4.7 Article

Optimal Sensor Placement for Source Localization: A Unified ADMM Approach

期刊

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 71, 期 4, 页码 4359-4372

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2022.3146603

关键词

Location awareness; Sensor placement; Noise measurement; Geometry; Optimization; Sea measurements; Three-dimensional displays; Optimal sensor placement; source localization; Cramer-Rao lower bound; alternating direction method of multipliers; majorization-minimization

资金

  1. ERC AGNOSTIC [EC/H2020/ERC2016ADG/742648]
  2. FNR CORE SPRINGER [C18/IS/12734677]

向作者/读者索取更多资源

This paper explores the importance of sensor placement in source localization and develops a unified optimization framework (UTMOST) to determine the optimal sensor placements.
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 Cramer-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 unified optimization framework for optimal sensor placement (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.

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