Discrete-time Linear Time-invariant Distributed Minimum Energy Estimator

Proper monitoring of large complex spatially critical infrastructures often requires a sensor network capable of inferring the state of the system. Such networks enable the design of distributed estimators considering only local (partial) measurements, local communication capabilities with nearby sensors, as well as the system model....

Distributed kalman filters for relative formation control of multi-agent systems

Formation control (FC) of multi-agent systems plays a critical role in a wide variety of fields. In the absence of absolute positioning, agents in FC systems rely on relative position measurements with respect to their neighbors. In distributed filter design literature, relative observation models are comparatively unexplored, and in FC...

Neuro-Adaptive Cooperative Tracking Rendezvous of Nonholonomic Mobile Robots

This brief proposes a neuro-adaptive method for the unsolved problem of cooperative tracking rendezvous of nonholonomic mobile robots (NMRs) subject to uncertain and unmodelled dynamics. A hierarchical cooperative control framework is proposed, which consists of a novel distributed estimator along with local neuro-adaptive tracking...

Distributed least-squares estimation applied to GNSS networks

In view of the recent proliferation of low-cost mass-market receivers, the number of network receivers and GNSS users will be growing rapidly, demanding an efficient way of data processing in terms of computational power and capacity. One way of improving the computational capacity is to decentralize the underlying data processing and distribute...

Distributed Estimation and Control for Robotic Networks

Mobile robots that communicate and cooperate to achieve a common task have been the subject of an increasing research interest in recent years. These possibly heterogeneous groups of robots communicate locally via a communication network and therefore are usually referred to as robotic networks. Their potential applications are diverse and...

Maximum smoothed likelihood estimation and smoothed maximum likelihood estimation in the current status model

We consider the problem of estimating the distribution function, the density and the hazard rate of the (unobservable) event time in the current status model. A well studied and natural nonparametric estimator for the distribution function in this model is the nonparametric maximum likelihood estimator (MLE). We study two alternative methods for...