One-vs-one Threat-Aware Weaponeering with Basic Engagement Zones

In this paper we address the problem of ‘weaponeering’, i.e., placing the weapon engagement zone (WEZ) of a vehicle on a moving target, while simultaneously avoiding the target's WEZ. A WEZ describes the lethality region of a range-limited weapon considering both the range of the weapon along with the state of the target. The weapons are assumed to have simple motion, while the vehicles carrying the weapons are modeled with Dubins dynamics. Three scenarios are investigated and are differentiated in the assumptions that can be made about the target in the process of the vehicle control design: 1) no knowledge of target control, 2) avoid unsafe positions assuming the target's optimal control, 3) full knowledge of target's optimal control. The engagement is formulated as a stochastic optimal control problem with uncertainty in the target's control modeled using a noise parameter applied to the target's control input. After discretizing the Hamilton-Jacobi-Bellman equation, Value iteration is then used to obtain an approximate solution for the optimal vehicle control and time-to-go. Simulation results support usage of the first paradigm: assume no knowledge of the target's control.

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Publication Details

Published
2025-05-14
DOI
https://doi.org/10.1109/icuas65942.2025.11007913
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
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article

One-vs-one Threat-Aware Weaponeering with Basic Engagement Zones

Isaac E. Weintraub, David W. Casbeer, Dejan Milutinović, Alexander Von Moll
Advanced Malware Detection Techniques
article

One-vs-one Threat-Aware Weaponeering with Basic Engagement Zones

Isaac E. Weintraub, David W. Casbeer, Dejan Milutinović, Alexander Von Moll
article en

Abstract

In this paper we address the problem of ‘weaponeering’, i.e., placing the weapon engagement zone (WEZ) of a vehicle on a moving target, while simultaneously avoiding the target's WEZ. A WEZ describes the lethality region of a range-limited weapon considering both the range of the weapon along with the state of the target. The weapons are assumed to have simple motion, while the vehicles carrying the weapons are modeled with Dubins dynamics. Three scenarios are investigated and are differentiated in the assumptions that can be made about the target in the process of the vehicle control design: 1) no knowledge of target control, 2) avoid unsafe positions assuming the target's optimal control, 3) full knowledge of target's optimal control. The engagement is formulated as a stochastic optimal control problem with uncertainty in the target's control modeled using a noise parameter applied to the target's control input. After discretizing the Hamilton-Jacobi-Bellman equation, Value iteration is then used to obtain an approximate solution for the optimal vehicle control and time-to-go. Simulation results support usage of the first paradigm: assume no knowledge of the target's control.

United States Air Force Research Laboratory (US), University of California, Santa Cruz (US)
Openalex Percentile: Top 87%
Advanced Malware Detection Techniques
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