Bang-Bang Evasion: Its Stochastic Optimality and a Terminal-Set-Based Implementation
For missile defense and pursuit-evasion game theory, this work extends the optimality of bang-bang evasion from deterministic to stochastic settings, providing a theoretically grounded and practically superior evasion strategy.
The paper proves that optimal evasion in stochastic planar engagements with bounded controls is always achievable by a bang-bang strategy, and introduces a terminal-set-based evasion (TSE) method that outperforms traditional stochastic evasion strategies (random telegraph, Singer, weaving) in Monte Carlo simulations against a proportional navigation pursuer.
We address the problem of optimal evasion in a planar endgame engagement, where a target with bounded lateral acceleration seeks to avoid interception by a missile guided by a linear feedback law. Contrary to existing approaches, that assume perfect information or use heuristic maneuver models in stochastic settings, we formulate the problem in an inherently stochastic framework involving imperfect information and bounded controls. Complying with the generalized separation theorem, the control law factors in the posterior distribution of the state. We extend the well-known optimality of bang-bang evasion maneuvers in deterministic settings to the realm of realistic, stochastic evasion scenarios. First, we prove that an optimal evasion strategy always exists, and that the set of optimal solutions includes at least one bang-bang policy, rendering the resulting optimal control problem finite-dimensional. Second, leveraging this structure, we propose the closed-loop terminal-set-based evasion (TSE) strategy, and demonstrate its effectiveness in simulation against a proportional navigation pursuer. Monte Carlo simulations show that the TSE strategy outperforms traditional stochastic evasion strategies based on random telegraph, Singer, and weaving models.