Identification of bilinear systems using bandlimited regression

This paper considers the identification of time-invariant bilinear models using observed input-output data. Bilinear models represent a parsimonious class of nonlinear parameterisations and have been used in a variety of applications. However the performance of the bilinear model can be limited in practice when standard least-squares techniques are used, as this leads to biased parameter estimates. Most existing solutions for this problem are restrictive, suboptimal, or computationally intensive. We propose an alternative approach to this identification task by utilising a robust regression technique, known as bandlimited regression, to obtain bilinear parameter estimates with reduced bias. The approach is numerically stable and computationally inexpensive. Simulations are given to demonstrate the usefulness of the technique for bilinear system identification