Autonomous interpretation of side scan sonar returns
- 4 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 248-253
- https://doi.org/10.1109/auv.1990.110464
Abstract
The automatic interpretation of side scan sonar (SSS) returns is addressed. The proposed system combines signal detection techniques, feature extraction using SSS geometry, and a neural network classification algorithm for real-time detection and classification. Preliminary results were obtained by experimenting with actual SSS data. Applying the automated system to the data, the detection subsystem was able to detect properly the targets of interest as well as other bottom objects. The feature extraction subsystem helped the neural classifier separate targets from extraneous objects. However, in many instances, more resolution was needed to describe small targets accurately so that the neural classifier could classify correctly.Keywords
This publication has 6 references indexed in Scilit:
- Automatic Target Detection and Cuing System for an Autonomous Underwater Vehicle (auv)Published by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Side Scan Sonar Object Classification AlgorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Learned classification of sonar targets using a massively parallel networkIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
- Probabilistic neural networks for classification, mapping, or associative memoryPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1988
- Parallel Distributed ProcessingPublished by MIT Press ,1986
- Acoustic Detection, Communication, and Signal Processing Requirements for the Optional Deployment of SSBNs in the Poseidon-X Mode in Shallow Ocean WatersPublished by Springer Nature ,1981