Probabilistic Weapon Engagement Zones for a Turn Constrained Pursuer

TL;DR

This paper introduces probabilistic engagement zones for evaders to avoid capture by turn-constrained pursuers under uncertainty, using multiple approximation methods and integrating into trajectory optimization.

Contribution

It derives an analytic deterministic engagement zone and extends it to a probabilistic framework with four uncertainty propagation methods, enabling safer evader trajectories.

Findings

Quadratic approximation balances accuracy and computational cost.

Neural-network regression offers fast approximation with acceptable accuracy.

Probabilistic constraints improve evader safety in uncertain pursuit scenarios.

Abstract

Curve-straight probabilistic engagement zones (CSPEZ) quantify the spatial regions an evader should avoid to reduce capture risk from a turn-rate-limited pursuer following a curve-straight path with uncertain parameters including position, heading, velocity, range, and maximum turn rate. This paper presents methods for generating evader trajectories that minimize capture risk under such uncertainty. We first derive an analytic solution for the deterministic curve-straight basic engagement zone (CSBEZ), then extend this formulation to a probabilistic framework using four uncertainty-propagation approaches: Monte Carlo sampling, linearization, quadratic approximation, and neural-network regression. We evaluate the accuracy and computational cost of each approximation method and demonstrate how CSPEZ constraints can be integrated into a trajectory-optimization algorithm to produce safe paths that explicitly account for pursuer uncertainty.