Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion Method
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 …
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 …
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