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Abstract:
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Sonar signal processing comprises of a large number of signal processing algorithms
for implementing functions such as Target Detection, Localisation, Classification, Tracking
and Parameter estimation. Current implementations of these functions rely on conventional
techniques largely based on Fourier Techniques, primarily meant for stationary signals.
Interestingly enough, the signals received by the sonar sensors are often non-stationary and
hence processing methods capable of handling the non-stationarity will definitely fare better
than Fourier transform based methods.Time-frequency methods(TFMs) are known as one of the best DSP tools for nonstationary
signal processing, with which one can analyze signals in time and frequency
domains simultaneously. But, other than STFT, TFMs have been largely limited to academic
research because of the complexity of the algorithms and the limitations of computing power.
With the availability of fast processors, many applications of TFMs have been reported in the
fields of speech and image processing and biomedical applications, but not many in sonar
processing. A structured effort, to fill these lacunae by exploring the potential of TFMs in
sonar applications, is the net outcome of this thesis. To this end, four TFMs have been
explored in detail viz. Wavelet Transform, Fractional Fourier Transfonn, Wigner Ville
Distribution and Ambiguity Function and their potential in implementing five major sonar
functions has been demonstrated with very promising results. What has been conclusively
brought out in this thesis, is that there is no "one best TFM" for all applications, but there is
"one best TFM" for each application. Accordingly, the TFM has to be adapted and tailored
in many ways in order to develop specific algorithms for each of the applications. |