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.
Bibliographical noteFunding Information:
Funding: This work was funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 773753 (SYMBIOSIS), and by the NATO Science for Peace and Security Programme under grant G5293. A.T. gratefully acknowledges the support of the NVIDIA Corporation for the donation of a Titan Xp GPU used for this research.
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Acoustic detection
- Deep learning
- Marine monitoring
- Track before detect
- Underwater signal detection
- Viterbi algorithm
ASJC Scopus subject areas
- Analytical Chemistry
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering