Unified Estimation-Guidance Framework Based on Bayesian Decision Theory
This work modifies classical guidance laws for interceptors by incorporating Bayesian decision theory to handle estimation errors in uncertain interception scenarios. The core contribution is a unified framework that uses an Interacting Multiple Model Particle Filter to estimate target states and then employs a new law, Information-Enhancement Trajectory Shaping (IETS), to exploit decision ambiguity for superior performance.