Algorithms for extracting a burst gravitational wave signal embedded within
the noise of resonant-mass gravitational wave antenna have been well chara
cterized theoretically, bur their effects on experimental data, which can b
e contaminated by non-stationary, non-Gaussian noise, are still being studi
ed. In this paper, we study the effects of three such algorithms, the zero-
order prediction, adaptiveWiener-Kolmogorov and non-adaptive Wiener-Kolmogo
rov algorithms, on data from the resonant-mass gravitational wave antenna,
Niobe, at the University of Western Australia. By applying these filters to
computer-simulated GW signals, we show that the adaptive Wiener-Kolmogorov
filter gives the best noise performance and signal-to-noise ratio in the p
resence of non-Gaussian noise. By searching for coincidences between the si
mulated signals. we show that a window larger than the sampling time of the
data is necessary to observe a coincidence between ail events. A method of
applying pulse excitations to Niobe by amplitude modulating the pump oscil
lator driving the parametric transducer is also describe. This method has t
he potential to be a very accurate calibration technique but uncertainties
in the input and output gains reduce its accuracy. Finally, the adaptive an
d non-adaptive Wiener-Kolmogorov filters are applied to pulses generated by
the amplitude modulation method to determine the overall timing delays and
energy uncertainties of Niobe and its data acquisition system.