Derivation of a bilinear Kalman filter with autocorrelated inputs

In this paper we derive a set of approximate but general bilinear Kalman filter equations for a multi- input multi-output bilinear stochastic system driven by general autocorrelated inputs. The derivation is based on a convergent Picard sequence of linear stochastic state-space subsystems. We also derive necessary and sufficient conditions for a steady-state solution to exist. Provided all the eigenvalues of a chain of structured matrices are inside the unit circle, the approximate bilinear Kalman filter equations converge to a stationary value. When the input is a zero-mean white noise process, the approximate bilinear Kalman filter equations coincide with those of the well known bilinear Kalman filter model operating under white noise inputs.