Engineering Note offers a framework for recovering kinematic information in sampled motion, suggesting practical assumptions for alias disambiguation.
This Zenodo release contains Engineering Note EN-001, Relative-Ratio Bookkeeping for Kinematic Aliasing: An Assumption-Driven Framework for Wrapped-Phase Recovery, by André Dupke, together with the accompanying reference implementation and synthetic validation material. The work addresses a core problem in sampled periodic motion: wrapped phase observations are non-injective, so the true motion cannot be uniquely recovered from aliased observations alone. Rather than proposing a universal de-aliasing method, the note provides a disciplined assumption-driven framework that makes the hidden information sources in alias recovery explicit. The release organizes alias disambiguation into four practical assumption classes: Known-start continuity - recovery by continuity-based phase unwrapping when motion begins in-band and evolves smoothly. Bounded two-rate congruence - recovery from two sample rates when the true frequency lies in a bounded interval. Fiducial timing - recovery using physical asymmetry or marker events that break rotational symmetry. External coarse prior - recovery by selecting the correct wrap class using a second sensor or coarse estimate. Included material documents the mathematical formulation, impossibility result without extra information, concise reference algorithms, and synthetic demonstrations showing that recovery succeeds only when the relevant assumption is supplied. The package also includes a clean pedagogical Python implementation (stt_dealias.py) and a more robust synthetic stress-test companion (stt_dealias_robust.py) covering dropout, jitter, abrupt jumps, confidence proxies, and missed fiducials. This release is intended as an engineering reference and teaching resource for researchers and practitioners working on sampled motion, phase wrapping, stroboscopic effects, rotor diagnostics, machine vision, wheel-speed estimation, and related sensing problems. It is not presented as a production-ready universal de-aliasing package, and the reported demonstrations are explicitly synthetic unless otherwise stated.
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Andre Dupke (2026) studied this question.
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