Accession Number:

ADA566684

Title:

Semi-Definite Programming Relaxation for Non-Line-of-Sight Localization

Descriptive Note:

Technical rept.

Corporate Author:

CALIFORNIA UNIV BERKELEY DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

Personal Author(s):

• Ekambaram, Venakatesan
• Fanti, Giulia
• Ramchandran, Kannan

Report Date:

2012-08-18

Pagination or Media Count:

9.0

Abstract:

We consider the problem of estimating the locations of a set of points in a k-dimensional euclidean space given a subset of the pairwise distance measurements between the points. We focus on the case when some fraction of these measurements can be arbitrarily corrupted by large additive noise. Given that the problem is highly non-convex, we propose a simple semidefinite programming relaxation that can be efficiently solved using standard algorithms. We define a notion of non-contractibility and show that the relaxation gives the exact point locations when the underlying graph is non-contractible. The performance of the algorithm is evaluated on an experimental data set obtained from a network of 44 nodes in an indoor environment and is shown to be robust to non-line-of-sight errors.

Descriptors:

• *POSITION(LOCATION)
• ALGORITHMS
• DETECTORS
• MATHEMATICAL PROGRAMMING
• RELAXATION

Subject Categories:

• Operations Research
• Target Direction, Range and Position Finding

Distribution Statement:

APPROVED FOR PUBLIC RELEASE