Paper Info
Title
Tensor Data Conformity Evaluation For Interference-Resistant Localization
Abstract
We consider the problem of robust, interference-resistant localization in GPS-denied environments. Each asset to be self-localized is equipped with an antenna array and leverages time-domain coded beacon signals from anchor nodes that are placed at known locations. Collected data snapshots over time at the antenna array are organized in a tensor data structure. The conformity of the received tensor data is evaluated through iterative projections on robust, high-confidence data feature characterizations that are returned by L1-norm tensor subspaces. Non-conforming tensor slabs are more likely to be contaminated by irregular, highly deviating measurements due to interference, thus they are removed from the received dataset. Subsequently, we estimate the direction-of-arrival of the beacon signals by using L2-norm and L1-norm tensor decomposition techniques on the conformity-adjusted dataset. Finally, the relative position of the asset to the anchor nodes is estimated via triangulation. We consider two anchor nodes, one interferer, and one asset to be self-localized using radio frequency signals at the 2.4 GHz ISM band in an indoor laboratory environment. We evaluate the performance of the proposed localization system in terms of angle-of-arrival estimation accuracy experimental measurements from a software-defined radio testbed.
Year
DOI
Venue
2019
10.1109/IEEECONF44664.2019.9048697
CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS
DocType
ISSN
Citations 
Conference
1058-6393
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
konstantinos tountas1152.51
George Sklivanitis2497.11
Dimitris Pados320826.49
Michael J. Medley433726.06