Dynamic Coupling in Cognitive Guidance Systems Under Variable Inertial and Gravitational Metric Perturbations: A Prospective Conceptual Framework

This document presents a conceptual analysis of the control, state estimation, computational latency, and sensory perception challenges that would arise in autonomous navigation systems equipped with hypothetical field actuators capable of dynamically modifying local inertial mass and the metric tensor. It is argued that conventional Kalman filters diverge under rapid mass variations, and an adaptive Hamiltonian estimation architecture (H-AKF) is proposed. The fundamental limit imposed by silicon latency under extreme acceleration regimes is analysed, suggesting the use of asynchronous event-driven vision sensors (DVS) and in-memory spiking neural networks (SNN). The loss of environmental neutrality due to self-induced gravitational lensing is discussed, and the concept of a real-time inverse conformal transformation module (CSTD) is introduced to rectify sensory telemetry. Finally, three catastrophic failure modes are defined. This work presents no empirical or simulation results; its purpose is to identify structural problems and outline directions for future research at the intersection of applied relativity, neuromorphic computing, and adaptive control.