SIMULTANEOUS ESTIMATION OF SPARSE SIGNALS AND SYSTEMS AT SUB-NYQUIST RATES

摘要

The novelty of this paper is divided into two technical sections; firstrnwe propose a novel algorithm for system identification with knownrninput sparse signal, based on the Finite Rate of Innovation samplingrntheory. Then we consider the problem of simultaneously estimatingrnthe input sparse signal and also the linear system and propose arnnovel iterative algorithm for that setup. We will show that, basedrnon our numerical simulations, the solution to the second problem isrnnormally convergent.