Research on system-of-systems combat simulation model formal specification and representation

To makesystem-of-systems combat simulation models easy to be developed and reused, simulation model formal specification and representation are researched. According to the view of system-of-systems combat simulation, and based on DEVS, the simulation model＇s fundamental formalisms are explored. It includes entity model, system-of-systems model and experiment model. It also presents rigorous formal specification. XML data exchange standard is combined to design the XML based language, SCSL, to support simulation model representation. The corresponding relationship between SCSL and simulation model formalism is discussed and the syntax and semantics of elements in SCSL are detailed. Based on simulation model formal specification, the abstract simulation algorithm is given and SCSL virtual machine, which is capable of automatically interpreting and executing simulation model represented by SCSL, is designed. Finally an application case is presented, which can show the validation of the theory and verification of SCSL.

Service-oriented air combat simulation architecture

Service-oriented air combat simulation architecture is proposed.The core design goal is high agility which represents the ability to accommodate the simulation requirements change.Its main idea is to design model units as services that can communicate and interoperate with any other services at runtime.A service is autonomous and is fully defined by a description contract which contains some combination of syntactic,semantic,and behavioral information.Based on the architecture,air combat simulation system can be described as an abstract composition of description contracts.It becomes concrete at run time as services that implement the constituent description contracts are discovered and bind.The whole process is a continuous run-time activity that responds to simulation needs and the availability of services.This provides benefits of implementation transparency and minimal dependency between models.Thus,simulation system can minimize the impact of change on it and increase the overall efficiency to respond to requirements change.

Hierarchical fuzzy ART for Q-learning and its application in air combat simulation

Value function approximation plays an important role in reinforcement learning(RL)with continuous state space,which is widely used to build decision models in practice.Many traditional approaches require experienced designers to manually specify the formulization of the approximating function,leading to the rigid,non-adaptive representation of the value function.To address this problem,a novel Q-value function approximation method named‘Hierarchical fuzzy Adaptive Resonance Theory’(HiART)is proposed in this paper.HiART is based on the Fuzzy ART method and is an adaptive classification network that learns to segment the state space by classifying the training input automatically.HiART begins with a highly generalized structure where the number of the category nodes is limited,which is beneficial to speed up the learning process at the early stage.Then,the network is refined gradually by creating the attached subnetworks,and a layered network structure is formed during this process.Based on this adaptive structure,HiART alleviates the dependence on expert experience to design the network parameter.The effectiveness and adaptivity of HiART are demonstrated in the Mountain Car benchmark problem with both fast learning speed and low computation time.Finally,a simulation application example of the one versus one air combat decision problem illustrates the applicability of HiART.

Model architecture-oriented combat system effectiveness simulation based on MDE

Combat system effectiveness simulation (CSES) is a special type of complex system simulation. Three non-functional requirements (NFRs), i.e. model composability, domain specific modeling, and model evolvability, are gaining higher priority from CSES users when evaluating different modeling methodologies for CSES. Traditional CSES modeling methodologies are either domain-neutral (lack of domain characteristics consideration and limited support for model composability) or domain-oriented (lack of openness and evolvability) and fall short of the three NFRs. Inspired by the concept of architecture in systems engineering and software engineering fields, we extend it into a concept of model architecture for complex simulation systems, and propose a model architecture-oriented modeling methodology in which the model architecture plays a central role in achieving the three NFRs. Various model-driven engineering (MDE) approaches and technologies, including simulation modeling platform (SMP), unified modeling language (UML), domain specific modeling (DSM), eclipse modeling framework (EMF), graphical modeling framework (GMF), and so forth, are applied where possible in representing the CSES model architecture and its components' behaviors from physical and cognitive domain aspects. A prototype CSES system, called weapon effectiveness simulation system (WESS), and a non-trivial air-combat simulation example are presented to demonstrate the methodology.

Design of Simulation System for Effectiveness Evaluation of Future Airspace Window Shooting

Future Airspace Window Shooting is a newly developed technology, which needs effectiveness evaluation before widely used. Future airspace window shooting technology, simulation system development principles and software chosen to develop the simulation system are introduced in the first. And then the overall design of the system, realization of the system and effectiveness evaluation through simulation are discussed in detail. Through the simulation, it is known that the FAW shooting has more superior performance when facing maneuvering targets.

Extending HTN to planning and execution control for small combat unit simulation

Modeling how military commanders carry out operations is considered complicated,requiring the capability of not only planning for multiple subordinates but also responding to unexpected events during execution.This paper presents an Hierarchical Task Network(HTN)embedded planning and execution control architecture for small unit commander agents.To be adaptive to dynamic world state changes,the architecture employs a partial planning mechanism and generates actions only applicable to current situations.It is also able to coordinate subordinates’actions and handle execution failures at runtime.We demonstrate the architecture’s use with an infantry company scenario,where the commander orders three platoons assaulting a defined hill.Our approach shows the effectiveness to control multiple entities in dynamic environments,making the architecture well-suited to represent small unit commanders’behavior.

An Improved Underwater Confrontation Simulation Method of Naval Amphibious Operational Training System

This paper described an improved underwater confrontation simulation method of naval amphibious operational training system. The initial position of submarine forces on the enemy is generated automatically, and the attacking distance model of torpedoes is established based on the kinematics theory, which is more flexible and reasonable to judge the launch condition compared to traditional method. The two kinds of confrontation behavior models on the enemy submarine are created to depict its tactical action from the defensive to the offensive as well as the contrary, ensuring that operational style is simulated more comprehensively and properly. The existing motion trajectory estimation and collision detection algorithms on operational platforms are also improved to reduce the iteration error and further enhance the detection accuracy of target hit.

Tube-based robust reinforcement learning for autonomous maneuver decision for UCAVs

Reinforcement Learning(RL)algorithms enhance intelligence of air combat AutonomousManeuver Decision(AMD)policy,but they may underperform in target combat environmentswith disturbances.To enhance the robustness of the AMD strategy learned by RL,thisstudy proposes a Tube-based Robust RL(TRRL)method.First,this study introduces a tube todescribe reachable trajectories under disturbances,formulates a method for calculating tubes basedon sum-of-squares programming,and proposes the TRRL algorithm that enhances robustness byutilizing tube size as a quantitative indicator.Second,this study introduces offline techniques forregressing the tube size function and establishing a tube library before policy learning,aiming toeliminate complex online tube solving and reduce the computational burden during training.Furthermore,an analysis of the tube library demonstrates that the mitigated AMD strategy achievesgreater robustness,as smaller tube sizes correspond to more cautious actions.This finding highlightsthat TRRL enhances robustness by promoting a conservative policy.To effectively balanceaggressiveness and robustness,the proposed TRRL algorithm introduces a“laziness factor”as aweight of robustness.Finally,combat simulations in an environment with disturbances confirm thatthe AMD policy learned by the TRRL algorithm exhibits superior air combat performance comparedto selected robust RL baselines.