Abstract
Localization by a sensor network has been extensively studied. In this paper, we address the source localization problem by using time-difference-of- arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements. Owing to the nonconvex nature of the maximum-likelihood (ML) estimation problem, it is difficult to obtain its globally optimal solution without a good initial estimate. Thus, we reformulate the localization problem as a weighted least squares (WLS) problem and perform semidefinite relaxation (SDR) to obtain a convex semidefinite programming (SDP) problem. Although SDP is a relaxation of the original WLS problem, it facilitates accurate estimate without postprocessing. Moreover, this method is extended to solve the localization problem when there are errors in sensor positions and velocities. Simulation results show that the proposed method achieves a significant performance improvement over existing methods.
Original language | English (US) |
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Article number | 6331564 |
Pages (from-to) | 853-862 |
Number of pages | 10 |
Journal | IEEE Transactions on Vehicular Technology |
Volume | 62 |
Issue number | 2 |
DOIs | |
State | Published - 2013 |
All Science Journal Classification (ASJC) codes
- Aerospace Engineering
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Automotive Engineering
Keywords
- Frequency difference of arrival (FDOA)
- localization
- semidefinite programming (SDP)
- sensor network
- time difference of arrival (TDOA)