Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparsity-Based Kalman Filters for Data Assimilation   Several variations of the Kalman filter algorithm, such as the extended
Kalman filter (EKF) and the unscented Kalman filter (UKF), are widely used in
science and engineering applications. In this paper, we introduce two
algorithms of sparsity-based Kalman filters, namely the sparse UKF and the
progressive EKF. The filters are designed specifically for problems with very
high dimensions. Different from various types of ensemble Kalman filters
(EnKFs) in which the error covariance is approximated using a set of dense
ensemble vectors, the algorithms developed in this paper are based on sparse
matrix approximations of error covariance. The new algorithms enjoy several
advantages. The error covariance has full rank without being limited by a set
of ensembles. In addition to the estimated states, the algorithms provide
updated error covariance for the next assimilation cycle. The sparsity of error
covariance significantly reduces the required memory size for the numerical
computation. In addition, the granularity of the sparse error covariance can be
adjusted to optimize the parallelization of the algorithms.

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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: cs
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph