Query: UAV swarm game confrontation

title: Research on UAV Swarm Confrontation Task Based on MADDPG Algorithm
abstract: In recent years, with the rapid development of UAV technology, the demand of anti UAV technology is increasingly urgent. Among the existing soft and hard means to counter the attack of UAV swarm, countering UAV swarm by UAV swarm is an important way to counter the attack of UAV swarm in the future. Based on the idea of UAVs intelligent attack and defense confrontation, this paper establishes a simulation environment of UAVs confrontation and a intelligent model of UAV swarm based on MADDPG algorithm. Aiming at the problems such as the speed control of UAV is not accurate and it is difficult to choose the appropriate attack angle in the confrontation, a rule coupled method is proposed to effectively improve the confront ability of UAV. The experimental results show that the rule coupled method can significantly improve the winning rate of the UAV swarm in the confrontation from 63% to 81% and reduce the average epochs required to destroy all the enemy UAVs in a winning game from 61 to 49.

title: Feasible region division of panoramic images based on morphological filter
abstract: Image stitching is a technique of forming a panoramic image by transforming, resampling and fusing a set of overlapping image sequences, which has been widely used in robot vision, UAV surveillance and other fields. Morphological filter is a very effective method for perceiving global situation in swarm agent game based on visual information. In this paper, a simple image background is taken as an example, and multi monocular camera image fusion is completed based on classical SURF algorithm. In the synthetic panoramic image, the agent motion process is equivalent to the image corrosion operation. In this way we can segment the feasible region of the rigid body, and then transform it into the coordinates of the unfeasible region of the target system through Euler coordinate transformation, which paves the way for the subsequent perception and evaluation of the global situation of multi agent confrontation based on visual information. The innovation point is to find the feasible area of rigid body by morphological filtering, and carry on the binarization processing to the miscellaneous points, making the operation simple and the programming easy to realize.

title: Research on Task Oriented Dynamic Reconstruction Method of UAV Swarm Evolutionary Game
abstract: The reconnaissance or electronic warfare payload UAV swarm with networking and coordination functions are highly autonomous swarm intelligent Cyber Physical System (CPS) The system core of continuous and effective operation lies in the cooperative work between intelligent entities. In the process of operation, the external interference hit of the system or the degradation abnormality of the internal nodes will affect the security, efficiency and reliability of the system. In this paper, the idea of evolutionary game is used to dynamically reconstruct the resources of the degenerating swarm, reconfigure the available resources, to make the swarm reaching an evolutionary stable state in a certain period of time, so as to improve the overall utilization rate of the cluster resources the flexibility of the swarm and the overall performance of the swarm from the system level, and ensuring continually and effectively safe operation of the swarm system.

title: Offense defense confrontation decision making for dynamic UAV swarm versus UAV swarm
abstract: This paper studies a dynamic swarm versus swarm unmanned aerial vehicle (UAV) combat problem and proposes a self organized offense defense confrontation decision making (ODCDM) algorithm. This ODCDM algorithm adopts the distributed architecture to account for real time implementation, where each UAV is treated as an agent and able to solve its local decision problem through the information exchange with neighbors. At each decision making step, the swarm seeks an optimal target allocation scheme and each UAV further selects the corresponding behavioral rules, leading to emergent offensive and defensive behaviors. Therefore, the offense defense confrontation decision making process is divided into the target allocation decision based on distributed consensus based auction algorithm (CBAA) and social force based swarm motion decision. An offense defense preference is introduced to the target allocation optimization model, providing the tactics options for UAV to adopt more offensive or more defensive posture. On the basis of classic collective behaviors of cohesion, separation and alignment, a combat stimulus is considered to drive UAV towards the assigned target. Finally, simulation experiments are carried out to verify the effectiveness of the ODCDM algorithm, and analyze the influences of the external deployment and internal tactics on the combat results.

title: AI and Game Theory based Autonomous UAV Swarm for Cybersecurity
abstract: Uninterrupted communication is crucial for modern electromagnetic (EM) spectrum operations where successes of situational awareness, defensive and offensive missions necessitate continuous reliance on wireless transmission. Preventing an adversary from dominating cyberspace becomes challenging as rapid technological developments allow state and non state actors to engage in a broad range of destructive cyber electromagnetic activities (CEMA) Digital threats to communication networks can range from eavesdropping and impersonation attempts to various forms of denial of service attacks. In this paper, we present bio inspired and game theory based flight control algorithms for a swarm of autonomous UAVs. Each UAV considers MANET connectivity, overshadowed ground area coverage and signal strength from interfering mobile radio emitters. Our algorithms use 3D Voronoi tessellations and linear interpolation for EM mapping of local neighborhood as part of decision making process. Simulation experiments in OPNET show that autonomous UAVS require only limited near neighbor communications to maintain a high area coverage overshadowed by the swarm with uninterrupted MANET connectivity. By providing a lightweight solution for rapidly deployable swarm of autonomous UAVS, our flight control algorithms are good candidates for deployment in complex environments in presence of adaptive and mobile sources of EM interference.

title: Mission driven autonomous perception and fusion based on UAV swarm
abstract: Distributed autonomous situational awareness is one of the most important foundation for Unmanned Aerial Vehicle (UAV) swarm to implement various missions. Considering the application environment being usually characterized by strong confrontation, high dynamics, and deep uncertainty, the distributed situational awareness system based on UAV swarm needs to be driven by the mission requirements, while each node in the network can autonomously avoid collisions and perform detection mission through limited resource sharing as well as complementarity of respective advantages. By efficiently solving the problems of self avoidance, autonomous flocking and splitting, joint estimation and control, etc. perception data from multi platform multi source should be extracted and fused reasonably, to generate refined, tailored target information and provide reliable support for decision making.

title: 3D Deployment of UAV Swarm for Massive MIMO Communications
abstract: In this paper, we consider the uplink transmission between a multi antenna ground station and an unmanned aerial vehicle (UAV) swarm. The UAVs are assumed as intelligent agents which can adaptively adjust their three dimensional (3D) deployment to maximize the channel capacity of the multiple input multiple output (MIMO) system. Specifically, for the limitations of each UAV in accessing the global information, we consider a decentralized control strategy and divide the optimization problem into several optimization sub problems, and then formulated into a UAVs channel capacity game. We analyze such game according to the designed reward function and the potential function. Then, we discuss the existence of the pure Nash equilibrium in the game. To achieve the best Nash equilibrium of the MIMO system, we develop a decentralized learning algorithm, namely decentralized UAVs channel capacity learning. The details of the algorithm is provided and the convergence of the algorithm is analyzed. In the meanwhile, we give some insightful remarks. The simulations show that the developed learning algorithm can achieve a high channel capacity than the benchmark methods.

title: A Damage Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments
abstract: As Unmanned Aerial Vehicles (UAVs) are widely used in many applications, a lot of military missions in confrontational environments are being undertaken by UAV swarm rather than human beings due to its advantages. In confrontational environments, the reliability and availability of UAV swarm would be the major concern because of UAVs' vulnerability, so damage tolerant task assigning algorithms are of great importance. In this paper, we come up with a novel damage tolerant framework for assigning real time tasks to UAVs with dynamical states in confrontational environments. Different from existing scheduling methods, we not only assign tasks but also back up copies of tasks to UAVs when needed, to promote reliability. Meanwhile, we adopt an overlapping mechanism, including Backup Primary overlapping and Backup Backup overlapping, in assignment to save the limited swarm resources. On the basis of the damage tolerant and overlapping mechanism, for the first time, we propose a new damage tolerant task assignment algorithm named DTTA, aiming at promoting the task success probability. Extensive experiments are conducted based on random synthetic workloads to compare DTTA with three baseline algorithms. The experimental results indicate that DTTA can efficiently promote the probability of tasks' success without affecting the effectiveness of swarms in confrontational environments.

title: Research on the modeling method of UAV swarm operation in cyberspace
abstract: As a kind of game changing combat style, the operation of the UAV swarm in cyberspace is characterized by high effective cost and high concealment, which make it play an important role in the future competition of cyberspace. Due to the lack of actual combat experience and high confidentiality of the cyberspace operation and the UAV swarm operation, the modeling and simulation (M and S) of the operation of the UAV swarm in cyberspace is the vital method for planning the UAV swarm system architecture and designing the tactical methods of the UAV swarm. At present, the research on the M and S of the operation of the UAV swarm in cyberspace is just starting from home and abroad, and there is no corresponding modeling method and concrete model. Taken the Integrated Air Defense System (IADS) as the operational object, the guiding ideology of the M and S of the operation of the UAV swarm in cyberspace was researched from the height of the joint operations, which includes the characteristics of the individual autonomy, the group cooperation, the offensive and defensive antagonism and the electromagnetic cyber the operation. Then, the model characteristic and elements was analyzed, such as the model system architecture, the operational rule and the intelligent algorithm. On this basis, the "action model pool" "operational rule pool" and "intelligent algorithm pool" would be constructed. Afterwards, the modeling method of the operation of the UAV swarm in cyberspace which based the "Rule+ Respond+ Algorithm" (RRA) is proposed, including the modeling framework and main process method of the RRA. The main process included the military concept modeling stage, the operational rules refining stage, the operational rules judgment stage, the action model response stage and the intelligent algorithm solution stage, that the five stages were in sequence, also the main work in each stage was analyzed. At the level of system of system M and S, the modeling focused of the abstraction, extraction and description of the operational rules of the UAV swarm in cyberspace. The description and construction methods of the operational rule are researched, such as the BNF format, the process format, and the operational rules tree graph. Finally, based on the future joint operations, an example of the operation of the UAV swarm in cyberspace is given, then the operational effectiveness is analyzed. The example showed that using nine UAV Swarms to attack IADS in cyberspace would reduce the normal power range of the sensor network by 90% and the warning time by 80.3%

title: Multi UAV counter game model based on uncertain information
abstract: When a multi UAV is performing a mission, the information obtained by the commander could be highly uncertain. How to choose strategies based on uncertain information will directly affect the success or failure of the UAV mission. In this paper, we present a multi UAV confrontation model based on uncertain information, which can provide a powerful reference for strategy selection. We established a multi UAV game model, and proposed a game payment function, and introduce a situation matrix to imitate the uncertainty of war information. In order to complete the calculation of uncertain information, the paper introduces a complex calculation method and establishes an interval decision model. The simulated experiment results show that the situation matrix proposed in this paper could represents the uncertainty in the battle field, and the interval decision method could process data effectively. Furthermore, the proposed algorithm could provide optimal strategy for decision support in battle field with uncertain information, and provide new research direction for game with uncertain information.