A Semidefinite Relaxation Method for Source Localization Using TDOA and FDOA Measurements

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...

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Vydáno v:IEEE transactions on vehicular technology Ročník 62; číslo 2; s. 853 - 862
Hlavní autoři: Gang Wang, Youming Li, Ansari, N.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.02.2013
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Applied sciences
Approximation methods
Convergence
Detection, estimation, filtering, equalization, prediction
Estimates
Exact sciences and technology
Frequency difference of arrival (FDOA)
Frequency measurement
Information, signal and communications theory
Least squares method
Localization
Maximum likelihood estimation
Noise
Optimization
Position (location)
Programming
Radiocommunication specific techniques
Radiocommunications
semidefinite programming (SDP)
sensor network
Sensors
Signal and communications theory
Signal, noise
Simulation
Telecommunications
Telecommunications and information theory
time difference of arrival (TDOA)
Vectors
Performance evaluation
localization
Least squares problem
Measurement sensor
Convex programming
Frequency difference of arrival (FDOA)
Arrival time
semidefinite programming (SDP)
Relaxation
Weighting
sensor network
Source localization
Least squares method
time difference of arrival (TDOA)
Optimal solution
Relaxation method
System simulation
Maximum likelihood
Sensor array
Target detection
Signal detection
Software radio
ISSN:0018-9545, 1939-9359
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Shrnutí: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.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2012.2225074