Speaker
Description
Recently, frequency domain based pole finding methods were developed for LTI systems, which, unlike current popular identification schemes, do not require knowledge of the system order. These methods rely on rational orthogonal expansions of the transfer function and usually require an accurate estimate of the frequency response. This is usually only available in the form of an empirical transfer function, which has a major drawback of being sensitive to noise and prone to numerical errors. In this presentation we propose a novel, end-to-end identification pipeline for LTI systems which incorporates the above mentioned new pole finding schemes, but requires only time domain data to produce the required expansion coefficients. We propose novel ways to compute these coefficients, which allows for noise robust and numerically stable frequency response representations. In addition, the computed Malmquist-Takenaka-Fourier coefficients can be directly used to identify the examined system, resulting in a nonparametric (with respect to system order) and robust identification scheme. To demonstrate the effectiveness of the proposed approach, we present experiments based both on simulated examples and real-life system identification problems.
