A current key problem in the development of passive sonar is the classification of data into its different noise sources. This paper focuses on solving the problem using feature extraction and clustering techniques. The methods described in this paper have been developed for data collected from a single sensor omni-directional passive sonar, with the input data being the extracted frequency tracks from a time-frequency lofagram. A single noise source will exhibit a number of frequency tracks within a lofagram, collectively these tracks form a harmonic set. The problem of harmonic detection is to associate the different components of each of the harmonic sets, or noise sources. Each of the frequency tracks within a harmonic set have a strong physical relationship defining their behaviour and properties. In solving the harmonic detection problem this physical relationship is exploited to associate frequency tracks with similar characteristics. In the solution to this problem each of the frequency tracks present have several key features measured. This allows the charactersitics of each of the frequency tracks to be described in a small number of key, directly comparable, parameters. In this paper a small selection of features that may be used in the analysis of the frequency tracks are described. These features are then measured for a number of different data sets. With these sets of parameterised features it is possible to associate harmonically related frequency tracks by implementing clustering analysis, since the strong physical relationship of the features mean that related tracks will be clustered. In applying clustering, decisions need to be made into how many clusters there are in the data. The approach used in this paper uses hierarchical clustering. Hierarchical clustering begins by placing each data point into its own cluster. Two clusters are then merged based on a clustering criterion. At each step two clusters are joined until all the data is held within a single cluster. The progression of the cluster can be shown in a tree diagram, or dendrogram, which is then used to find the optimal level based on the ratio of the distribution of data within a cluster and the seperation between different clusters.
|Title of host publication||Proceedings of MTS/IEEE OCEANS, 2005|
|Publication status||Published - 2005|
|Event||Marine Technology Society and Oceanic Engineering Society of the IEEE "One Ocean" Conference - Washington DC, United States|
Duration: 18 Sep 2005 → 23 Sep 2005
http://washington05.oceansconference.org (Link to Conference Website)
|Conference||Marine Technology Society and Oceanic Engineering Society of the IEEE "One Ocean" Conference|
|Period||18/09/05 → 23/09/05|
|Other||Over 2,000 attendees from nearly 40 nations participated in over 250 events during a six-day program that included about 500 oral technical paper presentations, nearly 200 exhibit booths, 25 plenary presentations from senior leaders in Washington and throughout the community, 9 focus sessions that brought together those both familiar and unfamiliar with specific topics to engage dialogue, a wide range of tutorial sessions, special presentations from the Smithsonian Institution and National Geographic Society, a student poster competition with incredible entries, a broad spectrum of social events to encourage networking, and a first-ever Town Hall meeting on Friday morning that was the capstone event for the conference.|
The conference also dedicated a special focus to education and outreach with a dynamic program that included a MATE/ROV demonstration, a full day of tutorial sessions for local educators, a plenary session on education, a presentation of the NOSB competition, a luncheon dedicated to education, a live career panel webcast for students, and the largest ever group of education-focused technical paper presentations.