Vibration-Induced Noise in Mobile NV-Center Magnetometers: Platform Characterization and Rejection Strategy Comparison

This record contains the Python simulation framework, manuscript source files, generated figures, and supplementary materials accompanying: Anderson II, J.D. (2026). Vibration-Induced Noise in Mobile NV-Center Magnetometers: Platform Characterization and Rejection Strategy Comparison. The work investigates vibration-induced noise in mobile NV-center magnetometers by modeling the coupling of platform acceleration power spectral densities (PSDs) to equivalent magnetic field noise through displacement-based transduction. Three vibration mitigation strategies are evaluated: passive mechanical isolation, accelerometer-based feed-forward cancellation, and digital filtering using notch filters and Kalman filtering. Simulations are performed across three representative deployment scenarios: multirotor UAVs, ground robots, and handheld systems. Parameter uncertainty is explored through Monte Carlo analysis spanning bias-magnet gradient, MEMS accelerometer noise floor, calibration error, and Kalman filter tuning assumptions. The combined mitigation pipeline achieves modeled noise reductions of approximately 3,000× to 17,000×, reducing vibration-induced noise to within 8× to 37× of the nominal shot-noise floor under the assumptions considered. Results identify the bias-magnet gradient (dB/dx) as a dominant system-level design parameter and highlight limitations of passive isolation for low-frequency vibration environments due to displacement coupling scaling as 1/(2πf)⁴. Included materials: PDF manuscript (preprint) Python simulation code Figure generation scripts Generated figures LaTeX source files Supplementary materials required for reproducibility These materials are provided as a reproducible foundation for further investigation of mobile quantum sensing systems and future experimental validation.