This thesis investigates the detection and classification of small boats using a passive sonar system. Noise radiated from a small boats consists of broadband noise and harmonically related tones that correspond to parameters in the boats engine and propeller. A novel signal processing method for detection and discrimination of noise radiated from small boats has been developed. There are two main components to the algorithm. The first component detects the presence of small boats by the harmonic tonals radiated from the boat propeller and engine. The second component was designed to extract the a signature from passive sonar data.
The Harmonic Extraction and Analysis Tool (HEAT) was designed to estimate the fundamental frequency of the harmonic tones, track the fundamental frequency using a Kalman filter, and automatically extract the amplitudes of the harmonic tonals to generate a harmonic signature for the boat. The algorithm is shown to accurately extract theses signatures, and results show that the signatures are unique enough that the same boat passing by the hydrophone multiple times can be recognized.
|Advisor:||Zurk, Lisa M.|
|Commitee:||Campbell, Richard L., Sytsma, Mark D.|
|School:||Portland State University|
|Department:||Electrical and Computer Engineering|
|School Location:||United States -- Oregon|
|Source:||MAI 49/01M, Masters Abstracts International|
|Keywords:||Frequency estimation, Kalman filter, Small boat classification, Small boat detection|
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