10 June 2013 Target localization and function estimation in sparse sensor networks
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The problem of distributed estimation of a parametric function in space is stated as a maximum likelihood estimation problem. The function can represent a parametric physical ¯eld generated by an object or be a deterministic function that parameterizes an inhomogeneous spatial random process. In our formulation, a sparse network of homogeneous sensors takes noisy measurements of the function. Prior to data transmission, each sensor quantizes its observation to L levels. The quantized data are then communicated over parallel noisy channels to a fusion center for a joint estimation. The numerical examples are provided for the cases of (1) a Gaussian-shaped ¯eld that approximates the distribution of pollution or fumes produced by an object and (2) a radiation ¯eld due to a spatial counting process with the intensity function decaying according to the inverse square law. The dependence of the mean- square error on the number of sensors in the network, the number of quantization levels, and the SNR in observation and transmission channels is analyzed. In the case of Gaussian-shaped ¯eld, the performance of the developed estimator is compared to unbiased Cramer-Rao Lower Bound.
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Natalia A. Schmid, Natalia A. Schmid, "Target localization and function estimation in sparse sensor networks", Proc. SPIE 8744, Automatic Target Recognition XXIII, 87440V (10 June 2013); doi: 10.1117/12.2016656; https://doi.org/10.1117/12.2016656

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