A Majorization-Minimization Algorithm for Hybrid TOA-RSS Based Localization in NLOS Environment

This letter investigates the problem of accurate localization of a target node in wireless sensor networks using time of arrival (TOA) and received signal strength (RSS) measurements in an adverse non-line of sight (NLOS) environment. The proposed methodology works without any requirement of the NLO...

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Bibliographic Details
Published in:IEEE communications letters Vol. 26; no. 5; pp. 1017 - 1021
Main Authors: Panwar, Kuntal, Katwe, Mayur, Babu, Prabhu, Ghare, Pradnya, Singh, Keshav
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Complexity
Computational efficiency
Convergence
Heuristic algorithms
Linear programming
Localization
Location awareness
majorization minimization (MM)
Network latency
non-line of sight (NLOS) propagation
Nonlinear optics
Optimization
received signal strength (RSS)
Signal strength
Time measurement
Time of arrival (TOA)
Wireless sensor networks
ISSN:1089-7798, 1558-2558
Online Access:Get full text
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Summary:This letter investigates the problem of accurate localization of a target node in wireless sensor networks using time of arrival (TOA) and received signal strength (RSS) measurements in an adverse non-line of sight (NLOS) environment. The proposed methodology works without any requirement of the NLOS path identification or the prior knowledge of NLOS bias distribution. A non-linear weighted least squares (NLWLS) problem is formulated through general approximations on the hybrid data model. The formulated NLWLS problem is solved using a computationally efficient majorization-minimization (MM) algorithm in which the NLWLS objective is iteratively minimized via simple update steps. The proposed MM algorithm is guaranteed to converge to a stationary point of the NLWLS objective. Simulation results and computational complexity analysis validate that the proposed MM algorithm attains fast convergence with lower latency. Moreover, the proposed hybrid localization algorithm outperforms the state-of-art methods in terms of estimation accuracy and computational complexity.
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2022.3155685