Underwater target classification using the wing BOSS and multi-channel decision fusion

Wachowski, Neil; Azimi-Sadjadi, M.R.; Cartmill, J.

doi:10.1117/12.720931

Cited by 2 publications

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“…When used to perform simultaneous detection and classification on data from an entire run, it was shown that the CMAC system was able to correctly detect and classify all of the mine-like objects and substantially reduce the number of false alarms not removed by the nonlinear DLF system. The adaptability and ease of implementation of this system coupled with its superior performance on all the BOSS tested data sets [3], [22], [23] makes it a valuable tool for underwater buried object classification.…”

Section: Discussionmentioning

confidence: 99%

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Buried Underwater Object Classification Using a Collaborative Multiaspect Classifier

Cartmill

Wachowski

Azimi-Sadjadi

2009

IEEE J. Oceanic Eng.

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Abstract-In this paper, a new collaborative multiaspect classification system (CMAC) is introduced, which utilizes a group of collaborative decision-making agents capable of producing a highconfidence final decision based on features obtained over multiple aspects. It is also shown how CMAC can be modified to perform multiaspect classification using a decision feedback (DF) strategy. The system is then applied to a buried underwater target classification problem. The results show that CMAC provides excellent multiple-ping classification of mine-like objects while reducing the number of false alarms compared to other multiple-ping classification fusion systems such as nonlinear decision-level fusion (DLF).

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Section: Discussionmentioning

confidence: 99%

“…Making the assumption that does not depend on , , , and by summing over values of , we get (21) Since the final decision is made based on the decision rule and is not dependent on , we can consolidate like parts in (21) and ignore the constant term (22) as it does not depend on . This simplifies (21) …”

Section: Appendixmentioning

confidence: 99%

Buried Underwater Object Classification Using a Collaborative Multiaspect Classifier

Cartmill

Wachowski

Azimi-Sadjadi

2009

IEEE J. Oceanic Eng.

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Buried Underwater Object Classification Using a Collaborative Multi-Aspect Classifier

Cartmill

Azimi-Sadjadi

Wachowski

2007

2007 International Joint Conference on Neural Networks

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In this paper, a new collaborative multi-aspect classification system (CMAC) is introduced. CMAC utilizes a group of collaborative decision-making agents capable of producing a high-confidence final decision based on features obtained over multiple aspects. This system is then applied to a buried underwater target classification problem. The results show that CMAC provides excellent multi-ping classification of mine-like objects while simultaneously reducing the number of false alarms compared to a multi-ping decision-level fusion classifier.

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Underwater target classification using the wing BOSS and multi-channel decision fusion

Cited by 2 publications

References 0 publications

Buried Underwater Object Classification Using a Collaborative Multiaspect Classifier

Buried Underwater Object Classification Using a Collaborative Multiaspect Classifier

Buried Underwater Object Classification Using a Collaborative Multi-Aspect Classifier

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