Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Localization Using Range Measurements with Unknown and Bounded
  Errors   Cooperative geolocation has attracted significant research interests in
recent years. A large number of localization algorithms rely on the
availability of statistical knowledge of measurement errors, which is often
difficult to obtain in practice. Compared with the statistical knowledge of
measurement errors, it can often be easier to obtain the measurement error
bound. This work investigates a localization problem assuming unknown
measurement error distribution except for a bound on the error. We first
formulate this localization problem as an optimization problem to minimize the
worst-case estimation error, which is shown to be a non-convex optimization
problem. Then, relaxation is applied to transform it into a convex one.
Furthermore, we propose a distributed algorithm to solve the problem, which
will converge in a few iterations. Simulation results show that the proposed
algorithms are more robust to large measurement errors than existing algorithms
in the literature. Geometrical analysis providing additional insights is also
provided.

15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an