Optimal Sensor Placement for Source Localization: A Unified ADMM Approach

• Published in: IEEE transactions on vehicular technology Vol. 71; no. 4; pp. 4359 - 4372
• Main Authors: Sahu, Nitesh, Wu, Linlong, Babu, Prabhu, M. R., Bhavani Shankar, Ottersten, Bjorn
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: alternating direction method of multipliers; Angle of arrival; Cramér-Rao lower bound; Design criteria; Design modifications; Geometry; Localization; Location awareness; Lower bounds; majorization-minimization; Noise measurement; Optimal sensor placement; Optimization; Placement; Sea measurements; Sensor placement; Sensors; Signal strength; source localization; Three-dimensional displays; Underwater communication; Wireless communications
• ISSN: 0018-9545; 1939-9359; 1939-9359
• DOI: 10.1109/TVT.2022.3146603

Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival (AOA) and Received Signal Strength (RSS) based. Since the Cramér-Rao lower bounds (CRLB) of these methods depend on the sensor geometry explicitly, sensor placement becomes a crucial issue in source localization applications. In this paper, we consider finding the optimal sensor placements for the TOA, TDOA, AOA and RSS based localization scenarios. We first unify the three localization models by a generalized problem formulation based on the CRLB-related metric. Then a u nified op t imization fra m ework for o ptimal s ensor placemen t (UTMOST) is developed through the combination of the alternating direction method of multipliers (ADMM) and majorization-minimization (MM) techniques. Unlike the majority of the state-of-the-art works, the proposed UTMOST neither approximates the design criterion nor considers only uncorrelated noise in the measurements. It can readily adapt to to different design criteria (i.e. A, D and E-optimality) with slight modifications within the framework and yield the optimal sensor placements correspondingly. Extensive numerical experiments are performed to exhibit the efficacy and flexibility of the proposed framework.