A Kalman Filter Based Approach to NV Diamond Data Fusion For Improved Temperature Sensing

Nitrogen-vacancy (NV) centers in diamond have been demonstrated to enable highly sensitive temperature measurements using multiple modalities. Standalone optically detected magnetic resonance (ODMR) provides robust temperature estimates, albeit with high latency, whereas all-optical measurements provide millisecond resolution but suffer from poorer long-term accuracy. In this work, we demonstrate a hot-start Kalman filtering approach that fuses the two modalities, leading to a 57% improvement in accuracy. The fused estimate achieves higher long-term accuracy with lower latency, demonstrating a viable route toward implementing self-correcting, high-precision NV-diamond temperature sensing schemes.