Fischell Erin M.

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Last Name
Fischell
First Name
Erin M.
ORCID
0000-0001-9267-179X

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Now showing 1 - 4 of 4
  • Thesis
    Characterization of underwater target geometry from Autonomous Underwater Vehicle sampling of bistatic acoustic scattered fields
    (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2015-06) Fischell, Erin M.
    One of the long term goals of Autonomous Underwater Vehicle (AUV) minehunting is to have multiple inexpensive AUVs in a harbor autonomously classify hazards. Existing acoustic methods for target classification using AUV-based sensing, such as sidescan and synthetic aperture sonar, require an expensive payload on each outfitted vehicle and expert image interpretation. This thesis proposes a vehicle payload and machine learning classification methodology using bistatic angle dependence of target scattering amplitudes between a fixed acoustic source and target for lower cost-per-vehicle sensing and onboard, fully autonomous classification. The contributions of this thesis include the collection of novel high-quality bistatic data sets around spherical and cylindrical targets in situ during the BayEx’14 and Massachusetts Bay 2014 scattering experiments and the development of a machine learning methodology for classifying target shape and estimating orientation using bistatic amplitude data collected by an AUV. To achieve the high quality, densely sampled 3D bistatic scattering data required by this research, vehicle broadside sampling behaviors and an acoustic payload with precision timed data acquisition were developed. Classification was successfully demonstrated for spherical versus cylindrical targets using bistatic scattered field data collected by the AUV Unicorn as a part of the BayEx’14 scattering experiment and compared to simulated scattering models. The same machine learning methodology was applied to the estimation of orientation of aspect-dependent targets, and was demonstrated by training a model on data from simulation then successfully estimating the orientations of a steel pipe in the Massachusetts Bay 2014 experiment. The final models produced from real and simulated data sets were used for classification and parameter estimation of simulated targets in real time in the LAMSS MOOS-IvP simulation environment.
  • Article
    Synchronous-clock range-angle relative acoustic navigation: a unified approach to multi-AUV localization, command, control, and coordination
    (Wiley, 2022-05-10) Rypkema, Nicholas R. ; Schmidt, Henrik ; Fischell, Erin M.
    This paper presents a scalable acoustic navigation approach for the unified command, control, and coordination of multiple autonomous underwater vehicles (AUVs). Existing multi-AUV operations typically achieve coordination manually by programming individual vehicles on the surface via radio communications, which becomes impractical with large vehicle numbers; or they require bi-directional intervehicle acoustic communications to achieve limited coordination when submerged, with limited scalability due to the physical properties of the acoustic channel. Our approach utilizes a single, periodically broadcasting beacon acting as a navigation reference for the group of AUVs, each of which carries a chip-scale atomic clock and fixed ultrashort baseline array of acoustic receivers. One-way travel-time from synchronized clocks and time-delays between signals received by each array element allow any number of vehicles within receive distance to determine range, angle, and thus determine their relative position to the beacon. The operator can command different vehicle behaviors by selecting between broadcast signals from a predetermined set, while coordination between AUVs is achieved without intervehicle communication by defining individual vehicle behaviors within the context of the group. Vehicle behaviors are designed within a beacon-centric moving frame of reference, allowing the operator to control the absolute position of the AUV group by repositioning the navigation beacon to survey the area of interest. Multiple deployments with a fleet of three miniature, low-cost SandShark AUVs performing closed-loop acoustic navigation in real-time provide experimental results validated against a secondary long-baseline positioning system, demonstrating the capabilities and robustness of our approach with real-world data.
  • Article
    Memory-efficient approximate three-dimensional beamforming
    (Acoustical Society of America, 2020-12-07) Rypkema, Nicholas R. ; Fischell, Erin M. ; Schmidt, Henrik
    Localization of acoustic sources using a sensor array is typically performed by estimating direction-of-arrival (DOA) via beamforming of the signals recorded by all elements. Software-based conventional beamforming (CBF) forces a trade-off between memory usage and direction resolution, since time delays associated with a set of directions over which the beamformer is steered must be pre-computed and stored, limiting the number of look directions to available platform memory. This paper describes a DOA localization method that is memory-efficient for three-dimensional (3D) beamforming applications. Its key lies in reducing 3D look directions [described by azimuth/inclination angles (ϕ, θ) when considering the array as a whole] to a single variable (a conical angle, ζ) by treating the array as a collection of sensor pairs. This insight reduces the set of look directions from two dimensions to one, enabling computational and memory efficiency improvements and thus allowing direction resolution to be increased. This method is described and compared to CBF, with comparisons provided for accuracy, computational speedup, and memory usage. As this method involves the incoherent summation of sensor pair outputs, gain is limited, restricting its use to localization of strong sources—e.g., for real-time acoustic localization on embedded systems, where computation and/or memory are limited.
  • Article
    Multistatic acoustic characterization of seabed targets
    (Acoustical Society of America, 2017-09-25) Fischell, Erin M. ; Schmidt, Henrik
    One application for autonomous underwater vehicles (AUVs) is detecting and classifying hazardous objects on the seabed. An acoustic approach to this problem has been studied in which an acoustic source insonifies seabed target while receiving AUVs with passive sensing payloads discriminate targets based on features of the three dimensional scattered fields. The OASES-SCATT simulator was used to study how scattering data collected by mobile receivers around targets insonified by mobile sources might be used for sphere and cylinder target characterization in terms of shape, composition, and size. The impact of target geometry on these multistatic scattering fields is explored, and a discrimination approach developed in which the source and receiver circle the target with the same radial speed. The frequency components of the multistatic scattering data at different bistatic angles are used to form models for target characteristics. Data are then classified using these models. Classification accuracies were greater than 98% for shape and composition. Regression for target volume showed potential, with 90% chance of errors less than 15%. The significance of this approach is to make classification using low-cost vehicles plausible from scattering amplitudes and the relative angles between the target, source, and receiver vehicles.