Error bounds in parameter estimation under mismatch
TELE-INFO'07 Proceedings of the 6th WSEAS Int. Conference on Telecommunications and Informatics
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IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Error bounds in parameter estimation under mismatch
SIP'08 Proceedings of the 7th WSEAS International Conference on Signal Processing
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ACM Transactions on Sensor Networks (TOSN)
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IEEE Transactions on Signal Processing
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Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
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IEEE Transactions on Information Theory
Error bounds in parameter estimation under mismatch
ICOSSE'06 Proceedings of the 5th WSEAS international conference on System science and simulation in engineering
Error bounds in parameter estimation under mismatch
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
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The extended Ziv-Zakai bound for vector parameters is used to develop a lower bound on the mean square error in estimating the 2-D bearing of a narrowband planewave signal using planar arrays of arbitrary geometry. The bound has a simple closed-form expression that is a function of the signal wavelength, the signal-to-noise ratio (SNR), the number of data snapshots, the number of sensors in the array, and the array configuration. Analysis of the bound suggests that there are several regions of operation, and expressions for the thresholds separating the regions are provided. In the asymptotic region where the number of snapshots and/or SNR are large, estimation errors are small, and the bound approaches the inverse Fisher information. This is the same as the asymptotic performance predicted by the local Cramer-Rao bound for each value of bearing. In the a priori performance region where the number of snapshots or SNR is small, estimation errors are distributed throughout the a priori parameter space and the bound approaches the a priori covariance. In the transition region, both small and large errors occur, and the bound varies smoothly between the two extremes. Simulations of the maximum likelihood estimator (MLE) demonstrate that the bound closely predicts the performance of the MLE in all regions