  The problem of distributed estimation of a parametric physical field is
stated as a maximum likelihood estimation problem. Sensor observations are
distorted by additive white Gaussian noise. Prior to data transmission, each
sensor quantizes its observation to $M$ levels. The quantized data are then
communicated over parallel additive white Gaussian channels to a fusion center
for a joint estimation. An iterative expectation-maximization (EM) algorithm to
estimate the unknown parameter is formulated, and its linearized version is
adopted for numerical analysis. The numerical examples are provided for the
case of the field modeled as a Gaussian bell. The dependence of the integrated
mean-square error on the number of quantization levels, the number of sensors
in the network and the SNR in observation and transmission channels is
analyzed.
