… 14], while distributed estimation of stationary Markov random … An alternative for deterministic
decentralized estimation in … Here, we focus on decentralized estimation of deterministic …

… in our deterministic parameter estimation problem is motivated by maximum a posteriori (MAP)
estimation principles which apply to random parameters. Viewing our deterministic …

… distributed in … signals are modelled as unknown deterministic signals. In practice, this allows
to use the proposed approach regardless of the statistical properties of the source signals. In …

… paper [17], we introduced optimal dis- tributed estimators for deterministic signals based on
… In this section we consider distributed estimation and smoothing under the dynamical setup …

… the source signals are modelled as unknown deterministic signals. This, in practice, allows
to use the proposed approach whatever the statistical properties of the source signals are. …

… should be chosen such that the estimation performance of the estimator is … estimation error
covariance for reliable channels and to minimize the upper bound of the expected estimation …

… These motivate well distributed estimation scenarios where sensors … even in the simplest
distributed setup of estimating a scalar location … sensors deployed to estimate a deterministic …

… 1) X and the signals received from the environment-the ZFO-are all jointly Gaussian and 2)
the transmission and reception times as well as the sets A/" are all deterministic. Then c;Cm …

… The deterministic maximum likelihood estimate for S N is found as(7) S ^ N = arg S N { ∂
ln ω ( X N ) ∂ S N = 0 } = [ A H ( Θ m ) A ( Θ m ) ] − 1 A H ( Θ m ) X N and after substitution of (7) …

… that although linear precoding design for decentralized detection remains new, its counterpart
for distributed estimation has been extensively investigated, eg [21], [22]. In addition, the …