Measurement as Constraint Enforcement: A Methodological Framework for High-Fidelity Observation

This paper presents a methodological framework for physical measurement based on the principle of constraint enforcement rather than passive signal acquisition. It argues that high-fidelity observation arises primarily from the active elimination of physically impossible states using known constraints such as conservation laws, geometry, causality, and system dynamics. By introducing a formal distinction between Passive Sampling Observers (PSOs) and Constraint-Enforcing Observers (CEOs), the paper unifies existing ideas from Bayesian inference, constrained state estimation, and information theory under a single design heuristic. The framework does not introduce new physics; instead, it provides a practical taxonomy and design philosophy for building more efficient, robust, and diagnostically powerful measurement systems, with direct relevance to instrumentation, metrology, robotics, and autonomous sensing.