High-Frequency Volume and Boundary Acoustic Backscatter Fluctuations in Shallow Water

Volume and boundary acoustic backscatter envelope fluctuations are characterized from data collected by the Toroidal Volume Search Sonar (TVSS), a 68 kHz cylindrical array capable of 360° multibeam imaging in the vertical plane perpendicular to its axis. The data are processed to form acoustic backscatter images of the seafloor, sea surface, and horizontal and vertical planes in the volume, which are used to attribute nonhomogeneous spatial distributions of zooplankton, fish, bubbles and bubble clouds, and multiple boundary interactions to the observed backscatter amplitude statistics. Three component Rayleigh mixture probability distribution functions (PDFs) provided the best fit to the empirical distribution functions of seafloor acoustic backscatter. Sea surface and near-surface volume acoustic backscatter PDFsare better described by Rayleigh mixture or log-normal distributions, with the high density portion of the distributions arising from boundary reverberation, and the tails arising from nonhomogeneously distributed scatterers such as bubbles, fish, and zooplankton. PDF fits to the volume and near-surface acoustic backscatter data are poor compared to PDF fits to the boundary backscatter, suggesting that these data may be better described by mixture distributions with component densities from different parametric families. For active sonar target detection, the results demonstrate that threshold detectors which assume Rayleigh distributed envelope fluctuations will experience significantly higher false alarm rates in shallow water environments which are influenced by near-surface microbubbles, aggregations of zooplankton and fish, and boundary reverberation

Call combination patterns in Icelandic killer whales (Orcinus orca)

Funding: Funding for data collection was provided by the Fundação para a Ciência e a Tecnologia (grant number SFRH/BD/30303/2006), the Icelandic Research Fund (grant numbers 120248042 and 217519), the National Geographic Global Exploration Fund (grant number GEFNE65-12), and a Russell Trust Award from the University of St. Andrews to FIPS. This project was funded in part by the generous support of Earthwatch. Additionally, funding was provided by the US Office of Naval Research (grant number N00014-08-1-0984), US Living Marine Resources (project 57), UK Defence Science and Technology Laboratory, and French Direction Générale de l’Armement to PJOM. A RANNÍS Infrastructure Fund (grant number 200229) provided funding to JS and PJW for CATS tags and tracking equipment. AS was supported by Doctoral Student Grants (grant number 206808 and 239641) from the Icelandic Research Fund.Acoustic sequences have been described in a range of species and in varying complexity. Cetaceans are known to produce complex song displays but these are generally limited to mysticetes; little is known about call combinations in odontocetes. Here we investigate call combinations produced by killer whales (Orcinus orca), a highly social and vocal species. Using acoustic recordings from 22 multisensor tags, we use a first order Markov model to show that transitions between call types or subtypes were significantly different from random, with repetitions and specific call combinations occurring more often than expected by chance. The mixed call combinations were composed of two or three calls and were part of three call combination clusters. Call combinations were recorded over several years, from different individuals, and several social clusters. The most common call combination cluster consisted of six call (sub-)types. Although different combinations were generated, there were clear rules regarding which were the first and last call types produced, and combinations were highly stereotyped. Two of the three call combination clusters were produced outside of feeding contexts, but their function remains unclear and further research is required to determine possible functions and whether these combinations could be behaviour- or group-specific.Publisher PDFPeer reviewe

Intelligent and Secure Underwater Acoustic Communication Networks

Underwater acoustic (UWA) communication networks are promising techniques for medium- to long-range wireless information transfer in aquatic applications. The harsh and dynamic water environment poses grand challenges to the design of UWA networks. This dissertation leverages the advances in machine learning and signal processing to develop intelligent and secure UWA communication networks. Three research topics are studied: 1) reinforcement learning (RL)-based adaptive transmission in UWA channels; 2) reinforcement learning-based adaptive trajectory planning for autonomous underwater vehicles (AUVs) in under-ice environments; 3) signal alignment to secure underwater coordinated multipoint (CoMP) transmissions. First, a RL-based algorithm is developed for adaptive transmission in long-term operating UWA point-to-point communication systems. The UWA channel dynamics are learned and exploited to trade off energy consumption with information delivery latency. The adaptive transmission problem is formulated as a partially observable Markov decision process (POMDP) which is solved by a Monte Carlo sampling-based approach, and an expectation-maximization-type of algorithm is developed to recursively estimate the channel model parameters. The experimental data processing reveals that the proposed algorithm achieves a good balance between energy efficiency and information delivery latency. Secondly, an online learning-based algorithm is developed for adaptive trajectory planning of multiple AUVs in under-ice environments to reconstruct a water parameter field of interest. The field knowledge is learned online to guide the trajectories of AUVs for collection of informative water parameter samples in the near future. The trajectory planning problem is formulated as a Markov decision process (MDP) which is solved by an actor-critic algorithm, where the field knowledge is estimated online using the Gaussian process regression. The simulation results show that the proposed algorithm achieves the performance close to a benchmark method that assumes perfect field knowledge. Thirdly, the dissertation presents a signal alignment method to secure underwater CoMP transmissions of geographically distributed antenna elements (DAEs) against eavesdropping. Exploiting the low sound speed in water and the spatial diversity of DAEs, the signal alignment method is developed such that useful signals will collide at the eavesdropper while stay collision-free at the legitimate user. The signal alignment mechanism is formulated as a mixed integer and nonlinear optimization problem which is solved through a combination of the simulated annealing method and the linear programming. Taking the orthogonal frequency-division multiplexing (OFDM) as the modulation technique, simulation and emulated experimental results demonstrate that the proposed method significantly degrades the eavesdropper\u27s interception capability

Long range and duration underwater localization using molecular messaging

In this paper, we tackle the problem of how to locate a single entity with an unknown location in a vast underwater search space. In under-water channels, traditional wave-based signals suffer from rapid distance- and time-dependent energy attenuation, leading to expensive and lengthy search missions. In view of this, we investigate two molecular messaging methods for location discovery: a Rosenbrock gradient ascent algorithm, and a chemical encoding messaging method. In absence of explicit diffusion channel knowledge and in presence of diffusion noise, the Rosenbrock method is adapted to account for the blind search process and allow the robot to recover in areas of zero gradient. The two chemical methods are found to offer attractive performance trade-offs in complexity and robustness. Compared to conventional acoustic signals, the chemical methods proposed offers significantly longer propagation distance (1000km) and longer signal persistence duration (months)

Underwater mapping using a SONAR

Este estudo explora a capacidade física do raio de um sonar mecânico para construir um mapa do ambiente envolvente e encontrar a localização do veículo nesse mesmo mapa. Os dados do sonar alimentam um extrator de pontos de interesse e um algoritmo SLAM. Esse algoritmo é composto por uma implementação de Octomaps, junto com um filtro de partículas. Vários testes foram executados dentro de um ambiente estruturado e os resultados desses testes são demonstrados neste estudo.This study explores the physical capabilities of the beam of a mechanical scanning imaging sonar to build a map of the surrounding environment and to find the location of the vehicle within the map. The data from the sonar feed into a feature extractor and a SLAM algorithm. The SLAM algorithm is composed of an Octomaps implementation together with a particle filter. Several tests were ran within a structured environment and the map of the structured environment as well as the location of the vehicle are presented

Environmental noise reduces predation rate in an aquatic invertebrate

Noise is one of a wide range of disturbances associated with human activities that have been shown to have detrimental impacts on a wide range of species, from montane regions to the deep marine environment. Noise may also have community-level impacts via predator–prey interactions, thus jeopardising the stability of trophic networks. However, the impact of noise on freshwater ecosystems is largely unknown. Even more so is the case of insects, despite their crucial role in trophic networks. Here, we study the impact of underwater noise on the predatory functional response of damselfly larvae. We compared the feeding rates of larvae under anthropogenic noise, natural noise, and silent conditions. Our results suggest that underwater noise (pooling the effects of anthropogenic noise and natural noise) decreases the feeding rate of damselflies significantly compared to relatively silent conditions. In particular, natural noise increased the handling time significantly compared to the silent treatment, thus reducing the feeding rate. Unexpectedly, feeding rates under anthropogenic noise were not reduced significantly compared to silent conditions. This study suggests that noise per se may not necessarily have negative impacts on trophic interactions. Instead, the impact of noise on feeding rates may be explained by the presence of nonlinearities in acoustic signals, which may be more abundant in natural compared to anthropogenic noise. We conclude by highlighting the importance of studying a diversity of types of acoustic pollution, and encourage further work regarding trophic interactions with insects using a functional response approach