Target Localization and Function Estimation in Sparse Sensor Networks

摘要

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 field 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 field that approximates the distribution of pollution or fumes produced by an object and (2) a radiation field 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 field, the performance of the developed estimator is compared to unbiased Cramer-Rao Lower Bound.