Tensor Kalman Filter and Its Applications

Publication Date

1-1-2022

Document Type

Article

Publication Title

IEEE Transactions on Knowledge and Data Engineering

DOI

10.1109/TKDE.2022.3169129

Abstract

Kalman filter is one of the most important estimation algorithms, which estimates certain unknown variables given the measurements observed over time subject to a dynamic system, for many applications in science and engineering including environmental science, ecometrics, robotics, financial analysis, data mining, etc. It is often necessary to characterize multiple relationships among various kinds of signals/data in tensor form. The conventional Kalman filter paradigm is based on the low-dimensional state-space representation, which is restricted by the state-transition, observation-model, process-noise covariance, and observation-noise covariance matrices. However, we often need to express some or all of them in terms of tensors in practice. Very lately, the aforementioned Kalman filter in tensor form was tackled using tensor decomposition but the exact estimator has never been established so far. In this work, we propose a new generalized Kalman filter framework consisting of state, state-transition model, observation-model, process-noise covariance, and observation-noise covariance tensors of arbitrary orders by applying the ShermanMorrisonWoodbury identity and block tensor inverse, which we call "Tensor Kalman Filter" (TKF). Our proposed new approach can produce the exact Kalman filter estimator without any need of tensor decomposition (approximation). The pertinent computational- and memory-complexity studies are also provided in this paper.

Keywords

Covariance matrices, EEG (electroencephalogram), Electroencephalography, expectation-maximization (EM) algorithm, Heuristic algorithms, Kalman filters, Mathematical models, multi-relational data, Tensor, tensor Kalman filter (TKF), tensor Kalman smoother (TKS), Tensors, Time series analysis

Department

Applied Data Science

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