Abstract—The recent boost in undersea operations has led
to the development of high-resolution sonar systems mounted
on autonomous vehicles, and aimed to scan the sea floor and
detect objects. An important part of sonar detection is the
image denoising, where the background is smoothed and noise
components are removed while preserving the object’s borders.
Sonar image denoising is a challenging task, mostly due to
the heavy intensity inhomogeneity of the background and the
heavy spatial varying background. In this paper, we propose
an algorithm for sonar image denoising that is based on the
adaptation of the nonlocal means-based filter. The noise in the
highlight and background regions is modeled by the exponential
distribution, while the noise in the shadow region is modeled by
the Gaussian distribution. We estimate the label of each pixel
through image segmentation to estimate the parameters of each
distribution. Then, the minimum entropy criteria is used to decide
which statistics model in the denoising filter gives the best results.
Results for synthetic sonar images and over real sonar images
demonstrate that the proposed method successfully removes the
noise components while preserving the object’s edges.
We study sharks biomass in open sea using non-invasive active acoustics. The importance of continuous long-term monitoring of top predator biomass is vital in understanding the healthiness and balance of the ecosystem. Instead of the traditional catch-and-release methods and visual inspections, which are too sporadic to supply reliable statistics, we rely on acoustic tools for the quantitative estimation of the number of sharks in a given area, their size, and the evaluation of their motion patterns.
We take a blind classification approach and identify sharks' related reflections from sea boundary reflections based on a track-before-detect approach. Specifically, by emitting a series of wideband acoustic signals, we create a time- delay image whose rows correspond to the received reflection response. We rely on the observation that sea boundary reflections are characterized by a random clutter-like pattern, while shark's related reflections are continuous and steady. Thus, we detect a shark in a clutter by identifying in the time-delay image continuous but curved lines whose structure meet certain limitations, namely, the shark's maximal speed and its expected carangiform motion pattern.
In the full paper, we will describe our method and show simulation results.
We address the challenge of detecting an arbitrary- shaped underwater acoustic signal. Instead of setting a detection threshold, which due to noise transients may result in a high false alarm rate, our method classifies each measured sample as either ’noise’ or ’signal’. Utilizing a-priori knowledge of only the minimal duration of the signal, the decision is made using loopy belief propagation over a factor graph. Numerical simulations and sea experimental results show that our scheme achieves a favorable trade-off between the recall and false alarm rates, and noise robustness which far exceeds that of benchmark schemes.
We introduce the communication operations of THEMO: the new Texas A&M - University of Haifa - Eastern Mediterranean Marine Observatory. THEMO includes moored sensors, surface sensors, and secondary moorings. In all cases, the data from the sensors is transferred in near real-time to a shore station where it is freely shared with the community. Variety of communication aids are used. These include underwater acoustic communications to connect the sensors onboard the secondary moorings with THEMO’s surface platform, underwater inductive communication to connect the moored submerged sensors to the buoy’s controller, and radio communication connecting the mooring to the shore station. After deployment of almost a year, our results show that the communication performance are reliable, and the data flows in all weather conditions. In this paper, we describe the mooring’s communication applications, and share the details of the different communication components.
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Monitoring of the marine environment requires large amounts of data, simply due to its vast size. Therefore, underwater autonomous vehicles and drones are increas- ingly deployed to acquire numerous photographs. How- ever, ecological conclusions from them are lagging as the data requires expert annotation and thus realistically can- not be manually processed. This calls for developing au- tomatic classification algorithms dedicated for this type of data. Current out-of-the-box solutions struggle to provide optimal results in these scenarios as the marine data is very different from everyday data. Images taken under water dis- play low contrast levels and reduced visibility range thus making objects harder to localize and classify. Scale varies dramatically because of the complex 3 dimensionality of the scenes. In addition, the scarcity of labeled marine data pre- vents training these dedicated networks from scratch. In this work, we demonstrate how transfer learning can be uti- lized to achieve high quality results for both detection and classification in the marine environment. We also demon- strate tracking in videos that enables counting and measur- ing the organisms. We demonstrate the suggested method on two very different marine datasets, an aerial dataset and an underwater one.
We focus on mitigating strong interferences that can jam underwater acoustics emissions aimed for detection or communications. We consider two types of interferences: narrowband like continuous waveform (CW) and wideband like chirp. Both types are assumed to be strong, such that, without interference cancellation, performance is poor. We offer two interference canceling algorithms, each corresponding to a different interference type. The two algorithms are designed to mitigate strong interference, while maintaining the desired signal intact. These algorithms can be executed sequentially to manage both types of interference simultaneously. Our solution takes advantage of the sparsity of the underwater acoustic channel, as well as the assumed correlation of the interference signals. Numerical simulations, as well as results from a sea experiment, show that our algorithms significantly reduce the effect of strong interferences for the fast time-varying and long-delay spread underwater acoustic channel.