Kill-Probability-Maximization Guidance: Breaking from the Miss-Distance-Minimization Paradigm
For missile guidance systems, this work shifts the objective from miss distance to kill probability, offering a new paradigm that improves effectiveness against nonnominal targets.
This paper proposes a guidance methodology that directly maximizes the single-shot kill probability (SSKP) instead of minimizing miss distance, achieving consistent SSKP improvement over standard and estimation-aware guidance laws in Monte Carlo simulations against both nominal and nonnominal evasively maneuvering targets.
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.