Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse Array Selection Across Arbitrary Sensor Geometries with Deep
  Transfer Learning   Sparse sensor array selection arises in many engineering applications, where
it is imperative to obtain maximum spatial resolution from a limited number of
array elements. Recent research shows that computational complexity of array
selection is reduced by replacing the conventional optimization and greedy
search methods with a deep learning network. However, in practice, sufficient
and well-calibrated labeled training data are unavailable and, more so, for
arbitrary array configurations. To address this, we adopt a deep transfer
learning (TL) approach, wherein we train a deep convolutional neural network
(CNN) with data of a source sensor array for which calibrated data are readily
available and reuse this pre-trained CNN for a different, data-insufficient
target array geometry to perform sparse array selection. Numerical experiments
with uniform rectangular and circular arrays demonstrate enhanced performance
of TL-CNN on the target model than the CNN trained with insufficient data from
the same model. In particular, our TL framework provides approximately 20%
higher sensor selection accuracy and 10% improvement in the
direction-of-arrival estimation error.

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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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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: stat
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The categories of this paper are: q-alg
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The categories of this paper are: econ