Fixed-wing unmanned aerial vehicle cooperative area coverage monitoring optimization deployment method

Abstract

The invention provides a fixed-wing unmanned aerial vehicle cooperative area coverage monitoring optimization deployment method. The method comprises the steps of determining an effective coverage area of multi-unmanned aerial vehicle combined area coverage monitoring according to a space detection probability of an unmanned aerial vehicle to a target and a time monitoring probability generated by operation of the unmanned aerial vehicle; determining deployment parameters of an effective coverage area according to two-machine parallel and cooperative area coverage monitoring; and according to the deployment parameters of the effective coverage area, establishing a gridding multi-machine collaborative area coverage monitoring deployment scheme taking a square as a unit. According to the invention, through the space detection probability and the time monitoring probability, the effective coverage area of multi-aircraft joint area coverage monitoring can be determined more comprehensively and accurately, theoretical support is provided for flexible application of the multi-fixed-wing unmanned aerial vehicle in area coverage monitoring scenes such as border patrol and disaster rescue, and a multi-aircraft cooperative area coverage monitoring deployment scheme has the advantages of high efficiency and high reliability. The effective monitoring area is increased, the number of used unmanned aerial vehicles is reduced, and the cooperative efficiency of the unmanned aerial vehicle cluster is effectively improved.

Description

Technical Field

[0001] This application relates to the field of electronic information countermeasures, specifically to a method for optimizing the deployment of fixed-wing unmanned aerial vehicles (UAVs) in collaborative area coverage surveillance. Background Technology

[0002] As a strategic supporting element for new infrastructure construction, unmanned aerial vehicle (UAV) swarms demonstrate multi-dimensional dynamic perception advantages in low-altitude economic fields such as disaster relief, environmental monitoring, and emergency communications. Their wide-area monitoring capabilities are a core functional module of low-altitude intelligent sensing systems. Existing research often models regional coverage as a static spatial coverage problem, achieving periodic single scans through trajectory planning. However, such methods exhibit limited spatiotemporal coverage capabilities in high-risk scenarios such as disaster area exploration and border intrusion early warning, failing to meet the timeliness requirements of dynamic threat early warning and creating the dual risks of monitoring blind spots and response delays. Traditional single-tracking methods have limitations and cannot meet the needs of modern warfare. Multi-UAV cooperative guidance can improve the acquisition probability and reduce the performance requirements of individual UAVs. Therefore, researching multi-UAV cooperative deployment strategies to achieve continuous dynamic monitoring of sensitive mission areas has significant engineering value.

[0003] To address the issue of area coverage monitoring using multiple fixed-wing UAVs hovering in flight, existing technologies have designed square / hexagonal deployment schemes based on the radius of the hovering circle. Subsequent research has further proposed a heterogeneous coverage strategy with a layered square layout. However, all of these approaches rely on the assumption of superimposed strong zones for individual UAVs and have not effectively explored the gain effect of multi-UAV collaborative detection. In fact, the actual detection probability is non-linearly related to distance, and the probability of a target point being detected should be characterized by the joint detection probability of multiple UAVs. These studies all rely on the strong zones of individual UAVs and have not fully utilized the collaborative detection capabilities among UAVs.

[0004] In addition, while some studies have established joint detection probabilities for dual-aircraft cooperative scenarios, confirming that the cooperative coverage exceeds the detection limit of a single aircraft and providing optimal spacing configuration conditions, their research is limited to dual-aircraft systems. They have neither analyzed the quantitative relationship between flight radius and coverage effectiveness nor extended it to the practical deployment and application of multi-aircraft cooperative systems.

[0005] Therefore, it is necessary to propose a solution to improve one or more problems existing in the above-mentioned related technical solutions.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this disclosure is to provide a method for optimizing the deployment of fixed-wing UAV-assisted regional coverage surveillance, thereby overcoming, at least to some extent, one or more problems caused by the limitations and defects of related technologies.

[0008] A method for optimizing the deployment of fixed-wing UAV collaborative area coverage surveillance according to an embodiment of this disclosure includes the following steps:

[0009] The effective coverage area of ​​multi-drone joint regional coverage surveillance is determined based on the spatial detection probability of the UAV to the target and the temporal monitoring probability generated by the UAV operation. The spatial detection probability is the detection probability of the UAV detection payload to the target point, and the temporal monitoring probability is the ratio of the time when the target point is effectively detected to the UAV's cruise cycle.

[0010] Based on the regional coverage monitoring of the two aircraft operating in parallel and in coordination, the deployment parameters of the effective coverage area are determined. The deployment parameters include the radius of the hovering circle, the distance between the two aircraft, the initial phase difference, and the flight direction.

[0011] Based on the deployment parameters of the effective coverage area, a gridded multi-machine collaborative regional coverage monitoring deployment scheme with square units is established.

[0012] In an exemplary embodiment of this application, the step of determining the effective coverage area of ​​multi-drone joint area coverage surveillance based on the spatial detection probability of the UAV to the target and the temporal surveillance probability generated by the UAV's operation, wherein the spatial detection probability is the detection probability of the UAV's detection payload to the target point, and the temporal surveillance probability is the ratio of the time when the target point is effectively detected to the UAV's cruise cycle, includes:

[0013] The probability of single-drone detection is determined based on the horizontal distance between the target point and the drone;

[0014] Calculate the joint spatial detection probability of multiple drones at any time. Based on the detection probability of a single drone, calculate the joint detection probability of multiple drones at a certain time. Set a spatial detection probability threshold. When the joint detection probability reaches or exceeds the threshold, it is determined that the target point has been effectively detected at that time.

[0015] The time surveillance probability of a target point is calculated by dividing the cumulative time the target point is effectively detected within a cruise cycle by the cruise cycle.

[0016] The effective coverage area is determined, and a time monitoring probability threshold is set. Each target point in the monitoring area is included in the effective coverage area if its time monitoring probability reaches or exceeds the threshold. All the target points that meet the criteria constitute the effective coverage area of ​​the multi-aircraft joint regional coverage monitoring.

[0017] In an exemplary embodiment of this application, the single-machine detection probability can be expressed as:

[0018]

[0019] Where d is the horizontal distance between the UAV and the target point, f(d) is a monotonically decreasing function of d, and f(d) min )=1,f(d max ) = 0.

[0020] In an exemplary embodiment of this application, the expression for the joint detection probability is:

[0021]

[0022] Where, d kt Let be the distance between the k-th UAV and the target point V at time t, and n be the number of UAVs. In an exemplary embodiment of this application, the expression for the effective coverage area is:

[0023] S eff ={A|η(V)≥η c}

[0024] Where η(V) is the ratio of the time it takes for target point V to be effectively detected to the cruise cycle, η c denoted as the time-based monitoring probability threshold, and A as the area to be monitored.

[0025] In an exemplary embodiment of this application, the step of determining deployment parameters for an effective coverage area based on the area coverage monitoring of two aircraft operating in parallel and in coordination, wherein the deployment parameters include the radius of the hovering circle, the distance between the two aircraft, the initial phase difference, and the flight direction, includes:

[0026] An optimization algorithm based on simulation iteration is used to determine the optimal initial phase difference and flight direction for two-aircraft coordination.

[0027] The radius of the hovering circle is determined. Based on the optimal initial phase difference and flight direction of the two aircraft working together, the golden section search algorithm combined with the inscribed rectangle optimization method is used to determine the preset optimal distance between the two aircraft that maximizes the area of ​​the inscribed rectangle when the two aircraft are working together side by side.

[0028] In an exemplary embodiment of this application, the simulation iterative optimization algorithm includes the following steps:

[0029] Construct a monitoring area with a preset side length, and rasterize the monitoring area with a preset precision, placing the center of the two-machine rotating circle in the middle of the area;

[0030] Set the angle change range, traverse all angle positions within the drone's flight cycle, and calculate the position coordinates of the two drones at different times;

[0031] The detection probability of each grid point at different times is calculated based on the joint detection probability, and the percentage of time that meets the detection probability threshold is statistically analyzed to determine the effective coverage area.

[0032] Iteratively compare the effective coverage area under different initial phase differences and flight directions until the initial phase difference and flight direction corresponding to the maximum effective coverage area are obtained.

[0033] In an exemplary embodiment of this application, the steps of the inscribed rectangle optimization method include:

[0034] Determine the radii of the hovering circles of the two aircraft, establish a coordinate system, place the hovering circles of the two aircraft on the x-axis, set the initial phase difference and flight direction corresponding to the maximum effective coverage area of ​​the two aircraft working together, and determine the boundary points of the effective coverage area.

[0035] Iterate through all boundary points, construct an inscribed rectangle with each point as a vertex, and calculate the area of ​​the rectangle;

[0036] The golden section search algorithm is used to find the inscribed rectangle with the largest area, and the corresponding distance between the two rectangles is the preset optimal value.

[0037] In an exemplary embodiment of this application, the initial phase difference corresponding to the maximum effective coverage area is π, and the two drones corresponding to the maximum effective coverage area are flying in the same direction; the hovering radius of the drone is the minimum turning radius, and the distance between the two drones is a preset optimal value d. opt , of which 2r <d opt ≤2d min r is the radius of the spiral circle, d min It is the minimum spacing between the spiral circles.

[0038] In an exemplary embodiment of this application, the step of establishing a gridded multi-machine collaborative regional coverage monitoring deployment scheme with square units based on the deployment parameters of the effective coverage area includes:

[0039] Based on the deployment parameters of two-machine collaboration, the side length L of the square corresponding to the maximum coverable 2:1 rectangle is calculated using an improved golden ratio algorithm, which serves as the smallest unit for multi-machine deployment.

[0040] The large area to be monitored is divided into multiple strip-shaped areas with a width of 2L. Multiple drones are deployed in each strip-shaped area, and the drones are deployed in a parallel and collaborative manner to achieve full coverage of the entire area through multiple deployments.

[0041] Adjacent drones maintain flight in the same direction, with an initial position phase difference of π, a hovering circle radius equal to the minimum turning radius, and a hovering circle spacing of the preset optimal value.

[0042] This application proposes an optimized deployment method for coordinated regional coverage surveillance using fixed-wing UAVs. On the one hand, by utilizing the spatial detection probability of UAVs targeting targets and the temporal surveillance probability generated by UAV operations, the effective coverage area of ​​multi-UAV joint regional coverage surveillance can be determined more comprehensively and accurately, providing theoretical support for the flexible application of multiple fixed-wing UAVs in regional coverage surveillance scenarios such as border patrol and disaster relief. On the other hand, by using two UAVs to conduct coordinated regional coverage monitoring, the deployment parameters of the effective coverage area are determined. Based on the deployment parameters of the effective coverage area, a gridded multi-UAV coordinated regional coverage monitoring deployment scheme with square units is established, which increases the effective surveillance area, reduces the number of UAVs used, and effectively improves the collaborative efficiency of UAV swarms. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0044] Figure 1 This illustration shows a schematic diagram of the steps in the fixed-wing UAV cooperative area coverage monitoring optimization deployment method in an exemplary embodiment of this application;

[0045] Figure 2 This diagram illustrates the instantaneous effective detection area for a single-machine and two-machine setups in an exemplary embodiment of this application.

[0046] Figure 3 This illustration shows a schematic diagram of multi-machine collaborative area monitoring in an exemplary embodiment of this application;

[0047] Figure 4 This diagram illustrates a dual-machine parallel collaborative regional monitoring deployment in an exemplary embodiment of this application.

[0048] Figure 5 This diagram illustrates the relationship between the phase angle and coverage area of ​​two parallel-mounted units in an exemplary embodiment of this application.

[0049] Figure 6 This diagram illustrates the optimal cooperative deployment of two aircraft flying in the same direction and in opposite directions in an exemplary embodiment of this application.

[0050] Figure 7 This diagram illustrates the relationship between the radius of the spiral circle and the area of ​​the effective coverage area in an exemplary embodiment of this application.

[0051] Figure 8 This diagram illustrates the relationship between the hovering circle distance and the effective coverage area in an exemplary embodiment of this application.

[0052] Figure 9 This diagram illustrates the change in the shape of the effective coverage area after the distance of the spiral circle increases in an exemplary embodiment of this application.

[0053] Figure 10 This diagram illustrates a rectangular inscribed area within the effective coverage zone of the two-machine cooperative system in an exemplary embodiment of this application.

[0054] Figure 11 This diagram illustrates a multi-machine collaborative regional coverage monitoring deployment in an exemplary embodiment of this application.

[0055] Figure 12 This diagram illustrates the relationship between the detection probability threshold and the area coverage in an exemplary embodiment of this application.

[0056] Figure 13 This diagram illustrates the effective monitoring zone for two-machine collaborative surveillance in an exemplary embodiment of this application.

[0057] Figure 14 This diagram illustrates the effective coverage area of ​​the two-machine collaborative monitoring system in an exemplary embodiment of this application.

[0058] Figure 15 This diagram illustrates a three-machine collaborative area coverage surveillance deployment in an exemplary embodiment of this application. Detailed Implementation

[0059] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0060] Furthermore, the accompanying drawings are merely illustrative of this application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0061] This example implementation provides a method for optimizing the deployment of fixed-wing UAVs for collaborative area coverage surveillance, such as... Figure 1 As shown, the following steps may be included:

[0062] Step S101: Determine the effective coverage area of ​​the multi-drone joint regional coverage surveillance based on the spatial detection probability of the UAV to the target and the temporal monitoring probability generated by the UAV operation, wherein the spatial detection probability is the detection probability of the UAV detection payload to the target point, and the temporal monitoring probability is the ratio of the time when the target point is effectively detected to the UAV cruise cycle.

[0063] Step S102: Based on the regional coverage monitoring of the two aircraft working together, determine the deployment parameters of the effective coverage area. The deployment parameters include the radius of the hovering circle, the distance between the two aircraft, the initial phase difference, and the flight direction.

[0064] Step S103: Based on the deployment parameters of the effective coverage area, establish a gridded multi-machine collaborative regional coverage monitoring deployment scheme with square units.

[0065] This application proposes a method for optimizing the deployment of fixed-wing UAV collaborative area coverage surveillance. On the one hand, by utilizing the spatial detection probability of UAVs to targets and the temporal surveillance probability generated by UAV operation, the effective coverage area of ​​multi-UAV joint area coverage surveillance can be determined more comprehensively and accurately, providing theoretical support for the flexible application of multiple fixed-wing UAVs in area coverage surveillance scenarios such as border patrol and disaster relief. On the other hand, by using two UAVs to conduct collaborative area coverage monitoring, the deployment parameters of the effective coverage area are determined. Based on the deployment parameters of the effective coverage area, a gridded multi-UAV collaborative area coverage monitoring deployment scheme with square units is established, which increases the effective surveillance area, reduces the number of UAVs used, and effectively improves the collaborative efficiency of UAV swarms.

[0066] Below, as Figures 1-3 As shown, a more detailed explanation will be given of the fixed-wing UAV cooperative area coverage surveillance optimization deployment method proposed in this example embodiment.

[0067] Step S101: Determine the effective coverage area of ​​the multi-drone joint regional coverage surveillance based on the spatial detection probability of the UAV to the target and the temporal monitoring probability generated by the UAV operation, wherein the spatial detection probability is the detection probability of the UAV detection payload to the target point, and the temporal monitoring probability is the ratio of the time when the target point is effectively detected to the UAV cruise cycle.

[0068] Specifically, step S101 includes the following sub-steps:

[0069] Step S1001: Determine the single-machine detection probability based on the horizontal distance between the target point and the UAV.

[0070] Specifically, UAVs monitor areas by carrying various types of detection payloads (such as radar, electro-optical, and infrared). The probability of these payloads detecting a target decreases as the detection distance increases. Assuming the UAV cruises at a fixed altitude and the horizontal distance between the target and the UAV is d, the single-UAV detection probability can be expressed as:

[0071]

[0072] Where f(d) is a monotonically decreasing function of d, and f(d) min )=1,f(d max ) = 0.

[0073] Step S1002: Calculate the joint spatial detection probability of multiple drones at any time. Specifically, based on the detection probability of a single drone, calculate the joint detection probability of multiple drones at a certain time, set a spatial detection probability threshold, and determine that the target point has been effectively detected at that time when the joint detection probability reaches or exceeds the threshold.

[0074] Specifically, at time t, when n UAVs of the same type jointly detect target point V, the detection probability is:

[0075]

[0076] Where, d kt Let be the distance between the k-th UAV and the target point V at time t, and n be the number of UAVs.

[0077] In practical applications, to meet task requirements, it is assumed that the detection probability must be greater than a set threshold p. c (e.g. p) c =95%). For single-machine instantaneous detection, based on the detection probability threshold, a circle with radius d centered on the UAV can be determined. c The effective detection area is the region within the circle; for multi-machine joint instantaneous detection, the effective area is the region with a joint detection probability greater than p. c The area. Figure 2 The instantaneous effective detection area is shown in single-machine and two-level joint monitoring scenarios. The dark area represents the area with a detection probability of 1, and the light area represents the area with a detection probability greater than the threshold p. c The effective detection area.

[0078] Step S1003: Calculate the time surveillance probability of the target point. In one cruise cycle, the cumulative time that the target point is effectively detected is counted. The ratio obtained by dividing the cumulative time by the cruise cycle is the time surveillance probability.

[0079] Step S1004: Determine the effective coverage area, set the time monitoring probability threshold, monitor each target point in the area, and if its time monitoring probability reaches or exceeds the threshold, the point is included in the effective coverage area. All the target points that meet the criteria constitute the effective coverage area of ​​the multi-aircraft joint area coverage monitoring.

[0080] Specifically, when conducting area coverage monitoring, it is assumed that a fixed-wing UAV patrols the monitored area by periodically flying at a constant speed along a circular flight path. Multi-UAV collaborative monitoring can increase the effectiveness of area coverage monitoring; this is typically achieved through multiple UAVs operating in parallel and in coordination, such as... Figure 3 As shown.

[0081] During a drone's flight, the region consisting of points that remain within the effective detection range throughout the entire flight is called the strong-effect region; the region consisting of points that are within the detection range for part of the time is called the weak-effect region. In practical applications, the proportion of time that some points are detectable, η, may exceed a set threshold η. c (e.g. η) c Even when coverage is ≥98%, it still meets the coverage monitoring requirements. Based on this, the effective coverage area is defined as...

[0082] S eff ={A|η(V)≥η c} (3)

[0083] Where η(V) is the ratio of the time it takes for the target point V to be effectively detected to the cruise cycle, η c denoted as the time-based monitoring probability threshold, and A as the area to be monitored.

[0084] It needs to be understood that when a single drone flies along a circular trajectory, the effective coverage area S eff The area decreases significantly with increasing flight trajectory radius. Under joint detection probability, multi-aircraft collaborative monitoring can increase regional coverage. Calculating the effective coverage area under multi-aircraft collaborative methods is key to achieving regional coverage monitoring.

[0085] Step S102: Based on the regional coverage monitoring of the two aircraft working together, determine the deployment parameters of the effective coverage area. The deployment parameters include the radius of the hovering circle, the distance between the two aircraft, the initial phase difference, and the flight direction.

[0086] Specifically, when two cameras work together to monitor coverage of an area (e.g.) Figure 3 (b) As shown, due to the change in the relative position of the UAV during flight, the covered area is an irregular shape, and its shape and area are affected by the radius of the UAV's hovering circle, the distance between the hovering circles, the initial position of the two aircraft, and the flight direction.

[0087] Assume two identical fixed-wing UAVs both cruise using hovering circles of radius r, and are symmetrically placed on the x-axis. Let the distance between the hovering circles be d = |O2O2|. Figure 4 As shown. Assume the first drone A starts from a position with phase 0 at the center and flies counter-clockwise. The initial position and phase angle of the other drone B are... Flight direction F dir = ±1, where 1 indicates that the two aircraft are flying in the same direction, and -1 indicates that the two aircraft are flying in opposite directions. Therefore, the two aircraft can be configured to fly side by side in a coordinated manner using a quaternion. Describe it.

[0088] Based on the dual-aircraft parallel and collaborative configuration, and with maximizing the effective coverage area as the objective function, the following mathematical model is established:

[0089]

[0090] Wherein, the effective coverage range S of the objective function eff As defined in equation (3), d max By definition in equation (1), r c Let be the minimum turning radius of the fixed-wing UAV. The first constraint requires that the radius of the hovering circle cannot be less than the minimum turning radius of the UAV; if it is too large, it may lead to a blind spot in the middle. The second constraint limits the distance d between the hovering circles; if it is too large, the two UAVs cannot coordinate.

[0091] To design the optimal two-machine deployment, the deployment parameters for the effective coverage area are determined below.

[0092] Step S102 includes the following sub-steps:

[0093] Step S2001: Based on the simulation iteration optimization algorithm, determine the optimal initial phase difference and flight direction for the two aircraft to cooperate.

[0094] Specifically, in a two-aircraft parallel cooperative scenario, when the radius and center-to-center distance of the hovering circle are determined, the initial positions and directions of the two aircraft on the hovering circle determine their subsequent flight trajectories. Based on the trajectories of the two aircraft, equation (3) can be used to determine whether the target point V belongs to the effective coverage area, where the effective coverage of V is actually...

[0095]

[0096] p(V,t) is defined by equation (1), u is a unit step function, and the variable is a positive number with a value of 1, otherwise it is 0.

[0097] It is difficult to give an effective coverage area S based on equation (3). eff The explicit expression is given. Therefore, for a given configuration of both machines, a simulation-based algorithm is used to calculate the area of ​​the effective coverage area, as shown in Algorithm 1.

[0098]

[0099] The simulation-based algorithm calculates the area of ​​the effective coverage zone, including the following steps:

[0100] Construct a monitoring area with a preset side length, and rasterize the monitoring area with a preset precision, placing the center of the two-machine rotating circle in the middle of the area;

[0101] Set the angle change range, traverse all angle positions within the drone's flight cycle, and calculate the position coordinates of the two drones at different times;

[0102] The detection probability of each grid point at different times is calculated based on the joint detection probability, and the percentage of time that meets the detection probability threshold is statistically analyzed to determine the effective coverage area.

[0103] Iteratively compare the effective coverage area under different initial phase differences and flight directions until the initial phase difference and flight direction corresponding to the maximum effective coverage area are obtained.

[0104] The algorithm described above calculates the effective coverage area of ​​two drones deployed in parallel and coordinated configurations. Based on this algorithm, the optimal configuration of the initial position and orientation of the drones can be calculated after the positions of the two circles are determined.

[0105] In the single-machine detection probability formula (1), d min =100, d max =200, f(d) is a linear function, p c and η c In scenarios where the values ​​are 95% and 98% respectively, assuming the two spiraling circles have a radius of 20km and a center distance of 60km, Figure 5 The relationship between the phase angle difference and the effective coverage area is given when two aircraft are flying side by side in a circular pattern and in opposite directions.

[0106] The results showed that when the two aircraft were flying in the same direction, their phase difference was... At that time, the effective coverage area of ​​collaborative monitoring is the largest, such as Figure 6 As shown in the left figure; when the two aircraft fly in opposite directions and their phase difference is 0, the effective coverage area of ​​the collaborative monitoring is the largest, as shown in the left figure. Figure 6 As shown in the right figure.

[0107] In summary, given the circular positions of the two aircraft, when they are flying in the same direction, the phase difference... The synergistic effect is best when this is achieved.

[0108] Step S2002: Determine the radius of the hovering circle. Based on the optimal initial phase difference and flight direction of the two aircraft working together, use the golden section search algorithm combined with the inscribed rectangle optimization method to determine the preset optimal distance between the two aircraft that maximizes the area of ​​the inscribed rectangle when the two aircraft are working together side by side.

[0109] Specifically, the two aircraft will circle and fly in a coordinated manner to cover the area, deployed according to the principle that the two aircraft are flying in the same direction and their phase angles differ by π. For example... Figure 7 As shown, when the distance between the spiral circles is given, the area of ​​the effective coverage area decreases monotonically as the radius of the spiral circles increases.

[0110] Depend on Figure 7 It can be seen that when the radius of the spiral circle is given, there exists an optimal spiral circle spacing that maximizes the effective coverage area.

[0111] Assume the radius of the hovering circle is smaller than the detection radius. If the two circles are close together, their effective coverage areas largely overlap, resulting in a smaller effective coverage area when they work together. If the two circles are far apart and the two cameras do not work together, the total effective coverage area is twice that of a single camera, which is clearly smaller than in a scenario with collaboration. Figure 8 As shown, there is a suitable distance between the spiraling circles to maximize the effective coverage area when the two machines work together.

[0112] like Figure 9 As shown, the distance d = |O2O2| in the spiral circle is in the range (2r, 2d). min There exists an optimal distance within a certain range that maximizes the effective coverage area of ​​the two-aircraft collaboration, where r is the radius of the hovering circle and d... min As defined in equation (1). Based on the unimodality of the relationship function between the spiral circle distance and the effective coverage area, the golden section search algorithm can be designed based on Algorithm 1 to quickly solve for its optimal value.

[0113] As the spacing between the spiral circles increases, the shape of the covered area changes from approximately elliptical to approximately the intersection of two circles. For example... Figure 10 As shown, a depression appears in the middle of the largest effective coverage area, which causes inconvenience for actual use of area coverage.

[0114] In practical applications, to facilitate multi-machine collaborative coverage of large areas, the inscribed rectangle of the effective coverage area is typically taken as the actual coverage range. For example... Figure 10 The maximum effective coverage area shown may not necessarily have the largest inscribed rectangle area due to the central depression. Therefore, determining the appropriate spacing between the spiral circles to maximize the inscribed rectangle area becomes crucial.

[0115] When two aircraft fly in the same direction and their phase angles differ by π, the effective coverage area is axially symmetrical vertically (e.g., Figure 10 (As shown). Therefore, by specifying a boundary point of the effective coverage area and constructing an inscribed rectangle from that boundary point, the corresponding rectangle can be obtained. Thus, by traversing the boundary points of the effective coverage area, the inscribed rectangle with the largest area can be obtained. To this end, Algorithm 2 is designed to calculate the maximum inscribed rectangle for two-machine parallel collaboration.

[0116]

[0117] The algorithm described above solves for the maximum area inscribed rectangle given the radius r of the two spiral circles and the distance d between them. Based on this algorithm, a golden section search algorithm can be designed to solve for the optimal spiral circle distance d. opt , of which 2r <d opt ≤2d min .

[0118] Based on the above algorithm, the hovering radius of the UAV is the minimum turning radius r. c The spacing is d opt When two drones fly in the same direction and the initial phase difference between their positions is π, the area of ​​the inscribed rectangle of the effective coverage area is the largest.

[0119] In practical applications, if the length or width of the rectangle is known, the above algorithm Algorithm 2 can be modified to determine whether the two machines can cover the rectangle and provide a suitable spacing between the spiral circles.

[0120] Step S103: Based on the deployment parameters of the effective coverage area, establish a gridded multi-machine collaborative regional coverage monitoring deployment scheme with square units.

[0121] Specifically, based on the optimal deployment of two-aircraft collaboration, when the coverage area is a narrow and elongated terrain or a small area, the target area can be appropriately divided, and multi-aircraft serial coverage can be carried out in groups of two.

[0122] When the area is large, to simplify the deployment strategy, coverage can be achieved in square units, where the size of the square is half the area covered by the two machines working together. For example... Figure 10 As shown, if the inscribed rectangle is a combination of two squares, then multi-drone collaborative area coverage can be achieved with squares as the unit, and the number of squares increases by one for each additional drone.

[0123] Step S3001: Based on the deployment parameters of the two-machine collaboration, the side length L of the square corresponding to the maximum coverable 2:1 rectangle is calculated using the improved golden ratio algorithm, which serves as the smallest unit for multi-machine deployment.

[0124] Specifically, Step 5 of Algorithm 2 is modified to construct a maximum rectangle with an aspect ratio of 2:1 within the effective coverage area based on the boundary points. This allows for the calculation of the maximum 2:1 coverage rectangle given the radius r of the hovering circle and the distance d between the two hovering circles, thus obtaining the side length of the square. Based on this, the minimum turning radius of the UAV is set to r. c Using the radius of the spiral circle as a variable, the following algorithm is designed:

[0125]

[0126] Based on Algorithm3, the optimal value of the spiral circle spacing d and the square side length L(d) can be obtained, and these can be used as the smallest coverage unit for multi-machine deployment.

[0127] Step S3002: Divide the large area to be monitored into multiple strip areas with a width of 2L. Deploy multiple drones in each strip area in a parallel and collaborative manner to achieve full coverage of the entire area through multiple deployments.

[0128] Specifically, for large-area monitoring coverage areas, the area is first divided into multiple strip-shaped regions with a width L(d). Each strip-shaped region is then deployed for regional monitoring coverage using a multi-camera parallel collaborative approach. For example... Figure 11 As shown, four drones operate in parallel and coordinate with each other. Adjacent drones fly in the same direction, with a phase difference of π at their starting positions and a circling distance of d. When n drones operate in parallel and coordinate, they can effectively cover a rectangular area with a width of L(d) and a length of (n-1)d+(2L-d).

[0129] To evaluate the effectiveness of the proposed method, simulations were used to verify the optimized deployment method of fixed-wing UAV collaborative area coverage surveillance and compared it with traditional high-efficiency area coverage.

[0130] Assuming a certain type of fixed-wing UAV has a flight speed of 100 m / s and a minimum turning radius of 2 km, its detection capability is defined by equation (1), where d min =10, d max =20, f(d) = 2 - d / 10. Assume the detection probability threshold p c =90%, when monitoring an area, the time during which any location is not effectively detected must not exceed T. b Second.

[0131] Verification of the coordinated area monitoring range of the two aircraft showed that, based on the minimum turning radius and flight speed, the time taken for the UAV to complete one rotation was calculated to be 125.6 seconds. (By T) b The effective detection time threshold can be calculated as η. c =1-T b / 125.6.

[0132] When two aircraft fly in the same direction and are π out of phase, their joint surveillance range is maximized. Algorithm 3 is used to find a suitable hovering circle spacing that maximizes the area of ​​the inscribed 2:1 rectangle within the effective coverage area of ​​the two aircraft. For T... b Table 1 shows the short side length L of the maximum 2:1 rectangle and its corresponding spiral circle spacing d for scenarios with different detection probability thresholds of 100% and 90%.

[0133] Table 1. Scope of Joint Monitoring Between Two Aircraft

[0134]

[0135] As shown in Table 1, given the detection threshold p c When the effective detection time threshold is smaller (T), b The larger the value of d, the larger the distance d between the two hovering circles under optimal deployment, and the larger the area of ​​the inscribed 2:1 rectangle.

[0136] Under the optimal deployment strategy, the effective coverage area is determined by the detection threshold p. c and joint detection probability threshold p c Sure. Figure 12 The probability threshold p for joint detection by two aircraft under three time thresholds is given. c The relationship between the detection probability threshold and the effective coverage area inscribed in a 2:1 rectangle is shown. The results indicate that, compared to the time threshold, the detection probability threshold has a more significant impact on the detection range.

[0137] When p c and η c When both are 100%, the effective monitoring range is the traditional high-efficiency zone, such as... Figure 13 As shown in Table 1, the 2:1 rectangle within the strong region is the smallest. Figure 14 p was shown c =90%, η c =68.1% (corresponding to T) b The effective coverage area of ​​the two-aircraft collaborative monitoring system at a ratio of 40) and its maximum 2:1 rectangle (short side length is 15.6km).

[0138] Assuming the area to be monitored is 50km × Within a 30km rectangular area, the time any location remains undetected for no more than 40 seconds. The following compares the effectiveness of traditional strong-area coverage with the effective-area coverage proposed in this application.

[0139] When using traditional strong-area coverage, the inscribed square of each drone's coverage area is 11.6 km (see calculation results in Table 1). Therefore, covering the surveillance area requires 15 drones arranged in three rows and five columns. Figure 15 As shown in the left figure. Using the strategy proposed in this paper, when two adjacent UAVs cooperate, the effective coverage area is an inscribed 2:1 rectangle with a short side of 15.6 km and a long side of 31.2 km, and the corresponding hovering circle distance between the two UAVs is 19.4. Therefore, six UAVs are needed in two rows and three columns, with adjacent UAVs in each row flying in the same direction and with a phase difference of π, as shown in the left figure. Figure 15 As shown in the right figure. In contrast, by using detection probability, with a square side length of 15.6 km, the effective surveillance area contributed by a single drone increased by 80.8%, the number of drones used decreased by 60%, and the collaborative efficiency of the cluster was effectively improved.

[0140] Since the technical solution of this application does not involve specific working or usage processes, there is no need to output related descriptions of working or usage processes. The above-described implementation modules fully elaborate on the technical solution of this application in terms of technology, principles, effects, and alternative solutions, covering the entire process from model construction and boundary condition setting to simulation calculation result analysis, providing comprehensive guidance for understanding and applying this invention.

[0141] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0142] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0143] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

Claims

1. A method for optimizing the deployment of fixed-wing unmanned aerial vehicles (UAVs) for collaborative area coverage surveillance, characterized in that, Includes the following steps: The effective coverage area of ​​multi-drone joint regional coverage surveillance is determined based on the spatial detection probability of the UAV to the target and the temporal monitoring probability generated by the UAV operation. The spatial detection probability is the detection probability of the UAV detection payload to the target point, and the temporal monitoring probability is the ratio of the time when the target point is effectively detected to the UAV's cruise cycle. Based on the regional coverage monitoring of the two aircraft operating in parallel and in coordination, the deployment parameters of the effective coverage area are determined. The deployment parameters include the radius of the hovering circle, the distance between the two aircraft, the initial phase difference, and the flight direction. Based on the deployment parameters of the effective coverage area, a gridded multi-machine collaborative regional coverage monitoring deployment scheme with square units is established.

2. The method for optimized deployment of fixed-wing UAV collaborative area coverage surveillance according to claim 1, characterized in that, The step of determining the effective coverage area of ​​multi-drone joint regional coverage surveillance based on the spatial detection probability of the UAV to the target and the temporal surveillance probability generated by the UAV's operation, wherein the spatial detection probability is the detection probability of the UAV's detection payload to the target point, and the temporal surveillance probability is the ratio of the time when the target point is effectively detected to the UAV's cruise cycle, includes: The probability of single-drone detection is determined based on the horizontal distance between the target point and the drone; Calculate the joint spatial detection probability of multiple drones at any time. Based on the detection probability of a single drone, calculate the joint detection probability of multiple drones at a certain time. Set a spatial detection probability threshold. When the joint detection probability reaches or exceeds the threshold, it is determined that the target point has been effectively detected at that time. The time surveillance probability of a target point is calculated by dividing the cumulative time the target point is effectively detected within a cruise cycle by the cruise cycle. The effective coverage area is determined, and a time monitoring probability threshold is set. Each target point in the monitoring area is included in the effective coverage area if its time monitoring probability reaches or exceeds the threshold. All the target points that meet the criteria constitute the effective coverage area of ​​the multi-aircraft joint regional coverage monitoring.

3. The fixed-wing UAV collaborative area coverage surveillance optimization deployment method according to claim 2, characterized in that, The probability of single-machine detection can be expressed as: Where d is the horizontal distance between the UAV and the target point, f(d) is a monotonically decreasing function of d, and f(d) min )=1,f(d max ) = 0.

4. The method for optimized deployment of fixed-wing UAV collaborative area coverage surveillance according to claim 3, characterized in that, The expression for the joint detection probability is: Where, d kt Let be the distance between the k-th UAV and the target point V at time t, and n be the number of UAVs.

5. The method for optimized deployment of fixed-wing UAV collaborative area coverage surveillance according to claim 4, characterized in that, The expression for the effective coverage area is: S eff ={A|η(V)≥η c } Where η(V) is the ratio of the time when target point V is effectively detected to the cruise cycle, ηc is the time monitoring probability threshold, and A is the area to be monitored.

6. The method for optimized deployment of fixed-wing UAV collaborative area coverage surveillance according to claim 5, characterized in that, The step of determining the deployment parameters of the effective coverage area based on the area coverage monitoring of two aircraft operating in parallel and in coordination, wherein the deployment parameters include the radius of the hovering circle, the distance between the two aircraft, the initial phase difference, and the flight direction, includes: An optimization algorithm based on simulation iteration is used to determine the optimal initial phase difference and flight direction for two-aircraft coordination. The radius of the hovering circle is determined. Based on the optimal initial phase difference and flight direction of the two aircraft working together, the golden section search algorithm combined with the inscribed rectangle optimization method is used to determine the preset optimal distance between the two aircraft that maximizes the area of ​​the inscribed rectangle when the two aircraft are working together side by side.

7. The method for optimized deployment of fixed-wing UAV collaborative area coverage surveillance according to claim 6, characterized in that, The simulation iterative optimization algorithm includes the following steps: Construct a monitoring area with a preset side length, and rasterize the monitoring area with a preset precision, placing the center of the two-machine rotating circle in the middle of the area; Set the angle change range, traverse all angle positions within the drone's flight cycle, and calculate the position coordinates of the two drones at different times; The detection probability of each grid point at different times is calculated based on the joint detection probability, and the percentage of time that meets the detection probability threshold is statistically analyzed to determine the effective coverage area. Iteratively compare the effective coverage area under different initial phase differences and flight directions until the initial phase difference and flight direction corresponding to the maximum effective coverage area are obtained.

8. The method for optimized deployment of fixed-wing UAV collaborative area coverage surveillance according to claim 6, characterized in that, The steps of the inscribed rectangle optimization method include: Determine the radii of the hovering circles of the two aircraft, establish a coordinate system, place the hovering circles of the two aircraft on the x-axis, set the initial phase difference and flight direction corresponding to the maximum effective coverage area of ​​the two aircraft working together, and determine the boundary points of the effective coverage area. Iterate through all boundary points, construct an inscribed rectangle with each point as a vertex, and calculate the area of ​​the rectangle; The golden section search algorithm is used to find the inscribed rectangle with the largest area, and the corresponding distance between the two rectangles is the preset optimal value.

9. The method for optimized deployment of fixed-wing UAV collaborative area coverage surveillance according to claim 8, characterized in that, The initial phase difference corresponding to the maximum effective coverage area is π, and the two drones corresponding to the maximum effective coverage area are flying in the same direction; the hovering radius of the drone is the minimum turning radius, and the distance between the two drones is a preset optimal value d. opt , of which 2r <d opt ≤2d min r is the radius of the spiral circle, d min It is the minimum spacing between the spiral circles.

10. The method for optimized deployment of fixed-wing UAV cooperative area coverage surveillance according to claim 9, characterized in that, The step of establishing a gridded, multi-machine collaborative regional coverage monitoring deployment scheme with square units based on the deployment parameters of the effective coverage area includes: Based on the deployment parameters of two-machine collaboration, the side length L of the square corresponding to the maximum coverable 2:1 rectangle is calculated using an improved golden ratio algorithm, which serves as the smallest unit for multi-machine deployment. The large area to be monitored is divided into multiple strip-shaped areas with a width of 2L. Multiple drones are deployed in each strip-shaped area, and the drones are deployed in a parallel and collaborative manner to achieve full coverage of the entire area through multiple deployments. Adjacent drones maintain flight in the same direction, with an initial position phase difference of π, a hovering circle radius equal to the minimum turning radius, and a hovering circle spacing of the preset optimal value.

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