Blind Identification of Fully Observed Linear Time-Varying Systems via Sparse Recovery
This work addresses the problem of identifying LTV systems without input knowledge, relevant for applications like inferring spatio-temporal dynamics in breast cancer research.
The paper proposes a method for blind identification of fully observed linear time-varying systems with sparse inputs, formulated as a compressive sensing problem. It derives sufficient conditions for unique recovery and demonstrates sensitivity to noise via synthetic experiments.
Discrete-time linear time-varying (LTV) systems form a powerful class of models to approximate complex dynamical systems with nonlinear dynamics for the purpose of analysis, design and control. Motivated by inference of spatio-temporal dynamics in breast cancer research, we propose a method to efficiently solve an identification problem for a specific class of discrete-time LTV systems, in which the states are fully observed and there is no access to system inputs. In addition, it is assumed that we do not know on which states the inputs act, which can change between time steps, and that the total number of inputs is sparse over all states and over time. The problem is formulated as a compressive sensing problem, which incorporates the effect of measurement noise and which has a solution with a partially sparse support. We derive sufficient conditions for the unique recovery of the system model and input values, which lead to practical conditions on the number of experiments and rank conditions on system outputs. Synthetic experiments analyze the method's sensitivity to noise for randomly generated models.