System Identification in Dynamical Sampling

We consider the problem of spatiotemporal sampling in a discrete infinite dimensional spatially invariant evolutionary process $x^{(n)}=A^nx$ to recover an unknown convolution operator $A$ given by a filter $a in ell^1(mathbb{Z})$ and an unknown initial state $x$ modeled as avector in $ell^2(mathbb{Z})$. Traditionally, under appropriate hypotheses, any $x$ can be recovered from its samples on $mathbb{Z}$ and $A$ can be recovered by the classical techniques of deconvolution. In this paper, we will exploit the spatiotemporal correlation and propose a new spatiotemporal sampling scheme to recover $A$ and $x$ that allows to sample the evolving states $x,Ax, cdots, A^{N-1}x$ on a sub-lattice of $mathbb{Z}$, and thus achieve the spatiotemporal trade off. The spatiotemporal trade off is motivated by several industrial applications cite{Lv09}. Specifically, we show that ${x(mmathbb{Z}), Ax(mmathbb{Z}), cdots, A^{N-1}x(mmathbb{Z}): N geq 2m}$ contains enough information to recover a typical low pass filter $a$ and $x$ almost surely, in which we generalize the idea of the finite dimensional case in cite{AK14}. In particular, we provide an algorithm based on a generalized Prony method for the case when both $a$ and $x$ are of finite impulse response and an upper bound of their support is known. We also perform the perturbation analysis based on the spectral properties of the operator $A$ and initial state $x$, and verify them by several numerical experiments. Finally, we provide several other numerical methods to stabilize the method and numerical example shows the improvement.