Robust classification of variable-length sonar sequences

Joydeep Ghosh; Narsimham V. Gangishetti; Srinivasa V. Chakravarthy

doi:10.1117/12.152618

19 August 1993 Robust classification of variable-length sonar sequences

Joydeep Ghosh, Narsimham V. Gangishetti, Srinivasa V. Chakravarthy

Proceedings Volume 1966, Science of Artificial Neural Networks II; (1993) https://doi.org/10.1117/12.152618
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

Two types of artificial neural networks are introduced for the robust classification of spatio- temporal sequences. The first network is the Adaptive Spatio-Temporal Recognizer (ASTER), which adaptively estimates the confidence that a (variable length) signal of a known class is present by continuously monitoring a sequence of feature vectors. If the confidence for any class exceeds a threshold value at some moment, the signal is considered to be detected and classified. The nonlinear behavior of ASTER provides more robust performance than the related dynamic time warping algorithm. ASTER is compared with a more common approach wherein a self-organizing feature map is first used to map a sequence of extracted feature vectors onto a lower dimensional trajectory, which is then identified using a variant of the feedforward time delay neural network. The performance of these two networks is compared using artificial sonograms as well as feature vectors strings obtained from short-duration oceanic signals.

Citation Download Citation

Joydeep Ghosh, Narsimham V. Gangishetti, and Srinivasa V. Chakravarthy "Robust classification of variable-length sonar sequences", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); https://doi.org/10.1117/12.152618

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