Kill-Probability-Maximization Guidance: Breaking from the Miss-Distance-Minimization Paradigm

TL;DR

This paper introduces a guidance law that directly maximizes the single-shot kill probability (SSKP) instead of minimizing miss distance, improving effectiveness against various target scenarios.

Contribution

It proposes a novel guidance paradigm based on Bayesian decision theory and differential games, enhancing kill probability in complex target engagement scenarios.

Findings

Monte Carlo simulations show improved SSKP over traditional guidance laws.

The new guidance law performs well against both nominal and evasively maneuvering targets.

It effectively accounts for probabilistic warhead lethality in guidance design.

Abstract

Classical guidance laws aim at minimizing the miss distance, thus implicitly determining the minimum warhead lethality radius required against nominal targets. However, nonnominal targets or scenarios might render the designed warhead insufficient, causing a significant degradation in the single-shot kill probability (SSKP). We propose a guidance methodology that shifts the interceptor's objective from minimizing the miss distance to directly maximizing the SSKP, while taking into account the warhead's probabilistic lethality model. Complying with the generalized separation theorem, the new paradigm is based on modifying deterministic differential-game-based guidance laws using Bayesian decision theory. Extensive Monte Carlo simulations demonstrate consistent SSKP improvement over the standard and recently introduced estimation-aware guidance laws, when tested against nominal and nonnominal evasively maneuvering targets.