Automated Species Classification Methods for Passive Acoustic Monitoring of Beaked Whales

The Littoral Acoustic Demonstration Center has collected passive acoustic monitoring data in the northern Gulf of Mexico since 2001. Recordings were made in 2007 near the Deepwater Horizon oil spill that provide a baseline for an extensive study of regional marine mammal populations in response to the disaster. Animal density estimates can be derived from detections of echolocation signals in the acoustic data. Beaked whales are of particular interest as they remain one of the least understood groups of marine mammals, and relatively few abundance estimates exist. Efficient methods for classifying detected echolocation transients are essential for mining long-term passive acoustic data. In this study, three data clustering routines using k-means, self-organizing maps, and spectral clustering were tested with various features of detected echolocation transients. Several methods effectively isolated the echolocation signals of regional beaked whales at the species level. Feedforward neural network classifiers were also evaluated, and performed with high accuracy under various noise conditions. The waveform fractal dimension was tested as a feature for marine biosonar classification and improved the accuracy of the classifiers. [This research was made possible by a grant from The Gulf of Mexico Research Initiative. Data are publicly available through the Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC) at https://data.gulfresearchinitiative.org.] [DOIs: 10.7266/N7W094CG, 10.7266/N7QF8R9K

Sonar systems for object recognition

The deep sea exploration and exploitation is one of the biggest challenges of the next century. Military, oil & gas, o shore wind farming, underwater mining, oceanography are some of the actors interested in this eld. The engineering and technical challenges to perform any tasks underwater are great but the most crucial element in any underwater systems has to be the sensors. In air numerous sensor systems have been developed: optic cameras, laser scanner or radar systems. Unfortunately electro magnetic waves propagate poorly in water, therefore acoustic sensors are a much preferred tool then optical ones. This thesis is dedicated to the study of the present and the future of acoustic sensors for detection, identi cation or survey. We will explore several sonar con gurations and designs and their corresponding models for target scattering. We will show that object echoes can contain essential information concerning its structure and/or composition

Underwater object localization using a biomimetic binaural sonar

Thesis (S.M. in Oceanographic Engineering)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Ocean Engineering; and the Woods Hole Oceanographic Institution), 1999.Includes bibliographical references (leaves 85-89).by Qiang Wang.S.M.in Oceanographic Engineerin

Biologically inspired processing of radar and sonar target echoes

Modern radar and sonar systems rely on active sensing to accomplish a variety of tasks, including detection and classification of targets, accurate localization and tracking, autonomous navigation and collision avoidance. Bats have relied on active sensing for over 50 million years and their echolocation system provides remarkable perceptual and navigational performance that are of envy to synthetic systems. The aim of this study is to investigate the mechanisms bats use to process echo acoustic signals and investigate if there are lessons that can be learned and ultimately applied to radar systems. The basic principles of the bat auditory system processing are studied and applied to radio frequencies. A baseband derivative of the Spectrogram Correlation and Transformation (SCAT) model of the bat auditory system, called Baseband SCAT (BSCT), has been developed. The BSCT receiver is designed for processing radio-frequency signals and to allow an analytical treatment of the expected performance. Simulations and experiments have been carried out to confirm that the outputs of interest of both models are “equivalent”. The response of the BSCT to two closely spaced targets is studied and it is shown that the problem of measuring the relative distance between two targets is converted to a problem of measuring the range to a single target. Nearly double improvement in the resolution between two close scatterers is achieved with respect to the matched filter. The robustness of the algorithm has been demonstrated through laboratory measurements using ultrasound and radio frequencies (RF). Pairs of spheres, flat plates and vertical rods were used as targets to represent two main reflectors

Towards a bionic bat: A biomimetic investigation of active sensing, Doppler-shift estimation, and ear morphology design for mobile robots.

Institute of Perception, Action and BehaviourSo-called CF-FM bats are highly mobile creatures who emit long calls in which much of the energy is concentrated in a single frequency. These bats face sensor interpretation problems very similar to those of mobile robots provided with ultrasonic sensors, while navigating in cluttered environments. This dissertation presents biologically inspired engineering on the use of narrowband Sonar in mobile robotics. It replicates, using robotics as a modelling medium, how CF-FM bats process and use the constant frequency part of their emitted call for several tasks, aiming to improve the design and use of narrowband ultrasonic sensors for mobile robot navigation. The experimental platform for the work is RoBat, the biomimetic sonarhead designed by Peremans and Hallam, mounted on a commercial mobile platform as part of the work reported in this dissertation. System integration, including signal processing capabilities inspired by the bat’s auditory system and closed loop control of both sonarhead and mobile base movements, was designed and implemented. The result is a versatile tool for studying the relationship between environmental features, their acoustic correlates and the cues computable from them, in the context of both static, and dynamic real-time closed loop, behaviour. Two models of the signal processing performed by the bat’s cochlea were implemented, based on sets of bandpass filters followed by full-wave rectification and low-pass filtering. One filterbank uses Butterworth filters whose centre frequencies vary linearly across the set. The alternative filterbank uses gammatone filters, with centre frequencies varying non-linearly across the set. Two methods of estimating Doppler-shift from the return echoes after cochlear signal processing were implemented. The first was a simple energy-weighted average of filter centre frequencies. The second was a novel neural network-based technique. Each method was tested with each of the cochlear models, and evaluated in the context of several dynamic tasks in which RoBat was moved at different velocities towards stationary echo sources such as walls and posts. Overall, the performance of the linear filterbank was more consistent than the gammatone. The same applies to the ANN, with consistently better noise performance than the weighted average. The effect of multiple reflectors contained in a single echo was also analysed in terms of error in Doppler-shift estimation assuming a single wider reflector. Inspired by the Doppler-shift compensation and obstacle avoidance behaviours found in CF-FM bats, a Doppler-based controller suitable for collision detection and convoy navigation in robots was devised and implemented in RoBat. The performance of the controller is satisfactory despite low Doppler-shift resolution caused by lower velocity of the robot when compared to real bats. Barshan’s and Kuc’s 2D object localisation method was implemented and adapted to the geometry of RoBat’s sonarhead. Different TOF estimation methods were tested, the parabola fitting being the most accurate. Arc scanning, the ear movement technique to recover elevation cues proposed by Walker, and tested in simulation by her, Peremans and Hallam, was here implemented on RoBat, and integrated with Barshan’s and Kuc’s method in a preliminary narrowband 3D tracker. Finally, joint work with Kim, K¨ampchen and Hallam on designing optimal reflector surfaces inspired by the CF-FM bat’s large pinnae is presented. Genetic algorithms are used for improving the current echolocating capabilities of the sonarhead for both arc scanning and IID behaviours. Multiple reflectors around the transducer using a simple ray light-like model of sound propagation are evolved. Results show phase cancellation problems and the need of a more complete model of wave propagation. Inspired by a physical model of sound diffraction and reflections in the human concha a new model is devised and used to evolve pinnae surfaces made of finite elements. Some interesting paraboloid shapes are obtained, improving performance significantly with respect to the bare transducer

Disguised Bionic Sonar Signal Waveform Design with Its Possible Camouflage Application Strategy for Underwater Sensor Platforms

IEEE The covertness of an active sonar is a very important issue and the sonar signal waveform design problem is studied to improve covertness of the system. Many marine mammals produce call pulses for communication and echolocation, and existing interception systems normally classify these biological signals as ocean noise and filter them out. Based on this, a disguised sonar signal waveform design approach with its camouflage application strategy for underwater sensor platforms is proposed by utilizing bio-inspired steganography. We first construct bionic sonar signal waveforms which are very close to the true whale whistle, and then embed these constructed bionic sonar signal waveforms into the true whale call trains to hide the real sonar signal waveforms. According to the time-frequency (TF) structure of the true whale whistle, a bionic sonar signal model is established to generate the proposed sonar signal waveforms. A single sonar signal is used to measure the range of the target and a combination of two sonar signals is utilized for measuring its speed. A high-performance range and speed measurement algorithm is deduced in detail. Based on the constructed signal waveforms and the characteristics of false killer whale call trains, a camouflage application strategy is designed to improve the camouflage ability of the sonar signal sequence. Finally, simulation results are provided to verify the performance of the proposed method

Adaptations to changes in the acoustic scene of the echolocating bat

Our natural environment is noisy and in order to navigate it successfully, we must filter out the important components so that we may guide our next steps. In analyzing our acoustic scene, one of the most common challenges is to segregate speech communication sounds from background noise; this process is not unique to humans. Echolocating bats emit high frequency biosonar signals and listen to echoes returning off objects in their environment. The sound wave they receive is a merging of echoes reflecting off target prey and other scattered objects, conspecific calls and echoes, and any naturally-occurring environmental noises. The bat is faced with the challenge of segregating this complex sound wave into the components of interest to adapt its flight and echolocation behavior in response to fast and dynamic environmental changes. In this thesis, we employ two approaches to investigate the mechanisms that may aid the bat in analyzing its acoustic scene. First, we test the bat’s adaptations to changes of controlled echo-acoustic flow patterns, similar to those it may encounter when flying along forest edges and among clutter. Our findings show that big brown bats adapt their flight paths in response to the intervals between echoes, and suggest that there is a limit to how close objects can be spaced, before the bat does not represent them as distinct any longer. Further, we consider how bats that use different echolocation signals may navigate similar environments, and provide evidence of species-specific flight and echolocation adaptations. Second, we research how temporal patterning of echolocation calls is affected during competitive foraging of paired bats in open and cluttered environments. Our findings show that “silent behavior”, the ceasing of emitting echolocation calls, which had previously been proposed as a mechanism to avoid acoustic interference, or to “eavesdrop” on another bat, may not be as common as has been reported

Object characterisation using wideband sonar pulses

Characterisation of objects in an underwater environment is challenging. Success in the task can be beneficial in a variety of scenarios, which include oil and gas pipe maintenance, archaeology, and assistance to general underwater object identification. This work focuses on object characterisation, providing a solution for material identification. To do this, one must sense the underwater environment for which there are several different ways. Some of the most popular rely on sonar images. These provide limited information about the objects,mostly the shape, size and distance to the object. The study of acoustic wave scattering over a wide frequency range provides more information about the targets characteristics. This work builds on the principles of sound scattering. An acoustic echo reflected from an object has a different pulse shape and frequency composition than its initial pulse. These changes in the pulse are due to the interaction of the sound wave with an object during the reflection process and the pulses interaction with the transmission medium. Study of the reflected pulse can provide information about physical properties such as size, material and shell thickness. The objects used in this work are limited to spherical shells made of a variety of materials, and filled with different liquids or air. The task of material identification is approached in two different ways. The first one is a machine learning based approach. The classification is not based on the object’s shape, but on its physical properties including the composition material. Two approaches will be presented: one, where the spherical shell is described by the echo’s representation in time frequency domain and one, where it is described by the form function. The objects are classified using a number of machine learning techniques including support vector machine, gradient boosting and neural networks. The machine learning approaches give good results for a number of tasks, but are not sufficient to distinguish between materials with similar properties, like water and salt water. An alternative solution is presented in this thesis, which identifies the filler and the shell materials separately. This material identification approach is based on the timing of the sound scattering components. The echo reflected from an object is formed by a number of processes. The information about these processes can be extracted from the echoes and used to identify the material. This approach does not require any training data and shows good results, which are demonstrated on both the simulated and experimental data. This work focuses on object characterisation, providing a solution for material identification using underwater acoustics and signal processing techniques