State Estimation of Linear Systems With Sparse Inputs and Markov-Modulated Missing Outputs

In this paper, we consider the problem of estimating the states of a linear dynamical system whose inputs are jointly sparse and outputs at a few unknown time instants are missing. We model the missing data mechanism using a Markov chain with two states representing the missing and non-missing data. This mechanism with memory governed by the...

Compressive Sampling for Wireless Communications

Wireless communications is undergoing massive development in all forms of its manifestations. In the field of short-range communications, technologies like ultra- wideband (UWB) systems are promising very high data rates, fine timing resolution and coexistence with other physical layer standards. Along with these benefits, the promise of low...

Dantzig Selector: Het schatten van een sparse parametervector met weinig metingen - Dantzig Selector: Statistical estimation of a sparse vector with undersampled data

Bij lineaire regressie geldt in het algemeen, dat het aantal metingen (n) groter is dan het aantal parameters (p). In praktische situaties kan het echter voorkomen, dat het aantal metingen veel kleiner is dan het aantal parameters. De kleinste kwadratenschatter kan in dat geval niet gebruikt worden om de parametervector te schatten, er is geen...