Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Joint Detection and Super-Resolution Estimation of Multipath Signal
  Parameters Using Incremental Automatic Relevance Determination   The presented work investigates a sparse Bayesian incremental automatic
relevance determination (IARD) algorithm in the context of multipath parameter
estimation in a super-resolution regime. The corresponding estimation problem
is highly nonlinear and, in general, requires an estimation of the number of
multipath components. In the IARD approach individual multipath components are
processed sequentially, which permits a tractable convergence analysis of the
corresponding inference expressions. This leads to a simple condition, termed
here a pruning condition, that determines if a multipath component is
"sparsified" or retained in the model, thus permitting a fast and adaptive
realization of the estimation algorithm. Yet previous experiments demonstrated
that IARD fails to select the correct number of components when the parameters
entering nonlinearly the multipath model are also estimated. To understand this
effect, an analysis of the statistical structure of the pruning condition is
proposed. It is shown that the corresponding test statistic in the pruning
condition follows an extreme value distribution. As a result, the standard IARD
algorithm implements a statistical test with a very high probability of false
alarm. This leads to insertion of estimation artifacts and underestimation of
signal sparsity. Moreover, the probability of false alarm worsens as the number
of measured signal samples grows. Based on the developed statistical
interpretation of the IARD, an optimal adjustment of the pruning condition is
proposed. This permits a reliable and efficient removal of estimation artifacts
and joint estimation of signal parameters, as well as optimal model order
selection within a sparse Bayesian learning framework.

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The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
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The categories of this paper are: econ
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an