Nonlinear space-time varying parameter estimation using consensus-based in-network distributed strategy

In the present world, distributed signal processing plays a significant role in applications ranging from surveillance and tracking to exploration and monitoring. In this paper, an online distributed framework of spatio-temporal Wiener model is presented. A conventional Wiener model is extended to nonlinear distributed parameter systems (DPSs), which comprises of a linear time-invariant (LTI) system in series with a static nonlinear element. The standard Wiener model identification framework is reformulated as the minimization of multiple constrained optimization subtasks that get solved using alternating direction method of multipliers (ADMM) along with coordinate descent techniques. DPSs are significantly used in industrial processes e.g. thermal process, fluid process, etc. Almost all the real-time data contain non-linearity in them which is modeled using several methods: Wiener modeling is one of them. Adaptive as well as distributed implementation of such model is considered to take the advantages of both adaptive and distributed signal processing. The proposed method overcomes the limitations concerning fusion center (FC) and least-square (LS) based approaches. Unknown parameters of Wiener DPS are identified in an adaptive and distributed manner. To dignify the effectiveness of the proposed methodology, simulations on a catalytic rod (an example of a parabolic system) are illustrated.