Abstract
In the Industry 4.0 era, it is a common situation that measurements are available with different resolutions (or granularities). Multiresolution (or multi-granularity) data can arise, for instance, from aggregation rules applied over measurements collected in batches during non-overlapping periods of time, or in composite sampling schemes (e.g., using industrial auto-sampler devices). Current solutions for optimal data fusion, such as Kalman filters (KF) and Moving Horizon Estimation (MHE) approaches, assume that all variables are measured at the same resolution, even if their acquisition rates are different. Therefore, they are unable to account for the multiresolution structure of data. In this article we introduce a multiresolution framework for the optimal fusion of multiple signals at distinct resolutions. This approach is compared against the benchmark alternative using simulated case studies, where the superior signal processing capability of the novel methodology is demonstrated.
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