OceanSonar.jl

This Julia package is in development.

The purpose of this package is to implement ocean acoustics and sonar models. Its goal is an open source community-developed acoustics package that enables:

• Performance like C++ and Fortran with legible syntax like MATLAB and Python.
• Distributed and parallel computing provided by Julia.
• Reproducibility of literature books and papers.
• Composability and modularity of models.
• Documented verbosities of mathematical results.
• Ease of collaboration.
• Code implementations that can be introspected mathematically, compiler-wise, and visualisation functionalities are built-in.
• Reliance and use of community-developed and tested equation solver libraries for differential equations, root finders, etc.

Motivations of the primary author:

• Mastery of field.
• Community engagement.
• Julia programming enjoyment and learning.
• Demonstration of Julia as next generation scientific software tool for analysis and deployment.

Usage

Installation requires the Julia programming language.

This package is still under development, thus the repository address is required for installation.

1. Install Julia using the instructions here.
2. Open a Julia REPL (terminal).
3. Activate the Pkg mode by pressing ].
4. Enter add https://github.com/kapple19/OceanSonar.jl which installs the OceanSonar.jl package.
5. Return to the Julian mode by backspacing.
6. Enter using OceanSonar.

Demonstration

Acoustic ray tracing

using OceanSonar
using CairoMakie

scen = Scenario("Munk Profile")
prop = Propagation("Trace", scen, angles = critical_angles(scen, N = 21))

visual(Beam, prop)

using OceanSonar
using CairoMakie

scen = Scenario("Parabolic Bathymetry")
lowest_angle = atan(1, 5)
highest_angle = atan(5, 2)
angles = angles = range(lowest_angle, highest_angle, 31)
prop = Propagation("Trace", scen, angles = angles)

visual(Beam, prop)

Visualise frequency changes (issues with CairoMakie image saving)

using OceanSonar
using CairoMakie
using Statistics

model = "Lloyd Mirror"

function run_prop(f)
    scen = Scenario(model)
    scen.f = f
    return Propagation("Trace", scen)
end

props = [run_prop(f) for f in series125(5e3)]

bounds(data) = mean(data) .+ (3std(data) * [-1, 1])
clims = [prop.PL for prop in props] |> splat(vcat) |> bounds

fig = Figure()
num_rows, num_cols = OceanSonar.rect_or_square_gridsize(props |> length)
heatmaps = Makie.Plot[]
for row in 1:num_rows, col in 1:num_cols
    lin = LinearIndices((num_cols, num_rows))

    idx_prop = lin[col, row]
    prop = props[idx_prop]

    pos = fig[row, col]
    axis = Axis(pos,
        yreversed = true,
        title = string(prop.scen.f, " Hz")
    )
    hidedecorations!(axis)

    hm = heatmap!(axis,
        prop,
        colormap = Reverse(:jet),
        colorrange = clims,
        interpolate = true # https://github.com/MakieOrg/Makie.jl/issues/2514
    )
    push!(heatmaps, hm)
end
Colorbar(fig[:, end+1], heatmaps[1],
    label = OceanSonar.label(Propagation)
)
Label(fig[0, :], model,
    fontsize = 25
)

fig

Compare square root operator approximations for the parabolic equation

using OceanSonar
using CairoMakie

scen = Scenario("Lloyd Mirror")
config = ParabolicConfig()
for model = list_models(RationalFunctionApproximation)
    # config.marcher = marcher
    # prop = Propagation(config, scen)
    # visual!(prop) # TODO: tile
end

nothing

In development.

Visualise the equation for an OceanSonar.jl model

using OceanSonar
using Symbolics

@variables x z
ocean_celerity("Munk", x, z) |> string

1500(1 + 0.00737(-1 + 2(-1 + (1//1300)*z) + exp(-2(-1 + (1//1300)*z))))

Implementation & Roadmap Summary

• Sonar oceanography

  • Conceptually-structured hierarchical containers
  • Convenience uni/multivariate interpolators
  • Inbuilt extensive suite of environments from literature

• Underwater acoustics

  • Reflection dynamics and losses
  • Scattering and reverberation
  • Propagation solution methods
    • Ray methods
    • Wavenumber integration
    • Normal modes
    • Parabolic equations
    • Finite difference and finite element methods
  • Replication of literature results

• Signal processing

  • Sonar types hierarchy enumeration
  • Efficiently implemented sonar type-sensitive sonar equation term calculations

• Statistical detection theory

  • ROC curves
  • Signal detector design
  • Detection metrics e.g. signal excess, transition detection probability, etc.

• Documentation

  • Book publishing of ocean sonar theory with executable, reproducible implementation examples
  • Mathematical formulations accompany implementation, reducing vagueness
  • Pushing frontiers of ocean acoustics and sonar with reproducible publications

• Computational performance

  • Removing allocations
  • Parallel and distributed computing

• Front-end compatibility

  • ModelingToolkit.jl compatibility
  • Ocean sonar GUI

Citation

Cite this work with citation.bib:

@misc{OceanSonar.jl,
    author  = {Aaron Kaw <[email protected]> and contributors},
    title   = {OceanSonar.jl},
    url     = {https://github.com/kapple19/OceanSonar.jl},
    version = {v1.0.0-DEV},
    year    = {2024},
    month   = {3}
}

Academic Bibliography

Abraham, D. A. (2019). Underwater Acoustic Signal Processing: Modeling, Detection, and Estimation. Springer.

Ainslie, M. A. (2010). Principles of Sonar Performance Modelling. Springer.

Jensen, F. B., Kuperman, W. A., Porter, M. B., & Schmidt, H. (2011). Computational Ocean Acoustics (2nd Ed.). Springer.

Lurton, X. (2016). An Introduction to Underwater Acoustics: Principles and Applications (2nd Ed.). Springer.

Software Bibliography

No citation provided:

• Aqua.jl
• Documenter.jl
• JET.jl
• Latexify.jl
• LiveServer.jl
• PropCheck.jl

Provided citations:

@article{DifferentialEquations.jl-2017,
    author = {Rackauckas, Christopher and Nie, Qing},
    doi = {10.5334/jors.151},
    journal = {The Journal of Open Research Software},
    keywords = {Applied Mathematics},
    note = {Exported from https://app.dimensions.ai on 2019/05/05},
    number = {1},
    pages = {},
    title = {DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia},
    url = {https://app.dimensions.ai/details/publication/pub.1085583166 and http://openresearchsoftware.metajnl.com/articles/10.5334/jors.151/galley/245/download/},
    volume = {5},
    year = {2017}
}

@article{RevelsLubinPapamarkou2016,
    title = {Forward-Mode Automatic Differentiation in {J}ulia},
    author = {{Revels}, J. and {Lubin}, M. and {Papamarkou}, T.},
    journal = {arXiv:1607.07892 [cs.MS]},
    year = {2016},
    url = {https://arxiv.org/abs/1607.07892}
}

@software{IntervalArithmetic.jl,
    author = {David P. Sanders and Luis Benet},
    title  = {IntervalArithmetic.jl},
    url    = {https://github.com/JuliaIntervals/IntervalArithmetic.jl},
    year   = {2014},
    doi    = {10.5281/zenodo.3336308}
}

@article{Julia-2017,
    title={Julia: A fresh approach to numerical computing},
    author={Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and Shah, Viral B},
    journal={SIAM {R}eview},
    volume={59},
    number={1},
    pages={65--98},
    year={2017},
    publisher={SIAM},
    doi={10.1137/141000671},
    url={https://epubs.siam.org/doi/10.1137/141000671}
}

@misc{ma2021modelingtoolkit,
    title={ModelingToolkit: A Composable Graph Transformation System For Equation-Based Modeling},
    author={Yingbo Ma and Shashi Gowda and Ranjan Anantharaman and Chris Laughman and Viral Shah and Chris Rackauckas},
    year={2021},
    eprint={2103.05244},
    archivePrefix={arXiv},
    primaryClass={cs.MS}
}

@article{PlotsJL,
    doi = {https://doi.org/10.5334/jors.431},
    url = {https://openresearchsoftware.metajnl.com/articles/10.5334/jors.431/},
    author = {Christ, Simon and Schwabeneder, Daniel and Rackauckas, Christopher and Borregaard, Michael Krabbe and Breloff, Thomas},
    keywords = {Graphics (cs.GR), FOS: Computer and information sciences, FOS: Computer and information sciences, I.3.3},
    title = {Plots.jl -- a user extendable plotting API for the julia programming language},
    publisher = {Journal of Open Research Software},
    year = {2023},
    copyright = {Creative Commons Attribution 4.0 International}
}