The plasma blackout phase of hypersonic atmospheric re-entry presents one of the most demanding navigation challenges for reusable launch vehicles. During this critical window, lasting several minutes, the vehicle is enveloped in an ionized sheath that renders external radio-frequency positioning and communication unreliable or unavailable. Navigation must then rely primarily on inertial measurement units that accumulate drift, while abrupt aerodynamic transients and control actions produce slew-like residual spikes. This paper applies the Drift--Slew Fusion Bootstrap (DSFB) framework---a deterministic, trust-adaptive residual correction mechanism formally specified in prior work---to the plasma-blackout navigation problem. By performing causal drift--slew decomposition, bounded-increment slew detection, and channel-local trust weighting with provably bounded corrections, DSFB enables robust multi-sensor state estimation without requiring probabilistic noise models or covariance tuning. High-fidelity 6-DoF re-entry simulations representative of Starship-class vehicles, including Monte-Carlo dispersion analysis and post-blackout Starlink reacquisition, demonstrate that DSFB maintains stable estimation behavior throughout the blackout window. These results illustrate the capability of the DSFB framework for robust navigation in communication-denied hypersonic environments and provide a foundation for further development in reusable launch vehicle guidance systems.
Riaan de Beer (Fri,) studied this question.