Multiple Hypothesis Bayesian Inference for Impact Point Prediction of Quasi‐Ballistic Missiles Under Manoeuvre Constraints

ABSTRACT

This paper proposes a Multiple Hypothesis Bayesian Inference (MHBI) framework for intent‐aware ground impact point (GIP) prediction of quasi‐ballistic missiles (QBM) under manoeuvre constraints. Although conventional methods often overgeneralise target manoeuvres—leading to delayed convergence and inconsistent GIP prediction results—the proposed approach structurally addresses these limitations through two main contributions. First, we analytically quantify the dynamic energy consumption and discuss thermo‐structural coupled loads during QBM pull‐up manoeuvres, which are severely limited by energy dissipation. Second, we integrate these derived physical manoeuvre limits and the target's intent to strike specific high‐value defence assets into the MHBI framework. By employing a dynamic pruning and sequential probability ratio test logic within the optimised search space, the framework effectively narrows the potential impact area even during terminal phases. Simulation results demonstrate that the proposed algorithm achieves significantly faster and more robust GIP confirmation compared to existing methods, offering a computationally efficient and practically deployable solution for time‐critical missile defence operations.