RASC: Region-Aware Self-Calibration for Dense 2D Sensor Arrays
For operators of dense sensor arrays (e.g., temperature monitoring), RASC provides a practical, low-bandwidth self-calibration method that eliminates the need for in-lab recalibration.
RASC is a self-calibration algorithm for dense 2D sensor arrays that reduces fixed-pattern noise by 71±5% on a deployed 16x16 array, restoring ±0.1°C accuracy despite post-deployment drift, and matches an oracle centralized EKF within 0.10°C with ~4x lower bandwidth in simulations.
BJT-based 2D temperature-sensor arrays are factory-calibrated to +/-0.1 degC, but post-deployment thermal and mechanical stresses drift their per-sensor gain-offset parameters by an order of magnitude, and in-lab recalibration is impractical. We present RASC (Region-Aware Self-Calibration), a five-stage algorithm that decomposes the global ill-posed problem into local cluster-level problems, runs robust alternating estimation (trimmed-mean field reconstruction + Huber IRLS) inside each cluster, and reconciles overlapping estimates by linear consensus on the cluster-overlap graph with provable exponential convergence. On 7,632 frames from a deployed 16x16 array exhibiting ~5x factory-spec non-uniformity, RASC cuts the locally-non-smooth fixed-pattern residual by 71+/-5% (10-fold CV), restoring +/-0.1 degC accuracy while perturbing the calibrated field by only 0.041 degC RMSE; reduction concentrates at the edges (78% vs 55% interior). In simulations on 8x8 to 32x32 arrays, RASC matches an oracle centralized EKF within 0.10 degC with ~4x lower bandwidth.