Mobility-Aided Wireless Sensor Network Localization via Semidefinite Programming

• Published in: IEEE transactions on wireless communications Vol. 12; no. 12; pp. 5966 - 5978
• Main Authors: Salari, Soheil, Shahbazpanahi, Shahram, Ozdemir, Kemal
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2013; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; convex programming; Detection, estimation, filtering, equalization, prediction; Distance measurement; Equipments and installations; Exact sciences and technology; Information, signal and communications theory; Kalman filters; mobile localization; Mobile radiocommunication systems; mobile sensor networks; Optimization; Radiocommunication specific techniques; Radiocommunications; Robot sensing systems; semi-definite programming; semi-definite relaxation; Sensor localization; Services and terminals of telecommunications; Signal and communications theory; Signal, noise; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Telemetry. Remote supervision. Telewarning. Remote control; Vectors; Velocity measurement; Wireless sensor networks; Performance evaluation; Positioning; Wireless telecommunication; Speed measurement; Convex programming; Optimization; Remote sensing; Relaxation; Localization; Signal detection; Measurement error; Mobile radiocommunication; Range finding; Motion estimation; mobile localization; semi-definite relaxation; Measurement sensor; mobile sensor networks; semi-definite programming; Sensor localization; Arrival time; Simulation; Signal processing; Wireless network; Maximum likelihood; Sensor array; Approximate solution; Software radio
• ISSN: 1536-1276
• DOI: 10.1109/TWC.2013.110813.120379

In this paper, considering a mobile wireless sensor network, we study the problem of exploiting sensor mobility information in the process of sensor localization under two range measurement models, namely the time-of-arrival (TOA) model and the received signal strength (RSS) model. To do so, for each model, we first derive the maximum likelihood (ML) location estimator for the case of error-free velocity measurements. As the corresponding optimization problems are non-convex, we resort to semi-definite relaxation (SDR) techniques to find approximate solutions to each problem using semi-definite programming (SDP). We then extend our results to the cases where the velocity measurements are subject to measurement errors. Our simulation results show that exploiting the mobility information in the localization process can significantly improve the performance of the sensor localization. Moreover, mobility-aided localization has the potential to address some of typical positioning problems, such as sensitivity to the ranging measurement errors and the requirement on the number of the anchors needed to uniquely localize the sensor nodes.