Dynamic and Robust Sensor Selection Strategies for Wireless Positioning With TOA/RSS Measurement

• Published in: IEEE transactions on vehicular technology Vol. 72; no. 11; pp. 1 - 16
• Main Authors: Oh, Myeung Suk, Hosseinalipour, Seyyedali, Kim, Taejoon, Love, David J., Krogmeier, James V., Brinton, Christopher G.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Approximation; Complexity; Convex analysis; Convexity; Cramér-Rao lower bound; Heuristic algorithms; Iterative methods; Lower bounds; Optimization; Position measurement; received signal strength; sensor selection; Sensors; Signal strength; Three-dimensional displays; time of arrival; Vehicle dynamics; Wireless communication; Wireless positioning; Wireless sensor networks
• DOI: 10.1109/TVT.2023.3279833
• ISSN: 0018-9545

Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this paper, we develop sensor selection strategies for 3D wireless positioning based on time of arrival (TOA) and received signal strength (RSS) measurements to handle two distinct scenarios: (i) known approximated target location, for which we conduct dynamic sensor selection to minimize the positioning error; and (ii) unknown approximated target location, in which the worst-case positioning error is minimized via robust sensor selection. We derive expressions for the Cramér-Rao lower bound (CRLB) as a performance metric to quantify the positioning accuracy resulted from selected sensors. For dynamic sensor selection, two greedy selection strategies are proposed, each of which exploits properties revealed in the derived CRLB expressions. These selection strategies are shown to strike an efficient balance between computational complexity and performance suboptimality. For robust sensor selection, we show that the conventional convex relaxation approach leads to instability, and then develop three algorithms based on (i) iterative convex optimization (ICO), (ii) difference of convex functions programming (DCP), and (iii) discrete monotonic optimization (DMO). Each of these strategies exhibits a different tradeoff between computational complexity and optimality guarantee. Simulation results show that the proposed sensor selection strategies provide significant improvements in terms of accuracy and/or complexity compared to existing sensor selection methods.