A Novel Uncertainty Management Approach for Air Combat Situation Assessment Based on Improved Belief Entropy

Zhou, Ying; Tang, Yongchuan; Zhao, Xiaojun

doi:10.3390/e21050495

Cited by 16 publications

(13 citation statements)

References 35 publications

(33 reference statements)

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“…e experiment results prove the method has practical value. Zhou et al [20] proposed an uncertain information fusion method for aerial situation awareness in the DSTframework. Firstly, the perceived uncertain information is preprocessed with belief entropy, and the evaluated information is combined according to Dempster combination rules.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Mobile Robot Path Planning Scheme Based on Modified Gray Wolf Optimization and Situation Assessment

Liu

2022

Journal of Robotics

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To better solve the problems associated with optimal pathfinding and dynamic obstacle avoidance in the path planning of mobile robots, a hybrid path planning scheme combining modified gray wolf optimization (MGWO) and situation assessment mechanism is proposed. Firstly, a MGWO algorithm is proposed to plan a global path. Secondly, different situational factors for robots in different regions are extracted from the fusion results of 2D laser measurements and image data, and a Bayesian network model of robot action selection is established. Then, the situational factors of the robot are used as evidence for reasoning. Based on the posterior probability value in the inference result, the grid to be moved is selected and the traveling direction of the robot is adjusted in order to take advantage of both global path planning and local dynamic obstacle avoidance. The simulation results show that the proposed MGWO has better optimization performance. When combined with a situation assessment mechanism, it realizes dynamic obstacle avoidance while keeping the path length as short as possible.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Mobile Robot Path Planning Scheme Based on Modified Gray Wolf Optimization and Situation Assessment

Liu

2022

Journal of Robotics

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“…The main rationale is that CBP is an almost infinitely complex engineered system with countless nodes and layers of interdependency, dynamic and emergent behaviours, with the different types of information flow and causality in general stochastic processes, inferences and coupling structures and parameters of system dynamics [ 42 ]. It is influenced by dynamic complexity and high real-time characteristic of the current and future battlefield environment [ 43 ] characterized by non-state diplomatic activities, information, and military and economic actions that create secondary and third-order effects. Therefore, in comparison to a CAWG (with its automated umpire and players), a manual WG would be the preferable technique to apply when defining capability requirements, because it does not lose the creativity of the human elements involved in WGs (as described in Figure 2 ).…”

Section: Problem Statementmentioning

confidence: 99%

Computer Assisted Wargame for Military Capability-Based Planning

Hodicky¹,

Procházka

Baxa

et al. 2020

Entropy

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Capability-based planning as an approach to defense planning is an almost infinitely complex engineered system with countless nodes and layers of interdependency, influenced by state and non-state diplomatic activities, information, military and economic actions creating secondary and third order effects. The main output of capability-based planning is the set of capability requirements needed to achieve the expected end-state. One revitalized qualitative technique that allows us to gain insights into unstructured and fuzzy problems in the military is wargaming—in its simplest form this involves manual wargaming. At the same time, there has been a push to bring computer assistance to such wargaming, especially to support umpire adjudication and move more generally towards full automation of human elements in wargames. However, computer assistance in wargaming should not be pushed, regardless of cost, towards quantitative techniques. The objective complexity of a problem often does not allow us to replicate the operational environment with the required fidelity to get credible experimental results. This paper discusses a discovery experiment aiming to verify the concept of applying a qualitative expert system within computer assisted wargaming for developing capability requirements in order to reduce umpire bias and risk associated with their decisions. The innovation here lies in applying system dynamics modelling and simulation paradigms when designing the theoretical model of capability development, which forms the core of the expert system. This new approach enables qualitative comparisons between different sets of proposed capability requirements. Moreover, the expert system allows us to reveal the effects of budget cuts on proposed capability requirement solutions, which the umpire was previously unable to articulate when comparing individual solutions by relying solely on his own knowledge. Players in the wargame validated the proposed concept and suggested how the study might be developed going forward: namely, by enabling users to define their own capabilities and not being limited by a predefined set of capabilities.

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“…As a typical multi-agent system, the complexity does not only emanate from the dynamics of the application domain, but also the limited and uncertainty in perceived information. In an attempt to handle these uncertainties, the research community has proposed several theories [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

Nantogma

Ran

2021

Symmetry

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Autonomous (unmanned) combat systems will become an integral part of modern defense systems. However, limited operational capabilities, the need for coordination, and dynamic battlefield environments with the requirement of timeless in decision-making are peculiar difficulties to be solved in order to realize intelligent systems control. In this paper, we explore the application of the learning classifier system and Artificial Immune models for coordinated self-learning air defense systems. In particular, this paper presents a scheme that implements an autonomous cooperative threat evaluation and weapon assignment learning approach. Taking into account uncertainties in a successful interception, target characteristics, weapon type and characteristics, closed-loop coordinated behaviors, we adopt a hierarchical multi-agent approach to coordinate multiple combat platforms to achieve optimal performance. Based on the combined strengths of learning classifier and artificial immune-based algorithms, the proposed scheme consists of two categories of agents; a strategy generation agent inspired by learning classifier system, and strategy coordination inspired by Artificial Immune System mechanisms. An experiment in a realistic environment shows that the adopted hybrid approach can be used to learn weapon-target assignment for multiple unmanned combat systems to successfully defend against coordinated attacks. The presented results show the potential for hybrid approaches for an intelligent system enabling adaptable and collaborative systems.

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A Novel Uncertainty Management Approach for Air Combat Situation Assessment Based on Improved Belief Entropy

Cited by 16 publications

References 35 publications

A Hybrid Mobile Robot Path Planning Scheme Based on Modified Gray Wolf Optimization and Situation Assessment

A Hybrid Mobile Robot Path Planning Scheme Based on Modified Gray Wolf Optimization and Situation Assessment

Computer Assisted Wargame for Military Capability-Based Planning

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

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