We propose a maximum likelihood estimation approach for the recovery of continuously-defined sparse signals from noisy measurements, in particular periodic sequences of Diracs, derivatives of Diracs and piecewise polynomials. The conventional approach for this problem is based on least-squares (a.k.a. annihilating filter method) and Cadzow denoising. It requires more measurements than the number of unknown parameters and mistakenly splits the derivatives of Diracs into several Diracs at different positions. Moreover, Cadzow denoising does not guarantee any optimality. The proposed approach based on maximum likelihood estimation solves all of these problems. Since the corresponding log-likelihood function is non-convex, we exploit the stochastic method called particle swarm optimization (PSO) to find the global solution. Simulation results confirm the effectiveness of the proposed approach, for a reasonable computational cost.

We address a problem of sampling and reconstructing periodic piecewise polynomials based on the theory for signals with a finite rate of innovation (FRI signals) from samples acquired by a sinc kernel. This problem was discussed in a previous paper. There was, however, an error in a condition about the sinc kernel. Further, even though the signal is represented by parameters, these explicit values are not obtained. Hence, in this paper, we provide a correct condition for the sinc kernel and show the procedure. The point is that, though a periodic piecewise polynomial of degree R is defined as a signal mapped to a periodic stream of differentiated Diracs by R + 1 time differentiation, the mapping is not one-to-one. Therefore, to recover the stream is not sufficient to reconstruct the original signal. To solve this problem, we use the average of the target signal, which is available because of the sinc sampling. Simulation results show the correctness of our reconstruction procedure. We also show a sampling theorem for FRI signals with derivatives of a generic known function.

In order to realize a continuous-time system model in digital computers, we must construct a discrete-time system model simulating the continuous-time processes in some characteristic aspect. Though many discretization methods have been proposed, they do not necessarily provide a discrete-time system in which input, state and output are identical with the sampled values of the original continuous-time system. The isomorphism discretization that all of the input, state and output of a continuous-time system can be recovered from the corresponding discrete-time system is crucial for our analysis. This paper aims at guaranteeing the isomorphism between a continuous- and a discrete-time system models (fluency system model) which were proposed by the authors. The isomorphism of input space had been already shown in the previous works by one of the authors. In this paper, by showing the isomorphism of the state function and output spaces, the aim will be achieved.