Comprehensive Approach to Directly Addressing Estimation Delays in Stochastic Guidance
Existing guidance laws fail because they assume constant estimation delays during pursuit-evasion scenarios where maneuvers are abrupt. This work introduces a unified framework that treats these delays as time-varying, using a real-time semi-Markov process to estimate them and a fixed-lag particle smoother to provide delay-consistent state estimates. The resulting TV-DGLCC guidance law significantly improves interception performance against challenging evasion maneuvers.