Robust Dynamic Multi-Modal Data Fusion: A Model Uncertainty Perspective
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This paper 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 based particle filter algorithm. 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.
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