System Identification Based on Noise Elimination for Response Signals

Measured signals are inevitably contaminated with noise when a data acquisition system is used for an experimental measurement. This situation often leads to serious difficulties in system identification with proper accuracy. This paper presents a noise elimination method for measured response signals based on structured low rank approximation (SLRA) so as to improve the accuracy of the system identification. Numerical studies use a 4 degree-of-freedom mass-spring-dashpot system. While measured impulse response function (IRF) with noise is simulated, the modal parameter identification based on the filtered IRF is very good.