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Adaptive Measurement Noise for Robust Kalman Filtering in Smart Beehive Telemetry

Honeybee colony monitoring generates multimodal, non-stationary telemetry streams that require reliable recursive state estimation with well-calibrated uncertainty for digital apiculture. Although Kalman filtering is widely used in environmental monitoring, adaptive measurement-noise modeling has not been systematically evaluated for smart-hive telemetry under leak-free chronological protocols. The approach performs online measurement-noise covariance adaptation using innovation statistics identified as the primary source of calibration improvement, while an innovation-based normalized innovation squared (NIS) gate serves as a secondary robustness safeguard against rare extreme observations.