Feature Modelling in Passive Sonar Data Association

Abstract

Data association is currently a major problem in passive sonar; it can be described as the problem of associating signals relating to a single source. In this thesis three novel algorithms are presented that address the data association problem for a single omni-directional passive sonar. Using this type of sensor means that it is not possible to obtain any information about the position of the noise source, consequently previously developed association algorithms, which rely on this information, cannot be implemented. As such the algorithms presented in this thesis associate information based on the shape, features and characteristics of the different signals. This is a new approach to solving the data association problem. The first of the algorithms presented uses a combination of template matching, with b-splines being used to form the template, and an exploitation of the known physical properties of signals emanating from a single noise source. The second algorithm identifies three key features (each of which is characterised by three parameters). Each of the signals are analysed to identify the presence of any of these features, which in turn are localised, identified and parametrised using a combination of wavelets, b-splines and a line-searching algorithm. It is then possible to form associations based of the presence of similar features occurring at the same time. The final approach uses cluster analysis. Several key ideas and developments in the field are identified and discussed before a number of the more suitable methods are tested on simulated data, which has been designed to mimic the characteristics expected in real data