TY - GEN
T1 - Robust Graph Localization for Underwater Acoustic Networks
AU - Sklivanitis, George
AU - Markopoulos, Panos P.
AU - Pados, Dimitris A.
AU - Diamant, Roee
N1 - Funding Information: This research was supported in part by the U.S. NSF under grants CNS-1753406 and OAC-1808582, by the U.S. AFOSR under YIP, by the MOST-BMBF German-Israeli Cooperation in Marine Sciences 2018-2020, and by the MOST action for Agriculture Environment and Water for year 2019. Publisher Copyright: © 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - We consider the problem of robust localization of a set of underwater network nodes, based on pairwise distance measurements. Localization plays a key role in underwater network optimization, as accurate node positioning enables location-aware scheduling, data routing, and geo-referencing of the collected underwater sensor data. State-of-the-art graph localization approaches include variations of the classical multidimensional scaling (MDS) algorithm, modified to handle unlabelled, missing, and noisy distance measurements. In this paper, we present MAD-MDS, a robust method for graph localization from incomplete and outlier corrupted pair-wise distance measurements. The proposed method first conducts outlier excision by means of Median Absolute Deviation (MAD). Then, MAD-MDS performs rank-based completion of the distance matrix, to estimate missing measurements. As a last step, MAD-MDS applies MDS to the reconstructed distance matrix, to estimate the coordinates of the underwater network nodes. Numerical studies on both sparsely and fully connected network graphs as well as on data from past sea experiments corroborate that MAD-MDS attains high coordinate-estimation performance for sparsely connected network graphs and high corruption variance.
AB - We consider the problem of robust localization of a set of underwater network nodes, based on pairwise distance measurements. Localization plays a key role in underwater network optimization, as accurate node positioning enables location-aware scheduling, data routing, and geo-referencing of the collected underwater sensor data. State-of-the-art graph localization approaches include variations of the classical multidimensional scaling (MDS) algorithm, modified to handle unlabelled, missing, and noisy distance measurements. In this paper, we present MAD-MDS, a robust method for graph localization from incomplete and outlier corrupted pair-wise distance measurements. The proposed method first conducts outlier excision by means of Median Absolute Deviation (MAD). Then, MAD-MDS performs rank-based completion of the distance matrix, to estimate missing measurements. As a last step, MAD-MDS applies MDS to the reconstructed distance matrix, to estimate the coordinates of the underwater network nodes. Numerical studies on both sparsely and fully connected network graphs as well as on data from past sea experiments corroborate that MAD-MDS attains high coordinate-estimation performance for sparsely connected network graphs and high corruption variance.
KW - Underwater acoustics
KW - corrupted distance measurements
KW - graph localization
KW - missing data
KW - robust MDS
UR - http://www.scopus.com/inward/record.url?scp=85123285457&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/ucomms50339.2021.9598114
DO - https://doi.org/10.1109/ucomms50339.2021.9598114
M3 - Conference contribution
T3 - 2021 5th Underwater Communications and Networking Conference, UComms 2021
BT - 2021 5th Underwater Communications and Networking Conference, UComms 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th Underwater Communications and Networking Conference, UComms 2021
Y2 - 31 August 2021 through 2 September 2021
ER -