RASC Self-Calibration Cuts Sensor Drift by 71% with 4x Lower Bandwidth

BJT-based 2D temperature sensor arrays leave the factory calibrated to ±0.1°C, but once deployed, thermal and mechanical stresses cause per-sensor gain and offset parameters to drift by an order of magnitude — making lab recalibration impractical. Yinglei Ma and Fei Xiao introduce RASC (Region-Aware Self-Calibration), a five-stage algorithm that tackles this ill-posed global problem by breaking it into local cluster-level subproblems. Inside each cluster, it runs a robust alternating estimation pipeline combining trimmed-mean field reconstruction with Huber iterative reweighted least squares (IRLS). Overlapping estimates from neighboring clusters are then reconciled via linear consensus on a cluster-overlap graph, which provably converges exponentially.

Real-world validation on 7,632 frames from a deployed 16×16 array exhibiting about 5x factory-spec non-uniformity showed RASC cutting the locally non-smooth fixed-pattern residual by 71±5% (10-fold cross-validation). It restored the original ±0.1°C accuracy while perturbing the calibrated field by only 0.041°C RMSE, with edge-located sensors benefiting most (78% reduction vs 55% interior). Simulations on 8×8 to 32×32 arrays further demonstrated that RASC matches an oracle centralized extended Kalman filter within 0.10°C while requiring roughly 4x lower communication bandwidth — a critical advantage for distributed sensor networks. This work makes in-field self-calibration practical for dense arrays used in industrial monitoring, IoT, and scientific instrumentation.