TY - JOUR
T1 - Alternating Minimization Based First-Order Method for the Wireless Sensor Network Localization Problem
AU - Gur, Eyal
AU - Sabach, Shoham
AU - Shtern, Shimrit
N1 - Funding Information: Manuscript received February 14, 2020; revised August 13, 2020; accepted October 1, 2020. Date of publication October 16, 2020; date of current version November 20, 2020. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Alexander Bertrand. This work was partially supported by the Israeli Science Foundation under Grant 1460/19. (Corresponding author: Shimrit Shtern.) The authors are with the Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Technion city, Haifa 3200003, Israel (e-mail: [email protected]; [email protected]; [email protected]). Digital Object Identifier 10.1109/TSP.2020.3031695 Publisher Copyright: © 1991-2012 IEEE.
PY - 2020
Y1 - 2020
N2 - We propose an algorithm for the Wireless Sensor Network localization problem, which is based on the well-known algorithmic framework of Alternating Minimization. We start with a non-smooth and non-convex minimization, and transform it into an equivalent smooth and non-convex problem, which stands at the heart of our study. This paves the way to a new method which is globally convergent: not only does the sequence of objective function values converge, but the sequence of the location estimates also converges to a unique location that is a critical point of the corresponding (original) objective function. The proposed algorithm has a range of fully distributed to fully centralized implementations, which all have the property of global convergence. The algorithm is tested over several network configurations, and it is shown to produce more accurate solutions within a shorter time relative to existing methods.
AB - We propose an algorithm for the Wireless Sensor Network localization problem, which is based on the well-known algorithmic framework of Alternating Minimization. We start with a non-smooth and non-convex minimization, and transform it into an equivalent smooth and non-convex problem, which stands at the heart of our study. This paves the way to a new method which is globally convergent: not only does the sequence of objective function values converge, but the sequence of the location estimates also converges to a unique location that is a critical point of the corresponding (original) objective function. The proposed algorithm has a range of fully distributed to fully centralized implementations, which all have the property of global convergence. The algorithm is tested over several network configurations, and it is shown to produce more accurate solutions within a shorter time relative to existing methods.
KW - Alternating minimization
KW - Convergence
KW - Distance measurement
KW - Minimization
KW - Noise measurement
KW - Optimization
KW - Signal processing algorithms
KW - Wireless sensor networks
KW - distributed algorithms
KW - global convergence
KW - non-convex optimization
KW - non-smooth optimization
KW - wireless sensor network localization
UR - https://www.scopus.com/pages/publications/85097353616
U2 - 10.1109/TSP.2020.3031695
DO - 10.1109/TSP.2020.3031695
M3 - Article
SN - 1053-587X
VL - 68
SP - 6418
EP - 6431
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
M1 - 9226609
ER -