Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Novel Sparsity-Based Approach to Recursive Estimation of Dynamic
  Parameter Sets   We consider the problem of estimating a variable number of parameters with a
dynamic nature. A familiar example is finding the position of moving targets
using sensor array observations. The problem is challenging in cases where
either the observations are not reliable or the parameters evolve rapidly.
Inspired by the sparsity based techniques, we introduce a novel Bayesian model
for the problems of interest and study its associated recursive Bayesian
filter. We propose an algorithm approximating the Bayesian filter, maintaining
a reasonable amount of calculations. We compare by numerical evaluation the
resulting technique to state-of-the-art algorithms in different scenarios. In a
scenario with a low SNR, the proposed method outperforms other complex
techniques.

12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio