Next Article in Journal
A Method for Measuring the Height of Hand Movements Based on a Planar Array of Electrostatic Induction Electrodes
Next Article in Special Issue
MorphoCluster: Efficient Annotation of Plankton Images by Clustering
Previous Article in Journal
BioMove: Biometric User Identification from Human Kinesiological Movements for Virtual Reality Systems
Previous Article in Special Issue
Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images
Open AccessArticle

Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets

1
Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35141 Padova, Italy
2
Department of General Psychology, University of Padova, Via Venezia 8, 35141 Padova, Italy
3
Hatter Department of Marine Technologies, University of Haifa, Haifa 3498838, Israel
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder. In Proceedings of the IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Le gosier, Guadeloupe, 15–18 December 2019.
Sensors 2020, 20(10), 2945; https://doi.org/10.3390/s20102945
Received: 23 April 2020 / Revised: 15 May 2020 / Accepted: 19 May 2020 / Published: 22 May 2020
(This article belongs to the Special Issue Sensor Applications on Marine Recognition)
Accurate detection and tracking of moving targets in underwater environments pose significant challenges, because noise in acoustic measurements (e.g., SONAR) makes the signal highly stochastic. In continuous marine monitoring a further challenge is related to the computational complexity of the signal processing pipeline—due to energy constraints, in off-shore monitoring platforms algorithms should operate in real time with limited power consumption. In this paper, we present an innovative method that allows to accurately detect and track underwater moving targets from the reflections of an active acoustic emitter. Our system is based on a computationally- and energy-efficient pre-processing stage carried out using a deep convolutional denoising autoencoder (CDA), whose output is then fed to a probabilistic tracking method based on the Viterbi algorithm. The CDA is trained on a large database of more than 20,000 reflection patterns collected during 50 designated sea experiments. System performance is then evaluated on a controlled dataset, for which ground truth information is known, as well as on recordings collected during different sea experiments. Results show that, compared to the benchmark, our method achieves a favorable trade-off between detection and false alarm rate, as well as improved tracking accuracy. View Full-Text
Keywords: underwater signal detection; deep learning; Viterbi algorithm; marine monitoring; acoustic detection; SONAR; track before detect underwater signal detection; deep learning; Viterbi algorithm; marine monitoring; acoustic detection; SONAR; track before detect
Show Figures

Figure 1

MDPI and ACS Style

Testolin, A.; Diamant, R. Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets. Sensors 2020, 20, 2945.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop