parameter identification

estimation problem

subspace identification

nonlinear filtering

org math mathml

noise covariance

model reduction

Signal Processing

Parameter estimation is important in signal modeling and signal filtering. This paper uses the filtering technique to study the parameter estimation problems of a class of bilinear systems, for which the input-output representation is derived by eliminating the state variables in the systems. According to the obtained identification model, a two-stage least squares based iterative algorithm and a filtering based least squares iterative algorithm are proposed for estimating the parameters of bilinear systems with colored noises by using the hierarchical identification principle and the data filtering technique. The least squares based iterative algorithm is given for comparison. The simulation results show that the proposed algorithms have good performance in estimating the parameters of bilinear systems.

The least squares based iterative algorithms for parameter estimation of a bilinear system with autoregressive noise using the data filtering technique