P. Lehmann et Eh. Schombacher, FEATURES OF A COMBINED FFT AND HILBERT TRANSFORM FOR PHASE DOPPLER SIGNAL-PROCESSING, Measurement science & technology, 8(4), 1997, pp. 409-421
Phase Doppler anemometry (PDA) is a laser optical method to determine
particle diameters and velocities within two-phase or multiphase flows
simultaneously and with high spatial and temporal resolution. The mea
sured phase difference between two Doppler bursts is related to the pa
rticle diameter. The proposed method for signal processing is based on
a combined Hilbert transform and Fast Fourier transform (FFT) phase D
oppler burst analysis. The numerical determination of the burst envelo
pes gives an estimation of the time delay between two related signals
or, more generally, of the burst maximum position within the record ti
me. This estimate is completed by the conventional FFT based signal an
alysis which is used to estimate frequency and phase difference. By th
is two-step estimation the restriction to the [0, 360 degrees] interva
l resulting from conventional signal processing can be avoided. The fe
asibility of the method in terms of an on-line determination of absolu
te phase differences is investigated by Monte Carlo simulations and de
monstrated by means of a standard PDA arrangement with ball lenses rot
ating as spherical particles through the measurement volume. Phase dif
ferences up to 2500 degrees were determined reliably. In addition, alg
orithms to calculate Doppler frequencies and Doppler intensities, burs
t lengths and further characteristic parameters taking the signal qual
ity into account are introduced and studied by Monte Carlo simulations
. Based on these results validation strategies to reduce the influence
of maltriggered bursts, trajectory or Gaussian beam effects on measur
ement results can be developed. Furthermore, by using the information
of burst length and maximum position, optimized signal processing algo
rithms can be realized in order to achieve maximum accuracy in frequen
cy, phase difference and signal-to-noise ratio (SNR) estimation.