Target localization and function estimation in sparse sensor networks

Natalia A. Schmid

doi:10.1117/12.2016656

Abstract

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.

Citation Download Citation

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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