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Tracking algorithms such as the Kalman filter aim to improve inferenceperformance by leveraging the temporal dynamics in streaming observations.However, the tracking regularizers are often based on the $\ell_p$-norm whichcannot account for important geometrical relationships between neighboringsignal elements. We propose a practical approach to using the earth mover'sdistance (EMD) via the earth mover's distance dynamic filtering (EMD-DF)algorithm for causally tracking time-varying sparse signals when there is anatural geometry to the coefficient space that should be respected (e.g.,meaningful ordering). Specifically, this paper presents a new Beckmannformulation that dramatically reduces computational complexity, as well as anevaluation of the performance and complexity of the proposed approach inimaging and frequency tracking applications with real and simulatedneurophysiology data.

Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer