Airborne early warning and control (AEW&c) platform and unmanned aerial vehicle wolf trap swarm coordination system and a coordination method suitable for this system

Abstract

The invention relates to a computer system and a coordination method suitable for that system, which enables airborne early warning and control (AEW&C) air platforms and reconnaissance / intelligence unmanned aerial vehicles (UAVs) to perform distributed task sharing, allowing the continuation of AEW&C missions including airspace surveillance, command and control, reconnaissance, intelligence, communication, computation, and emergency air traffic control even if one or more platforms become inoperable.

Description

[0001] DESCRIPTION

[0002] AIRBORNE EARLY WARNING AND CONTROL (AEW& C) PLATFORM AND UNMANNED AERIAL VEHICLE WOLF TRAP SWARM COORDINATION SYSTEM AND A COORDINATION METHOD SUITABLE FOR THIS SYSTEM

[0003] Technical Field

[0004] The invention relates to a computer system and a coordination method suitable for that system, which enables airborne early warning and control (AEW& C) air platforms and reconnaissance / intelligence unmanned aerial vehicles (UAVs) to perform distributed task sharing, allowing the continuation of AEW& C missions including airspace surveillance, command and control, reconnaissance, intelligence, communication, computation, and emergency air traffic control even if one or more platforms become inoperable.

[0005] Prior Art

[0006] Within the scope of current airspace control and surveillance activities, airborne early warning and control (AEW& C) or AW ACS aircraft equipped with early warning radar sensors carry out airspace monitoring. These aircraft can operate in greater numbers depending on the scale of the airspace. Airspace operators are stationed on board these aircraft. As the scale of the airspace increases, the number of aircraft required to scan the airspace also increases, resulting in a higher number of airspace operators onboard.

[0007] A shared network is established between AEW& C or AW ACS aircraft and ground-based fixed early warning radar systems to enable the exchange of track information, and the detection and identification of unauthorized elements entering the airspace are carried out through this network. Operators onboard the aircraft platforms define the track information they detect and identify within the shared network, aiming to accurately verify target detection with ground-based radars. In this way, track information related to enemy elements becomes readily available across the entire airspace picture for use by all military units. Not every element flying in the airspace is necessarily hostile; in addition, there may be friendly force elements or civilian entities. As a routine, airspace planning is associated with air pictures generated by early warning radars in accordance with flight plans specified in air tasking orders. However, the management of air elements that may appear unexpectedly and without a flight plan can be handled by different airborne early warning radar systems. AEW& C and AW ACS aircraft are also capable of providing takeoff and landing support to airfields affected by natural disasters, as well as managing airspace control around the crash site of a downed aircraft. For all such missions, the track information of every detected, identified, and monitored aerial element is shared over a common air network. This shared network also supports the structure referred to as network-enabled capability (NEC).

[0008] All airborne reconnaissance, intelligence, and surveillance platforms that perform track detection and identification over the common network generally encounter various issues in information sharing, depending on network communication range conditions, communication channel data transmission capacity, and link budgeting envelope status. These issues typically include delays, reductions in the amount of information, and potential inaccuracies in the transmitted data.

[0009] In addition, since a large number of operators serve onboard airborne platforms, an increase in human task load can lead to fatigue and result in information errors during track monitoring and identification, which may negatively affect ongoing operations. As AEW& C and AW ACS platforms are valuable force multipliers for a nation's air force, it is often necessary for these platforms to approach enemy elements in order to ensure accurate track detection and identification.

[0010] CN115902804A discloses a cluster-type identification system and method for unmanned aerial vehicles. WO2024054628A2 discloses a system and method for operating multiple unmanned aerial vehicles (UAVs) commanded and supported by a manned "Tender" aircraft carrying a pilot and flight controller(s).

[0011] In known prior art applications, under conditions referred to as network-enabled capabilities, all airborne command and control, reconnaissance, intelligence, and surveillance elements generally share information depending on communication channels among themselves. Additionally, each type of aerial platform, depending on its weight, electrical power, and environmental condition capabilities, experiences delays in information sharing, reductions in the amount of processed data, and potential inaccuracies in information accuracy related to reconnaissance, intelligence, and surveillance activities. These issues arise due to limitations such as operational duration, range, communication channel coverage, data transmission capacity, and link budget envelope status. Consequently, these challenges impact accurate mission planning, execution, monitoring, assessment, exercises, and training within the operational area.

[0012] Therefore, in situations referred to as network-enabled capabilities, due to the dependency of all airborne command and control, reconnaissance, intelligence, and surveillance elements on communication channels among themselves for information sharing, there arises a need for a system and a method operating accordingly that prevents delays in information sharing, reductions in the amount of processed information, and errors in information accuracy.

[0013] Objectives of the Invention

[0014] The object of the present invention is to provide a system and a method operating accordingly that prevent problems arising from delays in information sharing, reductions in the amount of processed information, and errors in information accuracy, which occur due to the dependency of all airborne command and control, reconnaissance, intelligence, and surveillance elements on communication channels among themselves for information sharing.

[0015] The method defined in independent claim 1 has been developed to eliminate these technical problems.

[0016] Coordinated swarm systems for AEW& C and reconnaissance / unmanned aerial vehicle (UAV) surveillance missions offer continuous detection and tracking potential with higher efficiency and lower system costs compared to traditional reconnaissance, intelligence, and surveillance technologies. This advantage primarily stems from the ongoing trend of miniaturization of electronic devices and advancements in autonomous system research, leading to increased availability of cost-effective reconnaissance, intelligence, and surveillance nodes.

[0017] In the current structure, under conditions referred to as network-enabled capabilities, all airborne reconnaissance, intelligence, and surveillance elements generally require mitigation of delays in information sharing, reductions in data volume, and potential inaccuracies in information accuracy, which arise due to communication channel range limitations, data transmission capacity, and link budget envelope constraints.

[0018] Contrary to the current situation, with a significant increase in the number of reconnaissance, intelligence, and surveillance nodes, airborne platform swarms can enhance detection and tracking performance through greater confidence, increased integrity, improved accuracy, time optimization, and enhanced robustness in intelligence, surveillance, and reconnaissance (ISR) missions. Under intensive operational area conditions, to gather intelligence on points, edges, and objects requiring control — such as the accumulation of enemy elements — some users may dynamically deploy a swarm ISR system package of platforms within a designated area to detect movements and obtain real-time target information. Detailed Description of the Invention

[0019] The swarm coordination system implemented to achieve the objective of this invention is illustrated in the figures.

[0020] Figures;

[0021] Figure 1: A schematic view of the system.

[0022] The components shown in the figures are individually numbered, and their corresponding references are given below.

[0023] 1. AEW& C platform mission computer

[0024] 2. AEW& C platform swarm coordination management system

[0025] 3. Communication interface

[0026] 4. UCAV platform mission computer

[0027] 5. UCAV ground support center communication interface

[0028] 6. Ground support center swarm coordination management system

[0029] 7. AEW& C platform

[0030] 8. Ground support center

[0031] 9. UCAV platform

[0032] The inventive method, in its most basic form, comprising,

[0033] - transmitting all surveillance information (coordinate / time, track detection and identification information, platform type, and sensor information ) collected from sensors on a AEW& C platform (7) to a AEW& C platform swarm coordination management system (2) by a AEW& C platform mission computer (1),

[0034] - receiving task surveillance information (coordinate / time, track detection and identification information) and platform status information (operational status, sensor information, and fuel information) for evaluation by the AEW& C platform swarm coordination management system (2),

[0035] - Executing a task related to reconnaissance and surveillance processes performed according to Killer Whale Orca Swarm Algorithms, utilizing swarm coordination parameters (track information, coordinate / time data, altitude, and speed) received via the AEW& C platform communication interface (3) from UCAV platforms (9), including the monitored target parameters and UCAV platform status information (fuel status, operational status, platform environmental conditions, altitude, platform payload sensor data, and platform coordinate / time information) by the AEW& C platform swarm coordination management system (2),

[0036] - sharing all reconnaissance and surveillance task execution status, result information, and UCAV platform data with the mission computer (1), in accordance with the results of the Killer Whale Orca Swarm Algorithm,

[0037] - sharing mission information, platform status information, and coordinate / time data between UCAV and AEW& C platforms via military wired and / or wireless communication interfaces with the UCAV platform mission computer (4) and the AEW& C platform mission computer (1) by the AEW& C platform communication interface (3),

[0038] - transmitting its mission parameters and platform status information to the AEW& C platform swarm coordination management system (2) via the AEW& C platform communication interface (3); receiving, by the AEW& C platform swarm coordination management system (2), said mission parameters and platform status information; and executing the assigned missions on the mission computer (4) by the UCAV platform mission computer (4),

[0039] - transmitting reconnaissance and surveillance mission information, platform status information, and information related to the reconnaissance and surveillance target between the UCAV and AEW& C platforms via a military wired and / or wireless communication interface to at least one of: the ground support center swarm coordination management system (6), the UCAV platform mission computer (4), or the communication interface (3) on the AEW& C platform by the ground support center communication interface (5) located at the ground support center,

[0040] - taking over the Killer Whale Orca Swarm Algorithm processes executed on the UCAV platform mission computer (4) in case of a possible failure; transferring task parameters for reconnaissance and surveillance, platform status information (fuel status, operational status, platform environmental conditions, altitude, and platform payload sensor information), and information related to the reconnaissance and surveillance (track information, coordinate / time, altitude, and speed) target from the UCAV platform mission computer (4) to the AEW& C platform swarm coordination management system (2) via the AEW& C platform communication interface (3); and receiving the same information from the AEW& C platform swarm coordination management system (2) into the ground support center swarm coordination management system (6) by the ground support center swarm coordination management system (6),

[0041] - processing the wolf trap workflow in reconnaissance and surveillance with performing the Killer Whale Orca Swarm Algorithm operated in the swarm coordination management system (2) in the AEW& C platform and in the ground support center swarm coordination management system (6).

[0042] The AEW& C and UCAV Wolf Trap Pack Coordination System and the method that works in accordance with this system work as follows.

[0043] AEW& C platform mission computer (1), all surveillance information (coordinate / time, track detection and identification information, platform type and sensor information) generated by the information collected by the sensors on the AEW& C platform (7) The HRM platform transmits to the herd coordination management system (2). In addition, the AEW& C platform swarm coordination management system (2) receives incoming mission surveillance information (coordinate / time, track detection and identification information), platform status information (operational status, sensor information and fuel information) for evaluation.

[0044] The AEW& C platform mission computer (1) transmits all surveillance information generated from data collected by the sensors on the AEW& C platform (7) including coordinates / time, track detection and identification information, platform type, and sensor details to the AEW& C platform swarm coordination management system (2). Additionally, the AEW& C platform swarm coordination management system (2) receives incoming mission surveillance data (coordinates / time, track detection and identification information) and platform status information (operational status, sensor data, and fuel information) for evaluation.

[0045] The AEW& C platform swarm coordination management system (2), using the communication interface (3), executes the reconnaissance and surveillance missions based on swarm coordination data received from UCAV platforms (9), including target parameters (track information, coordinates / time, altitude, speed) and UAV platform status information (fuel, operational status, environmental conditions, altitude, payload sensor data, and platform coordinates / time), in accordance with Killer Whale (Orca) Swarm Algorithms. All related reconnaissance and surveillance mission status executions, result information, and UCAV platform data are shared with the AEW& C platform mission computer (1) in accordance with the Killer Whale (Orca) Swarm Algorithm outcomes.

[0046] The communication interface (3) shares mission information, platform status data, and coordinate / time information between the UCAV and AEW& C platforms via military wired and / or wireless communication interfaces with the UCAV platform mission computer (4) and the AEW& C platform mission computer (1). The UCAV platform mission computer (4) transmits its mission parameters, platform status information (fuel, operational status, environmental conditions, altitude, payload sensor data), and information related to the reconnaissance and surveillance target (track information, coordinates / time, altitude, speed) to the AEW& C platform swarm coordination management system (2) on the AEW& C platform (7) via the communication interface (3), and the AEW& C platform swarm coordination management system (2) receives this information. Additionally, the assigned missions are executed by the AEW& C platform mission computer (1).

[0047] The UCAV ground support center communication interface (5), located on the ground support center (8), transmits reconnaissance and surveillance mission information, platform status information, and information about the reconnaissance and surveilled element between the ground support UCAV and AEW& C platforms via military wired and / or wireless communication interface to the ground support center swarm coordination management system (6), or to the UCAV platform mission computer (4), or to the communication interface (3) located on the AEW& C platform (7).

[0048] The ground support center swarm coordination management system (6), in case of a possible failure, takes over the Killer Whale Orca Swarm Algorithm processes performed on the UCAV platform mission computer (4) onto itself and sends the mission parameters for reconnaissance and surveillance, platform status information (fuel, operational status, environmental conditions, altitude, payload sensor data), and information related to the reconnaissance and surveillance target (track information, coordinates / time, altitude, speed) via the communication interface (3) to the AEW& C platform swarm coordination management system (2) on the AEW& C platform (7), and also receives the same information from the AEW& C platform swarm coordination management system (2) on the AEW& C platform (7). The Killer Whale Orca Swarm Algorithm, executed on the AEW& C platform swarm coordination management system (2) and the ground support center swarm coordination management system (6), operates as follows to perform the trap net workflow in reconnaissance and surveillance:

[0049] First, the algorithm framework should be established based on the mission model. If the most general variables are defined, the quadruple < P, H, G, K> is formed, where:

[0050] P represents the air platforms (AEW& C and UCAV) platforms, H represents the set of targets under reconnaissance and surveillance, G represents the set of anticipated air mission task lists for the targets, and K represents the set of constraints formulated for the problem model.

[0051] In this algorithm proposal, air platforms perform cooperative mission execution in reconnaissance and surveillance, determining multiple constraints, optimization goals, and a multi-cooperative multi-mission assignment model for the air platforms as defined in the table below:

[0052]

[0053] The definition of an airborne platform system may include the N_p reconnaissance mission. For any aerial platform p_i (i£[ 1, N_p]) with a fixed flight speed v_i, the maximum flight distance d_i_max and the maximum flight duration T_i_max.

[0054] Since the airspace is operationally constantly dynamically updating information, the information of each target point is updated after reconnaissance and surveillance. Thus, at the time of reconnaissance for Hj, the target point should have Hj information. In order for this mission assignment model framework to work correctly, the following assumptions must be made:

[0055] 1. The approach coordinate positions and numbers of the targets are known to the main bases of the air platforms.

[0056] 2. The air platform will fly at a constant speed, the flight altitudes of the air platforms are not consistent to avoid collision with other air platforms.

[0057] 3. The same mission may be carried out by more than one air platform, and the air platforms may work in coordination to reach the location of the reconnaissance and surveillance target at the same time, which may result in waiting time.

[0058] Based on the above symbolic interpretation and model assumptions, the common multi-mission allocation for the scheme air platforms can be expressed as a vector as shown in table 2 below. The vector has three rows; the first row represents the target sequence, the second row represents the sequence of tasks on the target (mainly multiple reconnaissance and surveillance searches, two searches as an example) and the third row represents the result of the task assignment.

[0059] In the communication infrastructure, different technologies can be used for the communication interface (3) and ground support center communication interface (5). These technologies can be different communication infrastructures such as satellite communication, Military Data Link communication, Intra-Arm Communication interfaces, wireless optical communication technologies.

[0060]

[0061] In table 2, G_k represents the Kth task of target HJ and is assigned to p_i, hence the third row X_ij (i 62 [1, N_p]) is 1. The other i’(i'^ i, i' 6 [1, N_h]) is 0 for X_ij, which means that

[0062] HJ is not assigned to p_i.

[0063] Objective Function:

[0064] In this inventive proposal, task / task activity assignment creates a task allocation model under multiple constraints such as limited resources. The objective function of the model mainly considers the minimization of resources. This is achieved by optimizing the consumption of resources and the completion effect of the task / task activity. A cost consumption function is used to measure the resource impact and the task / task activity completion impact is measured as an evaluation function.

[0065] Cost Function:

[0066] Generally, the traveled distance or time for a single assigned task / task activity is used as an evaluation measure for consumption. In this model, the effects of distance and time on resource consumption are measured. Taking these considerations into account, the travel distance and time costs of UCAV and AEW& C platform systems are expressed as follows.

[0067] a. Distance Cost If the distance cost of the AEW& C and UCAV platforms is denoted as d_total,

[0068]

[0069] Where dJkb(i) denotes the flight distance from target point h J to target point h_b for AEW& C and UCAV platform p_i; x_ijk is a binary value where k denotes the processing of target hj by AEW& C or UCAV platform p_i and is represented as the following specific expression:

[0070]

[0071] If there are no obstacles on the flight path, the distance djkb from the mission point Hj to the next point H_j' can be easily obtained with the following formula:

[0072]

[0073] denotes the location coordinates

[0074]

[0075] hj of the AEW& C and UCAV platform p_i in mission G_k and the target hj in mission G_k'.

[0076] b. Time Cost:

[0077] To ensure that tasks / task activities are completed as quickly as possible, the model also considers the total completion time of all tasks / task activities as another optimization index; here, the shortest time for all task / task activity completion enables rapid completion of all tasks / task activities. The time cost incurred by the AEW& C or UCAV system to perform the mission is represented as follows:

[0078]

[0079] Here,fcve rjkrepresent the time for task G_k processing at target h J on platform p_i, and the flight time from target hj to the next target point hj for AEW& C or UCAV p_i, respectively. Ttotai is the total time, which is the sum of r(®fcve rjk. zjkis a predefined parameter, and

[0080]

[0081] is calculated using the following formula (d):

[0082]

[0083] The system model in this invention proposal defines a cost function system to measure the resource consumption of AEW& C or UCAV. Distance and time are the main indicators for measuring resource consumption. Thus, the distance cost and time cost of the AEW& C or UCAV system should be the main components of the system. However, distance and time are different evaluation indicators. Direct addition of these two indicators in numerical dimensions affects fairness. Therefore, these two indicators need to be standardized to eliminate this; otherwise, the dimension will affect the calculation. Based on the above analysis, the cost function of the AEW& C or UCAV system can be expressed as follows:

[0084]

[0085] The first and second terms of formula (e) measure resource consumption as follows: respectively, the distance and time parameters. To eliminate the dimensional effect between them, the distance and time indicators are divided by their respective maximum values, and normalization is performed within the limits (the maximum flight distance of the AEW& C or UCAV system

[0086]

[0087] dnaxand the endurance time of the AEW& C or UCAV platform i

[0088]

[0089] c. Task / task activity Gain Function:

[0090] In an air operation scenario, having control over the enemy's critical intelligence is the key to victory. Assume that initially the AEW& C or UCAV platform only knows the approximate location of the target. After detecting the target, the AEW& C or UCAV platform will obtain the precise target location information HJ=(xJ, yj), where j£[l, N_h], and the initial value will be the target's value h_j.

[0091] The initial value of the task / task activity decreases as the waiting time increases. In this invention, it is accepted as a principle that the effectiveness of processing the task / task activity decreases as time increases. Accordingly, there is an inverse relationship between the function value and time. In order to eliminate the dimensional effect between cost and gain, the gain must be normalized by dividing the actual gain by the sum of the initial values of all tasks / task activities. This is given in the following formula (f):

[0092]

[0093] In equation (f), with

[0094]

[0095] being a binary value, the task hj assigned to AEW& C or UCAV platform h_i. The value of x_ijq is 1 only when p_q is a reconnaissance and surveillance task / task activity; value (hj) is the initial importance of the target in the first minute, and cp(t) 6 [0,1] decreases over time. This represents the value obtained over time for the detected value in reconnaissance and surveillance, and the calculation of cp(t) is given by equation (g) below:

[0096] <p(t) =e~pt (g)

[0097] Here, the value p indicates the rate of decrease in the gain. The larger p is, the faster the gain of the target under reconnaissance and surveillance decreases over time. Instead of detecting and identifying the same thing repeatedly, the AEW& C or UCAV platform should aim to select unexplored task / task activity target point(s). Therefore, we adjust the task / task activity gain function according to the actual gain. When AEW& C or UCAV platforms perform reconnaissance and surveillance on the same target repeatedly, the information they are seeking will largely overlap; thus, the value of the task / task activity should be halved. The value change in the task / task activity function is calculated with function (h).

[0098] value(h J)= l / 2)k~1* value(h_f) (h) Here, the value k indicates the amount of search conducted by the AEW& C or UCAV platform for the reconnaissance and surveillance of the target at the task / task activity point(s) h _J.

[0099] d. Task / task activity Allocation Model:

[0100] For AEW& C or UCAV platforms, the requirements of the task / task activity, cost consumption, task / task activity impact, and other factors are expressed in the following as a task / task activity assignment model with multiple constraints.

[0101]

[0102] Here a expresses the flag value in the binary structure as follows:

[0103]

[0104] Formula (j) ensures that the task / task activity is performed; formula (k), the values dtand r(represent the total flight distance and the total time spent to complete all tasks / task activities assigned by the AEW& C or UCAV platform p_i. None of them may exceed the specified upper limit. Formula (1) ensures that the task / task activity is successfully completed, and the resources must meet the criteria to be fulfilled by the AEW& C or UCAV platform that performs the completion of the task / task activity. In the formulated task / task activity allocation model, the cost function and the task / task activity benefit function are used respectively as the numerator and the denominator of the evaluation objective function. By maximizing the proposed objective function, the conflicting variables of the task / task activity gain value and the resource consumption are optimized simultaneously. Meanwhile, the following constraints likewise apply:

[0105] • Limited resources and task / task activity priority have been introduced to improve the applicability of the model.

[0106] • The introduction of multiple constraints makes the established model more accurate and precise.

[0107] It further increases the task / task activity execution efficiency of the AEW& C or UCAV system.

[0108] Killer Whale Orca Swarm Algorithm:

[0109] This algorithm is designed inspired by the predatory tactics of Killer Orcas. The Killer Orca swarm algorithm adopts strategic hunting behaviors as the basis of its operational framework. Known for their complex social structures and cooperative hunting strategies, orcas use echolocation to navigate and communicate in water regions, thereby executing coordinated attacks on their hunt. In this algorithmic model, a potential solution is conceptualized as an n-dimensional vector

[0110]

[0111] » ■•tw] representing the collective solution space. The Killer Whale Orca Swarm Algorithm consists of two main stages: chasing and attacking phases; each reflecting different aspects of orca hunting behaviors. The chasing phase comprises two distinct actions: driving the prey towards the water surface and surrounding it to prevent escape. The chasing phase consists of two different actions: driving the prey towards the water surface and surrounding it to prevent escape. This is governed by the parameter ppp, which determines the probability of executing each of these actions. Each action is based on comparing a random value rrr within the range [0, 1]. If p<r, the algorithm prefers the swarm strategy; otherwise, it proceeds with encirclement. The subsequent attack phase reflects the orcas' approach to closing the gap between themselves and their prey. They hunt using precision and cooperation to secure their targets. The effectiveness of the Killer Whale Orca Swarm Algorithm depends on its ability to mimic these complex behaviors, which require accurate sensory data, and on its collaborative decision-making processes to optimize the search for solutions.

[0112] 1-) Hunting / Chasing Phase:

[0113] This strategy operates under two different environmental scenarios. The first scenario occurs when a small fish school reduces the available spatial area for the fish school. Conversely, the Killer Whale Orca’s navigation arises in the second scenario as a large fish school, expanding the operational hunting area of the Killer Whale Orca. In response to these changing conditions, the algorithm defines two specific approaches to interact with the hunt.

[0114]

[0115] Velocity dynamics and spatial positioning are calculated by equations (l)-(4). In this strategy,

[0116]

[0117] defines the velocity relative to the initial pursuit tactic, while isthe spatial coordinate of i corresponding to Orca at time t. Similarly,

[0118]

[0119] are defined for the alternative follow-up strategy. The variable

[0120]

[0121] is the global coordinate of the i-th Orca at time t and the optimal location

[0122]

[0123] among Orcas, indicating proximity to prey or the most effective strategy at time t.

[0124] The term M quantitatively represents the average position within the Orca community where the random variables a, b, and d. are uniformly distributed over [0,1], and e covers the range [0,2]. The parameter F encompasses the effect or attractive force that one agent exerts on another, and q, which lies within [0,1], determines the probability of selecting a specific method for prey tracking. Since the prey is now accessible on the water surface, the next maneuver aims to surround the target. Orcas use sonar for communication, detecting upcoming developments and maintaining their positions through coordination with nearby members of their pod. It is proposed that orcas adjust their positions based on the coordinates of three randomly selected orcas. The calculation of their positions after the maneuver is expressed through equations (5)— (7), providing a mathematical representation of this coordinated movement with the following expressions:

[0125]

[0126] where Maxlter denotes the maximum number of iterations, while jl, j2 and j3 represent three different Orcas randomly selected from the ensemble such that j l j2 j3. Variable

[0127]

[0128] corresponds to the spatial coordinate of the i-th Orca after its adoption.

[0129] In the iteration, for the tertiary strategy ttt during the chase, the orcas utilize acoustic signals to detect the location of the prey. In response, they modulate their spatial orientation. These orcas either persist in the pursuit depending on the perceived proximity of the prey, or if the prey appears to be moving away, their instinctive behavior is to stop and maintain their current position.

[0130] 2-) Algorithm 1: Specifies the pseudocode procedure of the Killer Whale Orca Swarm Algorithm. Within the pseudocode, variables such as population size (S), method selection probabilities (p and q), and dimensionality (n) are defined to aim for producing the best solution. Thus, the algorithm starts as follows: initialization of the objective function and random calculation of the first generation of S orcas. For each orca and each decision variable, positions and velocities are assigned randomly. The fitness of each orca is calculated using the objective function defined in the optimization problem, and the best orca is identified. If a better orca is found, the best solution is updated accordingly.

[0131] Then, the algorithm enters a loop that generates Orca generations. The algorithm has reached a predefined recursion limit. Within this loop, operations are performed for each Orca. The algorithm selects three random numbers for the Orcas. Based on the probability of the variable p, the algorithm decides whether to perform the chasing phase to drive the prey or to encircle the prey for each Orca. If p is greater than a random value, the chasing phase is carried out by driving or surrounding the prey according to the value q. If p is less than a random value, the attack phase is performed for each variable.

[0132] The orcas mentioned above are used in practice as AEW& C or UCAV platforms. The objective function in the algorithm is based on minimizing resource consumption and is evaluated as the traveled distance or time assigned for a single task / task activity in the task / task activity allocation part. The probability for the prey is matched as the detection probability of the target element on which the AEW& C or UCAV platforms will conduct reconnaissance and surveillance in the task / task activity.

Claims

CLAIMS1. A coordination method, for airborne early warning control air platforms to perform airspace surveillance, command, control, reconnaissance and intelligence, communication, computation, and air traffic control activities in emergencies, by distributing tasks among reconnaissance and intelligence or unmanned aerial vehicles that serve as airborne early warning units, and enabling continuation of airborne early warning control tasks even if any of the platforms become inoperative for any reason, operating with a computer system coordinating via software algorithms, it comprises;- transmitting all surveillance information (coordinate / time, track detection and identification information, platform type, and sensor information ) collected from sensors on a AEW& C platform (7) to a AEW& C platform swarm coordination management system (2) by a AEW& C platform mission computer (1),- receiving task surveillance information (coordinate / time, track detection and identification information) and platform status information (operational status, sensor information, and fuel information) for evaluation by the AEW& C platform swarm coordination management system (2),- Executing a task related to reconnaissance and surveillance processes performed according to Killer Whale Orca Swarm Algorithms, utilizing swarm coordination parameters (track information, coordinate / time data, altitude, and speed) received via the AEW& C platform communication interface (3) from UCAV platforms (9), including the monitored target parameters and UCAV platform status information (fuel status, operational status, platform environmental conditions, altitude, platform payload sensor data, and platform coordinate / time information) by the AEW& C platform swarm coordination management system (2),- sharing all reconnaissance and surveillance task execution status, result information, and UCAV platform data with the mission computer (1), in accordance with the results of the Killer Whale Orca Swarm Algorithm,- sharing mission information, platform status information, and coordinate / time data between UCAV and AEW& C platforms via military wired and / or wireless communication interfaces with the UCAV platform mission computer (4) and the AEW& C platform mission computer (1) by the AEW& C platform communication interface (3),- transmitting its mission parameters and platform status information to the AEW& C platform swarm coordination management system (2) via the AEW& C platform communication interface (3); receiving, by the AEW& C platform swarm coordination management system (2), said mission parameters and platform status information; and executing the assigned missions on the mission computer (4) by the UCAV platform mission computer (4),- transmitting reconnaissance and surveillance mission information, platform status information, and information related to the reconnaissance and surveillance target between the UCAV and AEW& C platforms via a military wired and / or wireless communication interface to at least one of: the ground support center swarm coordination management system (6), the UCAV platform mission computer (4), or the communication interface (3) on the AEW& C platform by the ground support center communication interface (5) located at the ground support center,- taking over the Killer Whale Orca Swarm Algorithm processes executed on the UCAV platform mission computer (4) in case of a possible failure; transferring task parameters for reconnaissance and surveillance, platform status information (fuel status, operational status, platform environmental conditions, altitude, and platform payload sensor information), and information related to the reconnaissance and surveillance (track information, coordinate / time, altitude, and speed) target fromthe UCAV platform mission computer (4) to the AEW& C platform swarm coordination management system (2) via the AEW& C platform communication interface (3); and receiving the same information from the AEW& C platform swarm coordination management system (2) into the ground support center swarm coordination management system (6) by the ground support center swarm coordination management system (6),- processing the wolf trap workflow in reconnaissance and surveillance with performing the Killer Whale Orca Swarm Algorithm operated in the swarm coordination management system (2) in the AEW& C platform and in the ground support center swarm coordination management system (6).characterized in that, in the trap workflow, it further comprises;Calculating the total distance cost d_total of AEW& C and UCAV platforms by formula (a),Calculating the time cost spent by the AEW& C or UCAV system to perform the mission by formula (c),Calculating the cost of the AEW& C or UCAV system by formula (e),Calculating the mission activity gain function by formula (f).

2. A method according to claim 1, characterized in that it comprises, calculating the value change in the mission activity function according to formula (h).value(h_j) = (1 / 2)k-1* value(h_j) (h)3. A method according to claim 1 or 2, characterized in that it comprises, calculating the velocity dynamics and spatial positioning during the prey chasing phase in the Killer Whale Orca Swarm Algorithm by equations (1) to (4).

4. A method according to claim 3, characterized in that it comprises, Istem 3’teki gibi bir yontem olup, calculating the post-maneuver positions in the Killer Whale Orca Swarm Algorithm by equations (5) to (7).