A multi-dimensional pheromone-based narrow-beam search and tracking method for a UAV cluster
By combining UAV swarm collaborative search and multi-dimensional pheromone map fusion, the problem of traditional reconnaissance equipment's difficulty in detecting narrow-beam directional communication has been solved, enabling efficient search and tracking of electromagnetic targets.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- THE 54TH RESEARCH INSTITUTE OF CHINA ELECTRONICS TECHNOLOGY GROUP CORPORATION
- Filing Date
- 2023-06-27
- Publication Date
- 2026-05-15
AI Technical Summary
Traditional fixed reconnaissance equipment is difficult to effectively detect and intercept narrow-beam directional communication achieved by phased array antenna technology. Existing UAV swarms search targets are mostly visible physical objects, making it difficult to achieve efficient search and tracking of electromagnetic targets.
A multi-dimensional pheromone map construction method is adopted, and a UAV swarm collaborative search is carried out. The grid-based monitoring is carried out using attraction, repulsion and target pheromones. Combined with pheromone map fusion of local head nodes and beam fitting, the search and tracking of narrow beams can be realized.
It improves the search efficiency and tracking accuracy for narrow-beam targets, enables a more comprehensive perception of the electromagnetic environment, and achieves efficient search and tracking of narrow-beam dynamic targets.
Smart Images
Figure CN116795137B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electronic countermeasures in unmanned aerial vehicle (UAV) swarms, and in particular to a narrow-beam search and tracking method for UAV swarms based on multidimensional pheromones. Background Technology
[0002] With the development of intelligent, network, collaborative, and control technologies, various unmanned combat swarms, resembling "wolf packs," "fish schools," or "bee swarms," may emerge in all combat spaces—land, sea, air, and space—to conduct all-domain unmanned swarm attack and defense operations. Unmanned aerial vehicle (UAV) swarms, by establishing highly dynamic, scalable, and intelligent UAV networks, can enhance the adaptability of UAVs and collaboratively complete complex missions. Compared to individual UAVs, which are limited in detection capabilities and weapon payloads and struggle to complete complex combat missions, UAV swarms offer numerous advantages such as high efficiency, low cost, complementary functions, mission fault tolerance, and scalability. Through collaborative communication and interaction, they share information, expanding their environmental situational awareness and exhibiting superior coordination, intelligence, and autonomy. This significantly improves the survivability and overall combat effectiveness of UAVs, enabling collaborative task allocation, search, reconnaissance, and attack. Therefore, as a crucial technological tool, UAV swarm collaboration technology is rapidly changing future system-of-systems warfare models and the nature of warfare.
[0003] In existing technologies, advanced equipment often uses phased array antenna technology to achieve narrow beam directional communication, which has the characteristics of being covert and having strong anti-interference capabilities, making it difficult for traditional fixed reconnaissance equipment to effectively detect and intercept it. Summary of the Invention
[0004] To address the above problems, this invention proposes a narrow-beam search and tracking method for UAV swarms based on multi-dimensional pheromones. The UAV swarm, carrying distributed payloads, can comprehensively monitor electromagnetic signals over a wide area and from multiple directions. A multi-dimensional pheromone map enables information exchange and autonomous flight decisions among the UAVs, ensuring narrow-beam interception. Furthermore, a target pheromone beam fitting method is used to determine the main lobe position of the beam, and a hierarchical network of UAVs is established to fly within the beam, achieving narrow-beam search and tracking.
[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0006] A method for searching and tracking narrow beams in UAV swarms based on multidimensional pheromones is used to search and track narrow beams of data links within a certain spatial range, improving the efficiency of narrow beam search and interception. The method includes the following steps:
[0007] (1) The task space is gridded to construct a multi-dimensional pheromone map, including attracting pheromones, repelling pheromones, and target pheromones, and then attached to each UAV.
[0008] The multidimensional pheromone map is represented as follows:
[0009]
[0010] in, To attract pheromones, To repel pheromones, For the target pheromone, N x N y Let x and y be the coordinates on the gridded space, i represent the i-th drone, and t represent time.
[0011] (2) The UAV enters the collaborative search state, updates the local pheromone map according to the amplitude of the detected signal, interacts with neighboring nodes to form a local pheromone map, and elects a local head node. The local head nodes merge the pheromone maps to form a global pheromone map.
[0012] The local pheromone map update rules are as follows:
[0013]
[0014]
[0015] Where, η a μ a These are the evaporation and propagation coefficients of the attracting pheromones, respectively; D a (t), B a (t) represents the pheromone release and propagation matrix, respectively; η r μ r These are the volatilization and propagation coefficients of the repulsive pheromone, respectively; D r (t), B r (t) represents the repulsive pheromone release and propagation matrix, respectively; W is the visited state matrix, with the grids visited by the UAV assigned a value of 1 and the remaining positions assigned a value of 0; A(t) is the current signal strength detected by the UAV.
[0016] The pheromone map fusion rules are as follows:
[0017]
[0018] in, This is the pheromone map after fusion at time t. Let i and j be the local pheromone maps of drones i and j, respectively, and |·| represents the modulo operation;
[0019] (3) Each UAV makes motion decisions based on the collaborative interaction mechanism and repeats steps 2-3 to complete the collaborative search task;
[0020] (4) The local head node uses the least squares method to perform beam fitting on the target pheromone map to determine the position of the main lobe of the beam;
[0021] The beam fitting rule is the target pheromone and the fitted straight line. The overall error is minimized, and the fitting parameters are:
[0022]
[0023]
[0024] Where k = 1, 2, 3…M, M is the total number of target pheromones exceeding the threshold, and x k y k p represents the pheromone map coordinates. th A fixed threshold;
[0025] (5) Each UAV enters a collaborative tracking state, forms a pigeon flock-level network, flies towards the main lobe position of the beam, and continuously updates the target pheromone map, fits the beam orientation, and tracks the beam movement direction.
[0026] The dynamic equations of the unmanned aerial vehicle are expressed as follows:
[0027] L i (t)=L i (t-1)+h·v i (t-1)
[0028] v i (t)=v i (t-1)+h·u i (t-1)
[0029]
[0030] Among them, L i (t) represents the position of the i-th drone at time t, v i (t) represents the speed of the drone, u i (t) represents the net force acting on the drone. For attraction, It is a repulsive force. For a uniform force, h, ω a ω s ω c All are weight values.
[0031] Furthermore, in step (1), the task space is gridded to construct a multi-dimensional pheromone map, which is periodically updated to guide the narrow beam search process. The attracting pheromone is used to attract the UAV to the unsearched grid, and the repulsive pheromone is used to repel the UAV to the searched grid. The target pheromone is composed of the signal strength detected by the UAV's electronic detection payload and is used to pull the UAV to the narrow beam, so as to improve the search efficiency of the narrow beam.
[0032] Furthermore, in step (3), the drone swarm collaborative interaction mechanism specifically includes the following steps:
[0033] (301) When the UAV acquires the local status at the current moment, it makes the optimal path decision based on the pheromone map acquired at the current moment and determines the coordinate position of the UAV at the next moment.
[0034] (302) As the drone's location is updated, the drone updates the local pheromone map in real time;
[0035] (303) Set the communication range and communication constraints for UAV collaborative interaction. Once the communication conditions are met, the UAV sends the local pheromone map to the communication network and receives the pheromone maps of other UAVs to complete the pheromone map fusion, realize multi-UAV collaboration, and use this as a new local pheromone map to start the next search process until the search task is completed.
[0036] Furthermore, in step (4), the local head node performs beam fitting on the target pheromone to determine the position of the main lobe of the beam, performs straight-line fitting on the target pheromone points that exceed the threshold, and mines the beam pointing position through the symmetry of the spatial distribution of the target pheromone points.
[0037] Compared with the prior art, the present invention has the following advantages:
[0038] 1. Existing technologies mostly target visible physical objects, while this invention utilizes the maneuverability of UAV swarms and electronic reconnaissance payloads to achieve electromagnetic target search and tracking.
[0039] 2. This invention uses a swarm collaboration technology based on multidimensional pheromones, which can perceive surrounding environmental information more comprehensively, accelerate the convergence of the UAV swarm's search process for narrow beam targets, and achieve higher search efficiency; at the same time, it can continuously fit the beam orientation, and has better search and tracking efficiency for narrow beam dynamic electromagnetic targets.
[0040] 3. This invention uses beam fitting to extract beam positions from target pheromone maps. For narrow beam targets, this method is more efficient and accurate than traditional direction finding methods. Attached Figure Description
[0041] Figure 1This is a flowchart of the narrow beam search and tracking method of the present invention.
[0042] Figure 2 This is the target pheromone beam fitting diagram of the present invention.
[0043] Figure 3 This is a schematic diagram showing the relative positions of the actual beam and the fitted beam in this invention.
[0044] Figure 4 This is a schematic diagram of the narrow beam search and tracking method of the UAV of the present invention. Detailed Implementation
[0045] The present invention will now be described in further detail with reference to the accompanying drawings.
[0046] A method for cooperative search and tracking of narrow beams using a swarm of unmanned aerial vehicles (UAVs) includes: gridding the task space to construct a multi-dimensional pheromone map; UAVs entering a cooperative search state, updating their local pheromone maps, and fusing them to form a global pheromone map; local head nodes making motion decisions based on a cooperative interaction mechanism to complete the search of the task space; local head nodes performing beam fitting on the target pheromone map to determine the position of the narrow beam's main lobe; and each UAV entering a cooperative tracking state, forming a flock-like hierarchical network to fly towards the main lobe position and continuously updating the target pheromone map, tracking the beam's movement direction. The specific process is as follows: Figure 1 As shown, the steps are as follows:
[0047] (1) The task space is gridded to construct a multi-dimensional pheromone map, including attracting pheromones, repelling pheromones, and target pheromones, and then attached to each UAV.
[0048] The multidimensional pheromone map is represented as follows:
[0049]
[0050] in, To attract pheromones, To repel pheromones, For the target pheromone, N x N y Let x and y be the coordinates on the gridded space, i represent the i-th drone, and t represent time.
[0051] (2) The UAV enters the collaborative search state, updates the local pheromone map according to the amplitude of the detected signal, interacts with neighboring nodes to form a local pheromone map, and elects a local head node. The local head nodes merge the pheromone maps to form a global pheromone map.
[0052] The local pheromone map update rules are as follows:
[0053]
[0054]
[0055] Where, η a μ a These are the evaporation and propagation coefficients of the attracting pheromones, respectively; D a (t), B a (t) represents the pheromone release and propagation matrix, respectively; η r μ r These are the volatilization and propagation coefficients of the repulsive pheromone, respectively; D r (t), B r (t) represents the repulsive pheromone release and propagation matrix, respectively; W is the visited state matrix, with the grids visited by the UAV assigned a value of 1 and the remaining positions assigned a value of 0; A(t) is the current signal strength detected by the UAV.
[0056] The pheromone map fusion rules are as follows:
[0057]
[0058] in, This is the pheromone map after fusion at time t. Let i and j be the local pheromone maps of drones i and j, respectively, and |·| represents the modulo operation;
[0059] (3) Each UAV makes motion decisions based on the collaborative interaction mechanism and repeats steps 2-3 to complete the collaborative search task;
[0060] (3a) After the UAV acquires the local status at the current time, it makes the optimal path decision based on the pheromone map acquired at the current time and determines the coordinate position of the UAV at the next time.
[0061] (3b) As the drone's location is updated, the drone updates the local pheromone map in real time;
[0062] (3c) Set the communication range and communication constraints for UAV collaborative interaction. Once the communication conditions are met, the UAV sends the local pheromone map to the communication network and receives the pheromone maps of other UAVs to complete the fusion of pheromone maps, realize multi-UAV collaboration, and use this as a new local pheromone map to start the next search process until the search task is completed.
[0063] (4) The local head node uses the least squares method to perform beam fitting on the target pheromone map to determine the position of the main lobe of the beam;
[0064] The beam fitting rule is the target pheromone and the fitted straight line. The overall error is minimized, and the fitting parameters are:
[0065]
[0066]
[0067] Where k = 1, 2, 3…M, M is the total number of target pheromones exceeding the threshold, and x k y k p represents the pheromone map coordinates. th A fixed threshold;
[0068] (5) Each UAV enters a collaborative tracking state, forms a pigeon flock-level network, flies towards the main lobe position of the beam, and continuously updates the target pheromone map, fits the beam orientation, and tracks the beam movement direction.
[0069] The dynamic equations of the unmanned aerial vehicle are expressed as follows:
[0070] L i (t)=L i (t-1)+h·v i (t-1)
[0071] v i (t)=v i (t-1)+h·u i (t-1)
[0072]
[0073] Among them, L i (t) represents the position of the i-th drone at time t, v i (t) represents the speed of the drone, u i (t) represents the net force acting on the drone. For attraction, It is a repulsive force. For a uniform force, h, ω a ω s ω c All are weight values.
[0074] Attraction, repulsion, and cohesion can be specifically represented as:
[0075]
[0076]
[0077]
[0078]
[0079]
[0080]
[0081]
[0082] Where i, j = 1, 2, 3, ..., N, the cluster size is N, and d ij r represents the Euclidean distance between any two individuals in the cluster. h and r a These represent the range of repulsive force and the range of attractive force, respectively. C i W i O i These are the interaction matrix, weight matrix, and dominance matrix, used to construct matrix K. i ,symbol This indicates the multiplication of corresponding terms in a matrix.
[0083] To verify the cooperative search and tracking effect of this invention on narrow beams, a simulation verification experiment was conducted. The target pheromone beam fitting process is as follows: Figure 2 As shown in the figure, the scattered points represent the target pheromones, and the straight lines represent the fitted beams. The fitted beams are located symmetrically to the target pheromones; the relative positions of the actual beams and the fitted beams are as follows: Figure 3 As shown in the figure, the actual beam is represented by field strength, and the fitted beam is represented by a straight line. It can be seen that the positions of the fitted beam and the actual beam basically coincide. During the narrow beam search and tracking process, the UAV swarm is distributed near the actual beam, and their relative positions are as follows: Figure 4 As shown.
[0084] In summary, this invention provides guidance information for target search in UAV swarms by adjusting the pheromone concentration in the pheromone map, helping UAVs make subsequent motion decisions. At the same time, through collaborative interaction between UAVs, it can more comprehensively perceive the surrounding electromagnetic environment, explore more unknown mission areas, accelerate the convergence of the search process, and finally achieve narrow beam tracking through target pheromone beam fitting.
[0085] It should be understood that the above description of the specific embodiments of this patent is merely an exemplary description provided to facilitate understanding of the patent solution by those skilled in the art, and does not imply that the scope of protection of this patent is limited to these specific examples. Those skilled in the art can obtain more specific embodiments without any creative effort by combining technical features, replacing some technical features, adding more technical features, etc., of the various examples listed in this patent, provided that they have a full understanding of the technical solution of this patent. All of these specific embodiments are within the scope of the claims of this patent, and therefore, these new specific embodiments should also be within the scope of protection of this patent.
Claims
1. A method for narrow-beam search and tracking of UAV swarms based on multidimensional pheromones, characterized in that, To achieve narrow-beam cooperative search and tracking in drone swarm warfare, the following steps are included: (1) The task space is gridded to construct a multi-dimensional pheromone map, including attracting pheromones, repelling pheromones, and target pheromones, and then attached to each UAV. The multidimensional pheromone map is represented as follows: in, To attract pheromones, To repel pheromones, For the target pheromone, N x N y denoted as the size of the pheromone map, x and y are coordinates in the gridded space, i represents the i-th drone, and t represents time; (2) The UAV enters the collaborative search state, updates the local pheromone map according to the amplitude of the detected signal, interacts with neighboring nodes to form a local pheromone map, and elects a local head node. The local head nodes merge the pheromone maps to form a global pheromone map. The local pheromone map update rules are as follows: Where, η α μ a These are the evaporation and propagation coefficients of the attracting pheromones, respectively; D a (t), B a (t) represents the pheromone release and propagation matrix, respectively; η r μ r These are the volatilization and propagation coefficients of the repulsive pheromone, respectively; D r (t), B r (t) represents the repulsive pheromone release and propagation matrix, respectively; W is the visited state matrix, with the grids visited by the UAV assigned a value of 1 and the remaining positions assigned a value of 0; A(t) is the current signal strength detected by the UAV. The pheromone map fusion rules are as follows: in, This is the pheromone map after fusion at time t. Let i and j be the local pheromone maps of UAVs i and j, respectively, and |·| represents the modulo operation; (3) Each UAV makes motion decisions based on the collaborative interaction mechanism and repeats steps 2-3 to complete the collaborative search task; (4) The local head node uses the least squares method to perform beam fitting on the target pheromone map to determine the position of the main lobe of the beam; The beam fitting rule is the target pheromone and the fitted straight line. The overall error is minimized, and the fitting parameters are: Where k = 1, 2, 3…M, M is the total number of target pheromones exceeding the threshold, and x k ,y k p represents the pheromone map coordinates. th A fixed threshold; (5) Each UAV enters a collaborative tracking state, forms a pigeon flock-level network, flies towards the main lobe position of the beam, and continuously updates the target pheromone map, fits the beam orientation, and tracks the beam movement direction. The dynamic equations of the unmanned aerial vehicle are expressed as follows: L i (t)=L i (t-1)+h·v i (t-1) v i (t)=v i (t-1)+h·u i (t-1) Among them, L i (t) represents the position of the i-th drone at time t, v i (t) represents the speed of the drone, u i (t) represents the net force acting on the drone. For attraction, It is a repulsive force. For a uniform force, h,ω a ,ω s ,ω c All are weight values.
2. The method for narrow-beam search and tracking of UAV swarms based on multidimensional pheromones according to claim 1, characterized in that, In step (1), the task space is gridded to construct a multi-dimensional pheromone map, which is periodically updated to guide the narrow beam search process. The attracting pheromone is used to attract the UAV to the unsearched grid, and the repulsive pheromone is used to repel the UAV to the searched grid. The target pheromone is composed of the signal strength detected by the UAV's electronic detection payload and is used to pull the UAV to the narrow beam to improve the search efficiency of the narrow beam.
3. The method for narrow-beam search and tracking of UAV swarms based on multidimensional pheromones according to claim 1, characterized in that, In step (3), the drone swarm collaborative interaction mechanism specifically includes the following steps: (301) When the UAV acquires the local status at the current moment, it makes the optimal path decision based on the pheromone map acquired at the current moment and determines the coordinate position of the UAV at the next moment. (302) As the drone's location is updated, the drone updates the local pheromone map in real time; (303) Set the communication range and communication constraints for UAV collaborative interaction. Once the communication conditions are met, the UAV sends the local pheromone map to the communication network and receives the pheromone maps of other UAVs to complete the fusion of pheromone maps, realize multi-UAV collaboration, and use this as a new local pheromone map to start the next search process until the search task is completed.
4. The method for narrow-beam search and tracking of UAV swarms based on multidimensional pheromones according to claim 1, characterized in that, In step (4), the local head node performs beam fitting on the target pheromone to determine the position of the main lobe of the beam, performs straight-line fitting on the target pheromone points that exceed the threshold, and mines the beam pointing position through the symmetry of the spatial distribution of the target pheromone points.
5. The method for narrow-beam search and tracking of UAV swarms based on multidimensional pheromones according to claim 1, characterized in that, In step (5), when the beam moves, the UAV swarm forms a pigeon flock hierarchical network, continuously and synchronously updates the target pheromone map, fits the beam, obtains the beam movement direction, and realizes beam tracking.