Improvement of Filter Estimates Based on Data from Unmanned Underwater Vehicle with Machine Learning

Erol, Berna; Cantekin, Recep Fatih; Kartal, Seda Karadeniz; Hacıoğlu, Rıfat; Gormus, K. S.; Kutoğlu, Şenol Hakan; Leblebi̇ci̇oğlu, Kemal

doi:10.1109/asyu50717.2020.9259853

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…All these results indicate that the underwater map created with bathymetric data, obtained by integrating the echosounder into the underwater vehicle rather than a sea-surface vehicle, is more accurate. In future studies, we will compare the results of an experiment performed in the same test area with the bathymetric data obtained using an unmanned underwater vehicle [41].…”

Section: Discussionmentioning

confidence: 99%

Modeling and Analysis of Sea-Surface Vehicle System for Underwater Mapping Using Single-Beam Echosounder

Kartal

Hacıoğlu

Gormus

et al. 2022

JMSE

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Detailed knowledge on the shape of the seafloor is crucial for many researchers. Bathymetric data are critical for navigational safety and are used for underwater mapping. This study develops a sea-surface vehicle (SSV) system for underwater mapping by using both bathymetric data from a low-cost single-beam echosounder located on the SSV, and the navigation data of the SSV. The navigation of the SSV was obtained using a global positioning system (GPS). The effect of changing bathymetric and navigation data due to external disturbances such as wind and waves on the map was analyzed. The sea-bottom slope angles, which are effective in changing bathymetric data, were estimated and corrected in relation to the estimated angles in a particular mapped area for more accurate underwater mapping. Additionally, the effects of the grid range of the mapped area, beam angle of the echosounder, and position of the echosounder on the underwater mapping were analyzed. These analyses were based on simulation data, and were performed in a MATLAB, HYPACK, and Global Mapper environment. An underwater map was also obtained in the Kozlu/Zonguldak area, Black Sea by using a single-beam echosounder located on the SSV. This map was improved by estimating sea-bottom slope angles and the corrected bathymetric data to obtain a more accurate underwater map of the area. The experimental and simulation results were compared, focusing on the sea-bottom slope changes, sea-surface disturbances, bathymetry grid range changes, and draft effects.

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Section: Discussionmentioning

confidence: 99%

Modeling and Analysis of Sea-Surface Vehicle System for Underwater Mapping Using Single-Beam Echosounder

Kartal

Hacıoğlu

Gormus

et al. 2022

JMSE

View full text Add to dashboard Cite

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İnsansız Sualtı Aracı Hareketinin Kalman Filtre İle Kestirimi ve Makine Öğrenmesi ile İyileştirilmesi

Erol

Cantekin

Kartal

et al. 2021

International Journal of Advances in Engineering and Pure Sciences

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Bu çalışmada bir insansız sualtı aracının altı serbestlik dereceli doğrusal olmayan matematiksel modeli elde edilmiştir. Aracın matematiksel model cevabından aracın konum ve yönelim bilgileri elde edilmiştir. Elde edilen konum ve yönelim bilgilerine gürültü eklenerek navigasyon sensör verileri üretilmiştir. Üretilen gürültülü sensör verilerinin kestirimi için kokusuz ve genişletilmiş Kalman filtre algoritmaları kullanılmıştır. Kokusuz Kalman filtresinde, sistem modeli için insansız sualtı aracının doğrusal olmayan modeli kullanılmıştır. Genişletilmiş Kalman filtresinde ise sualtı aracının doğrusal olmayan modeli belirli denge noktalarında doğrusallaştırılmıştır. Kokusuz ve genişletilmiş Kalman filtresi kestirim sonuçları karşılaştırılmıştır. Kokusuz Kalman filtre ve genişletilmiş Kalman filtre kestirimlerine makine öğrenmesi olan Destek Vektör Makinesi algoritması uygulanarak, gürültünün fazla olduğu durumlar için, kestirimler iyileştirilmiştir. Buna ek olarak, aracın verilen bir kare yolu takip ettiği hareketi için kokusuz Kalman filtre ve genişletilmiş Kalman filtre kestirimleri iyileştirilmiştir. Tüm çalışma MATLAB/Simulink ortamında yapılmıştır.

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Improvement of Filter Estimates Based on Data from Unmanned Underwater Vehicle with Machine Learning

Cited by 2 publications

References 12 publications

Modeling and Analysis of Sea-Surface Vehicle System for Underwater Mapping Using Single-Beam Echosounder

Modeling and Analysis of Sea-Surface Vehicle System for Underwater Mapping Using Single-Beam Echosounder

İnsansız Sualtı Aracı Hareketinin Kalman Filtre İle Kestirimi ve Makine Öğrenmesi ile İyileştirilmesi

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