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Synthesizing Realistic, High-Resolution Anti-Submarine Sonar Data

Operations involving active sonars in littoral environments are generally associated with reverberation limited sonar performance and large amounts of false alarms. The increase in false alarm rates is both due to the signal processing induced phenomena false alarm rate inflation and to non-Rayleigh reverberation envelopes. High false alarm rates may be handled either simply by increasing the threshold applied in the detection algorithm, or through more refined methods such as automatic classification schemes. However, classification methods derived through supervised learning require large data sets for training and validation. Data sets containing actual targets are expensive to collect and often highly classified. Realistic synthetization of sonar data is a possible means for providing the required data. Here we analyze the statistics of reverberation envelopes from a data set recorded using an active low frequency towed source and receiver array close to the coast in the Norwegian Trench. An exhaustive search using maximum likelihood is applied for optimizing the parameters of K-distributions in order to fit the distribution of the measured reverberation envelopes. These envelopes are then combined with modelled reverberation in order to estimate realistic reverberation envelopes.