Estimation procedures for nonstationary Markov chains appear to be relatively sparse. This work introduces empirical  Bayes estimators  for the transition probability  matrix of a finite nonstationary  Markov chain. The data are assumed to be of  a panel study type in which each data set consists of a sequence of observations on N>=2 independent and identically dis...

For the first time, a distributed output feedback control scheme is presented which combines distributed model predictive control with distributed moving horizon estimation. More specifically, we combine the iterative methods of sensitivity-driven distributed model predictive control (S-DMPC) with sensitivity-driven partition-based moving horizon estimation (S-PMHE). To that end, S-PMHE is exte...

State estimation is essential to access observable network models for online monitoring and analyzing of power systems. Due to the integration of distributed energy resources and new technologies, state estimation in distribution systems would be necessary. However, accurate input data are essential for an accurate estimation along with knowledge on the possible correlation between the real and...

This paper presents several new distributed algorithms to solve consensus based estimation problems in a decentralized way by adopting the well-known relaxation methods Jacobi, Gauss-Seidel and successive over-relaxation within a sensor network. In distributed estimation, all nodes collaborate to estimate the signals emitted from some common sources, employing iterative processing with one-hop ...

In direction-of-arrival (DOA) estimation, the direction of a signal is usually assumed to be a point. If the direction of a signal is distributed due to some environmental phenomenon, however, DOA estimation methods based on the point source assumption may result in poor performance. In this paper, we consider DOA estimation when the signal sources are distributed. Parametric and nonparametric ...

The domain of system identification may be developed today using the powerful tool represented by the intelligent sensor networks, placed in real distributed parameter systems. The sensor networks, as a “distributed sensor”, allow the usage of multivariable estimation techniques, in different ways: classical linear methods of modelling or methods based on artificial intelligence for complex non...

Distributed estimation algorithms over Wireless Sensor Networks have been widely studied since the seminal work of Tsitsiklis. The goal of these algorithms is to make the network reach a consensus over the value of interest by means of local communications between the sensors. Currently there are no techniques for distributed estimation under dynamic communication constraints. Existing centrali...

This letter proposes a novel distributed compressed estimation scheme for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is also presented and an algorithm is developed to optimize measurement matrices, which ...