Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Some New Results on l1-Minimizing Nullspace Kalman Filtering for Remote
  Sensing Applications   This paper describes some new results on recursive l_1-minimizing by Kalman
filtering. We consider the l_1-norm as an explicit constraint, formulated as a
nonlinear observation of the state to be estimated. Interpretiing a sparse
vector to be estimated as a state which is observed from erroneous
(undersampled) measurements we can address time- and space-variant sparsity,
any kind of a priori information and also easily address nonstationary error
influences in the measurements available. Inherently in our approach we move
slightly away from the classical RIP-based approaches to a more intuitive
understanding of the structure of the nullspace which is implicitly related to
the well understood engineering concepts of deterministic and stochastic
observability in estimation theory

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The categories of this paper are: physics
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: acc-phys
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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
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The categories of this paper are: econ
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The categories of this paper are: bayes-an