Robust Transmit Beamforming for Parameter Estimation Using Distributed Sensors

In this letter, we study the problem of estimating a complex parameter in a wireless sensor network under individual power constraints and bounded channel uncertainty, where the channel state information is assumed to be imperfect at both the sensors and the fusion center. Transmit beamformers and a...

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Vydáno v:IEEE communications letters Ročník 20; číslo 7; s. 1329 - 1332
Hlavní autoři: Jiang Zhu, Blum, Rick S., Xiaokang Lin, Yuantao Gu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.07.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Beamforming
Channels
collaborative beamforming
Estimation
Estimators
Mathematical models
Mean square values
Optimization
Optimized production technology
Remote sensors
Robust estimation
Robustness
SDP
SDR
Sensor fusion
Sensors
Uncertainty
ISSN:1089-7798, 1558-2558
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Abstract In this letter, we study the problem of estimating a complex parameter in a wireless sensor network under individual power constraints and bounded channel uncertainty, where the channel state information is assumed to be imperfect at both the sensors and the fusion center. Transmit beamformers and a linear estimator that minimize the maximum mean square error are found. Although this problem is nonconvex, it can be relaxed to become a semidefinite programming problem by re-parameterization and semidefinite relaxation. Surprisingly, we find that relaxation is tight and a global optimal solution can be found. Finally, numerical simulations are performed to evaluate the performance of the robust estimator.
AbstractList In this letter, we study the problem of estimating a complex parameter in a wireless sensor network under individual power constraints and bounded channel uncertainty, where the channel state information is assumed to be imperfect at both the sensors and the fusion center. Transmit beamformers and a linear estimator that minimize the maximum mean square error are found. Although this problem is nonconvex, it can be relaxed to become a semidefinite programming problem by re-parameterization and semidefinite relaxation. Surprisingly, we find that relaxation is tight and a global optimal solution can be found. Finally, numerical simulations are performed to evaluate the performance of the robust estimator.
Author Jiang Zhu
Blum, Rick S.
Xiaokang Lin
Yuantao Gu
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10.1515/9781400831050
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10.1109/TSP.2010.2062178
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SubjectTerms Beamforming
Channels
collaborative beamforming
Estimation
Estimators
Mathematical models
Mean square values
Optimization
Optimized production technology
Remote sensors
Robust estimation
Robustness
SDP
SDR
Sensor fusion
Sensors
Uncertainty
Title Robust Transmit Beamforming for Parameter Estimation Using Distributed Sensors
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