Nonlinear sequential algorithms for estimation under uncertainty

Abstract

In many realistic estimation problems, the presence of the state vector to be estimated is uncertain. In this instance, use of the linear sequential conditional mean filter algorithms (the Kalman filter) results in suboptimum performance. This paper derives nonlinear sequential filter algorithms for conditional mean estimation of a Gauss Markov process when there is uncertainty as to the presence of the Gauss Markov process (signal process) in the observation. Associated estimation error variances are determined and a simple example is considered.