Gaussian process parameter estimation using zero crossing data from wireless sensors

The parameters in a general Gaussian process, including the parameters in an additive Gaussian noise process, are estimated based on zero crossing data for the total process and arbitrarily filtered versions thereof. A nonlinear weighted least squares estimate is considered and an analysis of the asymptotic covariance matrix of the estimated parameter vector is made. The proposed estimator and the use of zero crossing data are suitable when information of a process is sent from wireless sensors to a node center for further processing due to an efficient use of available bandwidth.