Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion Method
This addresses sensor degradation and data fusion for improved signal estimation in applications like monitoring, but it is incremental as it builds on existing correction and fusion methods.
The paper tackles the problem of inferring ground-truth signals from multiple degraded sensor signals by learning a multiplicative degradation effect through iterative corrections and fusing noisy corrected signals using Gaussian processes, achieving convergence to the ground-truth in noiseless cases and handling data gaps effectively.
We present a novel method for inferring ground-truth signal from multiple degraded signals, affected by different amounts of sensor exposure. The algorithm learns a multiplicative degradation effect by performing iterative corrections of two signals solely from the ratio between them. The degradation function d should be continuous, satisfy monotonicity, and d(0) = 1. We use smoothed monotonic regression method, where we easily incorporate the aforementioned criteria to the fitting part. We include theoretical analysis and prove convergence to the ground-truth signal for the noiseless measurement model. Lastly, we present an approach to fuse the noisy corrected signals using Gaussian processes. We use sparse Gaussian processes that can be utilized for a large number of measurements together with a specialized kernel that enables the estimation of noise values of all sensors. The data fusion framework naturally handles data gaps and provides a simple and powerful method for observing the signal trends on multiple timescales(long-term and short-term signal properties). The viability of correction method is evaluated on a synthetic dataset with known ground-truth signal.