Name
Abstract | ||
|---|---|---|
This letter is concerned with multi-modal data fusion (MMDF) under unexpected modality failures in nonlinear non-Gaussian dynamic processes. An efficient framework to tackle this problem is proposed. In particular, a notion termed modality "usefulness," which takes a value of 1 or 0, is used for indicating whether the observation of this modality is useful or not. For n modalities involved, 2(n) combinations of their "usefulness" values exist. Each combination defines one hypothetical model of the true data generative process. Then the problem of concern is formalized as a task of nonlinear non-Gaussian state filtering under model uncertainty, which is addressed by a dynamic model averaging (DMA) based particle filter (PF) algorithm. This DMA algorithm employs 2(n) models, while all models share the same state-transition function and a unique set of particle values. That makes its computational complexity only slightly larger than a single model based PF algorithm, especially for scenarios in which n is small. Experimental results show that the proposed solution outperforms remarkably state-of-the-art methods. Code and data are available at https://github.com/robinlau1981/fusion. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LSP.2021.3117731 | IEEE SIGNAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Multi-modal data fusion, robust data fusion, model uncertainty, nonlinear non-Gaussian systems, particle filter | Journal | 28 |
Issue | ISSN | Citations |
1 | 1070-9908 | 0 |
PageRank | References | Authors |
0.34 | 0 | 1 |