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Proceedings ArticleDOI

Artificial Intelligence learning based on proportional navigation guidance

TLDR
This research proposes the use of AI for guidance of an Unmanned Aerial Vehicle (UAV) to a maneuvering target and an orthogonal array based training strategy is proposed that provides a better training and reduced miss distance values.
Abstract
Artificial Intelligence (AI) is a vast domain with variety of applications. This research proposes the use of AI for guidance of an Unmanned Aerial Vehicle (UAV) to a maneuvering target. The AI agent is trained using machine learning algorithm. The PN guidance algorithm is used as a basis for training the agent. An orthogonal array based training strategy is proposed that provides a better training and reduced miss distance values. The AI based guidance provides performance equal to the PN Guidance law and in some cases it outperforms PN guidance law.

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Citations
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Proceedings ArticleDOI

Artificial Intelligence guidance for Unmanned Aerial Vehicles in three dimensional space

TL;DR: This research proposes use of Artificial Intelligence for dynamic target tracking capability of an UAV and the AI guidance algorithm performance is compared against standard guidance algorithm like Proportional Navigation Guidance (PNG) algorithm.
Journal ArticleDOI

Guidance law of interceptors against a high-speed maneuvering target based on deep Q-Network:

TL;DR: In this paper, a novel guidance law for intercepting a high-speed maneuvering target based on deep reinforcement learning is proposed, which mainly includes the interceptor-target relative motion model.
Journal ArticleDOI

Aerial vehicle guidance based on passive machine learning technique

TL;DR: A completely new guidance scheme based on Q-learning whose performance is better than standard guidance schemes is proposed, which can be used in various aerial guidance applications to reach a dynamically moving target in three-dimensional space.
Journal ArticleDOI

Adaptive True Proportional Navigation Guidance Based On Heuristic Optimization Algorithms

TL;DR: The heuristic optimisation approach is used at the first time to update the navigation constant of PN-guidance and shows that while the missile guided by ATPN is maneuvering, it is exposed to less acceleration and less strain.
Proceedings ArticleDOI

Deep reinforcement learning based missile guidance law design for maneuvering target interception

TL;DR: In this paper, a novel guidance law based on deep reinforcement learning (DRL) algorithm is presented to deal with the maneuvering target interception problem, where a missile target interception environment model under the framework of DRL is constructed.
References
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Book

Artificial Intelligence: A Modern Approach

TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
Journal Article

Near-optimal Regret Bounds for Reinforcement Learning

TL;DR: For undiscounted reinforcement learning in Markov decision processes (MDPs), this paper presented a reinforcement learning algorithm with total regret O(DS√AT) after T steps for any unknown MDP with S states, A actions per state, and diameter D.
Journal ArticleDOI

Proportional Navigation With Delayed Line-of-Sight Rate

TL;DR: It is observed that in the presence of line-of-sight rate delay, increasing the effective navigation constant of the PN guidance law deteriorates its performance.
Proceedings ArticleDOI

Biased PN based impact angle constrained guidance using a nonlinear engagement model

TL;DR: A modified ACBPNG (MACBPNG) where the required bias term is derived in a closed form considering non-linear equations of motion, which is capable of achieving a wide range of impact angles.
Journal ArticleDOI

Current Trends in Tactical Missile Guidance

TL;DR: A brief survey of the existing techniques and current trends in tactical missile guidance is presented in this paper, where the authors present a brief review of the current techniques and trends in this field.