Abstract:
For a large and dense outdoor sensor network, the impact of sensor density and signal to noise ratio (SNR) are investigated on the performance of a maximum likelihood (ML...Show MoreMetadata
Abstract:
For a large and dense outdoor sensor network, the impact of sensor density and signal to noise ratio (SNR) are investigated on the performance of a maximum likelihood (ML) location estimation algorithm. The ML estimator fuses data, in the form of signal amplitudes, transmitted from local sensors to estimate the location of a source. A Gaussian-like isotropic signal decay model is adopted to make the problem tractable and meaningful. This model is suitable for situations such as passive sensors monitoring a target emitting acoustic signals. The exact Crameacuter-Rao lower bound (CRLB) on the estimation error has been derived. In addition, an approximate closed-form CRLB by using the Law of Large Numbers is obtained. The closed-form results indicate that the Fisher information is a linearly increasing function of the product of the sensor density and the SNR. Even though the results are derived assuming a large number of sensors, numerical results show that the closed-form CRLB is very close to the exact CRLB for both high and relatively low sensor densities.
Published in: 2009 12th International Conference on Information Fusion
Date of Conference: 06-09 July 2009
Date Added to IEEE Xplore: 18 August 2009
Print ISBN:978-0-9824-4380-4
Conference Location: Seattle, WA, USA