  In this paper, we consider a general distributed estimation problem in
relay-assisted sensor networks by taking into account time-varying asymmetric
communications, fading channels and intermittent measurements. Motivated by
centralized filtering algorithms, we propose a distributed innovation-based
estimation algorithm by combining the measurement innovation (assimilation of
new measurement) and local data innovation (incorporation of neighboring data).
Our algorithm is fully distributed which does not need a fusion center. We
establish theoretical results regarding asymptotic unbiasedness and consistency
of the proposed algorithm. Specifically, in order to cope with time-varying
asymmetric communications, we utilize an ordering technique and the generalized
Perron complement to manipulate the first and second moment analyses in a
tractable framework. Furthermore, we present a performance-oriented design of
the proposed algorithm for energy-constrained networks based on the theoretical
results. Simulation results corroborate the theoretical findings, thus
demonstrating the effectiveness of the proposed algorithm.
