2286 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 55, NO. 6, DECEMBER 2006
Leakage Reduction in Frequency-Response
Function Measurements
Johan Schoukens, Yves Rolain, and Rik Pintelon
Abstract—This paper analyzes how to reduce leakage errors in
frequency-response function (FRF) measurements. First, the na-
ture of leakage errors is revealed; next, windowing methods are
analyzed, and a new default window is proposed. Finally, a supe-
rior Taylor-series-based method is proposed.
Index Terms—Frequency-response function (FRF) methods,
leakage, windows.
I. INTRODUCTION
F
REQUENCY-RESPONSE FUNCTION (FRF) measure-
ments of transfer functions are a basic tool in many
engineering fields. For random excitations, these measurements
are disturbed by leakage and disturbing noise errors. For these
reasons, we strongly advise applying periodic excitation signals
whenever it is possible [8]. However, in many applications,
for psychological or technological reasons, the users prefer
to apply random noise excitations. It is well known that for
random noise excitations, the FRF measurements are disturbed
by leakage (windowing) errors that are induced by the finite
length of the measurement window. This paper uses new
insights in the nature of these errors to propose improved
FRF-measurement techniques.
The classical approach to reduce the leakage errors is based
on the use of windows. In the literature, a large number of
windows is defined and their properties are intensively studied,
keeping essentially spectral analysis applications in mind [2],
[6]. In this paper, these properties are analyzed again keeping
FRF measurements in mind which leads to new insights, and
eventually to the definition of a new window. This allows a
reduction of the “leakage errors” on the FRF measurements,
while the noise sensitivity is not increased. In the next step,
an alternative Taylor-based method is proposed. In its simplest
version it reduces to the Hanning window, but with more
advanced settings a superior method is found.
II. HIDDEN NATURE OF LEAKAGE ERRORS
Let us consider a stable, causal, discrete- or continuous-time,
single-input–single-output linear, time-invariant system with im-
pulse response that is excited with a random input
(1)
Manuscript received June 15, 2005; revised May 9, 2006. This work was sup-
ported by theFund for Scientific Research (FWO-Vlaanderen), the Flemish gov-
ernment (GOA-IMMI), and the Belgian government (Interuniversity Poles of
Attraction, IUAP V/22).
The authors are with the Electrical Measurement Department (ELEC), Vrije
Universiteit Brussel, Brussels B1050, Belgium (e-mail: Johan.Schoukens@
vub.ac.be).
Digital Object Identifier 10.1109/TIM.2006.887034
with the convolution, the exact input and output
signal, and disturbing noise. samples of the input and
output are measured at . For notational sim-
plicity, we note these measurements as
with (2)
Those results of measurements are used for estimation of the
FRF at frequency . The dis-
crete Fourier transform (DFT) of the input–output
signal [3] is
(3)
The following remarkably simple relation holds between the
DFT spectra for [8], [7]
(4)
with and smooth rational functions of the frequency.
can be interpreted as a generalized “transient” term. Some of
these ideas were already reported in [5]. Due to the DFT defini-
tion (3), , and are of order , and the
transient is of order [8].
In the absence of disturbing noise , the FRF estimate
is given by
(5)
It is the last term in (5) that causes the leakage in the FRF mea-
surements. The ratio has a random behavior and
looks like noise in FRF measurements because is random.
However, this hides the highly structured nature that is described
by the smooth function . Windowing methods exploit this
smoothness to reduce the leakage errors.
It is common practice to average over multiple
measurements [1]
(6)
where is the spectrum of the signal in the th realiza-
tion of the experiment. This estimate converges for to
the solution corresponding to if the output noise
is not correlated with the input
(7)
Due to the leakage effects, this limit is still biased as it will be
shown later.
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