes a computer program, which is used to execute the steps of a multi-UAV collaborative anti-interference method disclosed by the present invention when it runs.

[0063] A storage medium stores a computer program, which is used to execute the steps of a multi-UAV collaborative anti-interference method disclosed by the present invention when it runs.

Claims

1. A multi-drone collaborative anti-interference method, characterized in that Here are the steps: Step 1: Each drone in the cluster is powered on and connected to the network; Step 2: Cluster head UAV M establishes the network time reference and broadcasts access polling frame; Step 3: After receiving the access polling frame, the non-cluster head UAV randomly sends an access request frame, which includes the node MAC address, UAV type and UAV location; Step 4: After receiving the access request frame, the cluster head UAV M sends an access confirmation frame, which contains the ID, MAC address and timing allocation of each UAV; Step 5: After the non-cluster head UAV receives the access confirmation frame, if it finds its own ID in it, it adjusts its own clock and goes to step 6, otherwise it goes to step 3; Step 6: According to the timing regulations, the network access phase is completed, the network switches to the transmission phase, and then goes to step 7. Otherwise, it returns to step 2. Step 7: All UAVs receive the mission planning numbers bound by the ground tracking and control station; Step 8: Each UAV flies according to the planned route in the mission planning data, extracts time domain and frequency domain information of the received abnormal signal, and sends the extracted information to the cluster head UAV M; Step 9: The cluster head UAV M receives the information sent by other UAVs and extracts the time domain and frequency domain information of the abnormal signals and the positions of each UAV; Step 10: The cluster head UAV M divides the UAVs that detect the same abnormal signal into a group according to the time-frequency characteristics of the received abnormal signal, and sorts the UAVs from large to small according to the signal strength they detect. Each UAV gets an identifier E. j i , i = 1, 2, ..., n, j = 1, 2, ..., m, where n is the number of abnormal signals and m is the total number of drones in the same group; Step 11: Let i = 1, then Select the 4 drones with the largest signal strength, t represents E i Total number of drones included; Step 12: Calculate the position precision reduction factor PDOP value; Step 13: If the PDOP value is less than the threshold δ, then select these four drones as the drones participating in the solution and go to step 14. Otherwise, go to step 14 from E i Remove these 4 drones, and select the 4 drones with the largest signal strength from the remaining drones, and go to step 12; Step 14: According to the position and time difference of the UAV selected in step 13, the passive time difference positioning method is used to determine the position of the interference source corresponding to the i-th group of abnormal signals, and its effective interference radius is estimated according to the signal attenuation model and the anti-interference ability of the UAV; Step 15: Let i=i+1, repeat steps 11 to 14 until i=n, ​​and obtain the positions and interference radius of all interference sources; Step 16: Update the interference source data in the mission planning data according to the positions and interference radius of all interference sources obtained in step 15, and re-plan the route; Step 17: The cluster head UAV sends the re-planned data to each UAV; Step 18: Each drone flies according to the new planned route.

2. The method for cooperative anti-interference of multiple unmanned aerial vehicles according to claim 1, characterized in that: The calculation formula for the position reduction factor PDOP value described in step 12 is: Where H represents the vector matrix from the interference signal to the observed UAV.

3. The method for cooperative anti-interference of multiple unmanned aerial vehicles according to claim 1, characterized in that: The value range of the threshold δ in step 13 is [4,10].

4. The method for cooperative anti-interference of multiple unmanned aerial vehicles according to claim 2, characterized in that: The vector matrix H of the interference signal to the observation drone is calculated as follows: Among them, e xy Represents the unit vector from drone x to drone y among the 4 selected drones, x=1,2,3,4, y=1,2,3.

5. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the method according to any one of claims 1 to 4 when executed by the processor.

6. A program product, characterized in that: The invention comprises a computer program, which is used to execute the steps of the method according to any one of claims 1 to 4 when the computer program is executed.

7. A storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, it is used to execute the steps of the method according to any one of claims 1 to 4.

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