|Title||Smoothed estimation of unknown inputs and states in dynamic systems with application to oceanic flow field reconstruction|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Fang H.Z, de Callafon R.A, Franks PJS|
|Journal||International Journal of Adaptive Control and Signal Processing|
|Type of Article||Article|
|Keywords||discrete-time-systems; filters; forward-backward smoothing; input estimation; minimum-variance estimation; nonlinear systems; nonlinear-systems; ocean observing; state estimation|
Forward-backward smoothing based unknown input and state estimation for dynamic systems is studied in this paper, motivated by reconstruction of an oceanographic flow field using a swarm of buoyancy-controlled drifters. The development is conducted in a Bayesian framework. A Bayesian paradigm is constructed first to offer a probabilistic view of the unknown quantities given the measurements. Then a maximum a posteriori is established to build a means for simultaneous input and state smoothing, which can be solved by the classical Gauss-Newton method in the nonlinear case. Application to reconstruction of a complex three-dimensional flow field is presented and investigated via simulation studies. Copyright (c) 2014 John Wiley & Sons, Ltd.