Sea floor recognition with bio-inspired echolocation

Mariia Dmitrieva; Keith Brown; Gary Heald; David Lane

doi:10.1109/OCEANSE.2017.8084811

Sea floor recognition with bio-inspired echolocation

Mariia Dmitrieva, Keith Brown, Gary Heald, David Lane

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents a study of different feature extraction methods for sea floor recognition. This analysis is based on the frequency components of the return signals from a bio-inspired wideband sonar. Part of the sea floor of Tallinn Bay was surveyed using a wideband sonar with linear chirp pulses. The area contains citadel constructions and sediment regions. The recognition task is to distinguish between these two types of the sea floor. It was observed that the frequency spectrum of the signals returned from these two surfaces has different distributions. The work presents three frequency based approaches for the feature extraction process: Time-Frequency Moment Singular Value Decomposition (tFM-SVD), Energy Vector and Complete Frequency Spectrum. Comparison of the techniques is made in terms of the classification results.

Original language	English
Title of host publication	OCEANS 2017 - Aberdeen
Publisher	IEEE
ISBN (Electronic)	9781509052783
DOIs	https://doi.org/10.1109/OCEANSE.2017.8084811
Publication status	Published - 26 Oct 2017
Event	OCEANS 2017 - Aberdeen - Aberdeen, United Kingdom Duration: 19 Jun 2017 → 22 Jun 2017 http://www.oceans17mtsieeeaberdeen.org/

Conference

Conference	OCEANS 2017 - Aberdeen
Country/Territory	United Kingdom
City	Aberdeen
Period	19/06/17 → 22/06/17
Internet address	http://www.oceans17mtsieeeaberdeen.org/

ASJC Scopus subject areas

Instrumentation
Computer Networks and Communications
Oceanography
Acoustics and Ultrasonics
Automotive Engineering

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10.1109/OCEANSE.2017.8084811

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@inproceedings{887b1c9ae1c144c68cc2f2321a221896,

title = "Sea floor recognition with bio-inspired echolocation",

abstract = "This paper presents a study of different feature extraction methods for sea floor recognition. This analysis is based on the frequency components of the return signals from a bio-inspired wideband sonar. Part of the sea floor of Tallinn Bay was surveyed using a wideband sonar with linear chirp pulses. The area contains citadel constructions and sediment regions. The recognition task is to distinguish between these two types of the sea floor. It was observed that the frequency spectrum of the signals returned from these two surfaces has different distributions. The work presents three frequency based approaches for the feature extraction process: Time-Frequency Moment Singular Value Decomposition (tFM-SVD), Energy Vector and Complete Frequency Spectrum. Comparison of the techniques is made in terms of the classification results.",

author = "Mariia Dmitrieva and Keith Brown and Gary Heald and David Lane",

year = "2017",

month = oct,

day = "26",

doi = "10.1109/OCEANSE.2017.8084811",

language = "English",

booktitle = "OCEANS 2017 - Aberdeen",

publisher = "IEEE",

address = "United States",

note = "OCEANS 2017 - Aberdeen ; Conference date: 19-06-2017 Through 22-06-2017",

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T1 - Sea floor recognition with bio-inspired echolocation

AU - Dmitrieva, Mariia

AU - Brown, Keith

AU - Heald, Gary

AU - Lane, David

PY - 2017/10/26

Y1 - 2017/10/26

N2 - This paper presents a study of different feature extraction methods for sea floor recognition. This analysis is based on the frequency components of the return signals from a bio-inspired wideband sonar. Part of the sea floor of Tallinn Bay was surveyed using a wideband sonar with linear chirp pulses. The area contains citadel constructions and sediment regions. The recognition task is to distinguish between these two types of the sea floor. It was observed that the frequency spectrum of the signals returned from these two surfaces has different distributions. The work presents three frequency based approaches for the feature extraction process: Time-Frequency Moment Singular Value Decomposition (tFM-SVD), Energy Vector and Complete Frequency Spectrum. Comparison of the techniques is made in terms of the classification results.

AB - This paper presents a study of different feature extraction methods for sea floor recognition. This analysis is based on the frequency components of the return signals from a bio-inspired wideband sonar. Part of the sea floor of Tallinn Bay was surveyed using a wideband sonar with linear chirp pulses. The area contains citadel constructions and sediment regions. The recognition task is to distinguish between these two types of the sea floor. It was observed that the frequency spectrum of the signals returned from these two surfaces has different distributions. The work presents three frequency based approaches for the feature extraction process: Time-Frequency Moment Singular Value Decomposition (tFM-SVD), Energy Vector and Complete Frequency Spectrum. Comparison of the techniques is made in terms of the classification results.

UR - http://www.scopus.com/inward/record.url?scp=85044716406&partnerID=8YFLogxK

U2 - 10.1109/OCEANSE.2017.8084811

DO - 10.1109/OCEANSE.2017.8084811

M3 - Conference contribution

AN - SCOPUS:85044716406

BT - OCEANS 2017 - Aberdeen

PB - IEEE

T2 - OCEANS 2017 - Aberdeen

Y2 - 19 June 2017 through 22 June 2017

ER -

Sea floor recognition with bio-inspired echolocation

Abstract

Conference

ASJC Scopus subject areas

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