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Experimentalists are familiar with the aliasing that happens in data acquisition when the sampling rate is less than twice the highest frequency of energy in the signal to be sampled. Much effort has been made over the years using a combination of analog and digital filters to make sure that the higher frequencies are filtered out to avoid or minimize the effect of this aliasing. Much less talked about is the aliasing that occurs in modal parameter estimation, or curvefitting, when the residual effects of out of band modes violate the assumptions of the finite dimensional parametric model that the experimentalist uses to curvefit the acquired digitized data. While the out of band energy has been filtered out of the now band limited data, the tails, sometimes called residual flexibility and inertial restraint of the out of band modes are still present in the data.

Kalman filters have been employed very successfully in control and guidance systems since the sixties, with particular application to avionics and navigation. These filters can track accurately signals of known structure among noise and other signal components with different structure. This paper investigates the application of nonstationary Kalman filters to track harmonic components. This approach enables the analysis of harmonic components in signals even when the rate of change of frequency, or slew rate, is high. A Kalman filter formulation to track harmonic components in data records sampled with constant frequency is first reviewed. Particular attention is paid to the characteristics of the filters in terms of filter resolution and harmonic separation, as well as ability to handle higher slew rates. The applicability of the method is demonstrated for analyzing driveline harshness problem.