Fast matching methods for inversion with underwater sound

Michalopoulou, Zoi‐Heleni; Ma, Xiaoqun; Picarelli, Michele; Ghosh-Dastidar, Urmi

doi:10.1109/oceans.2000.881327

Cited by 4 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This suggests that matching the locations with significant contributions in the delay-Doppler plane may be a reasonable criterion for quantifying the discrepancy between DDSFs, even at high frequencies. The approach proposed in [14], [15] for simple and robust inversion by matching the time difference between selected arrivals seems to be directly inspired by this. In our work we adopt an intermediate approach, discarding the phase information in DDSFs when evaluating discrepancies, but keeping the relative magnitudes of arrivals.…”

Section: B Ddsf Discrepancy Measuresmentioning

confidence: 99%

“…Matching DDSFs is envisaged for estimating geometric information such as source-receiver positions, velocities, and water depth, as modeling uncertainties at high frequencies seem too large to recover more subtle properties such as bottom composition. The framework is reminiscent of [14], [15], where the delays between selected multipath arrivals were used to invert for the source position through a modelbased approach. In our work sparse DDSF estimation for single-carrier communications is used to obtain a "skeleton" of the acoustic field, including dynamic information on relative velocities from the Doppler shifts observed on different paths.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification and matching of sparse Delay-Doppler Spread Functions from high-frequency communications signals

Zamanizadeh

Gomes

Bioucas-Dias

2010

Oceans 2010 MTS/Ieee Seattle

View full text Add to dashboard Cite

Abstract-This work proposes methods for estimating the responses of time-varying underwater acoustic channels using 2 − 1 basis pursuit and quantifying their similarity. This is motivated by problems where environmental information is to be extracted from communications signals through high-frequency ocean acoustic tomographic techniques. We view underwater channels as time-varying linear systems and characterize them by their Delay-Doppler Spread Functions (DDSF), which have many adjustable parameters but are often very sparse. Basis pursuit estimation of DDSF coefficients is achieved with either the SpaRSA or TwIST algorithms, which can efficiently handle sparse unconstrained 2 − 1 minimization even for very large problem sizes, and directly operate on the complex signals of baseband communication models. We show that these algorithms offer better performance than matching pursuit methods, in terms of DDSF estimation accuracy and computational complexity, using both simulated and real data. We examine criteria based on Euclidean distance, correlation, and Kullback-Leibler divergence for quantifying the similarity of DDSFs, as needed in the envisaged tomographic systems for matching observations and model predictions. Their performance is found to be similar, with the correlation metric showing somewhat less consistent performance than the other two in simulation. Our matching approach also enables a simplification of DDSF estimation algorithms by tolerating the inherent biasing in 2 − 1 solutions, and thereby avoiding the need for further postprocessing.

show abstract

Section: B Ddsf Discrepancy Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification and matching of sparse Delay-Doppler Spread Functions from high-frequency communications signals

Zamanizadeh

Gomes

Bioucas-Dias

2010

Oceans 2010 MTS/Ieee Seattle

View full text Add to dashboard Cite

show abstract

“…Instead, the literature appears to treat other problems with elegant approaches where some of the variables are known without error ͑such as some or all of the receiver coordinates͒ and/or some linear approximation is adopted. 12,[15][16][17][18][19][20] The approach taken here has its root in a method for estimating the distribution of an animal's location given realistic a priori estimates for the distributions of sound speed, receiver locations, and measurement error. 9 We explain how that approach can be generalized to estimate the distributions of all the variables and how to include dynamical models for the evolution of all variables between the receptions of different animal calls.…”

Section: Introductionmentioning

confidence: 99%

Probability distributions for locations of calling animals, receivers, sound speeds, winds, and data from travel time differences

Spiesberger

2005

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

A new nonlinear sequential Monte Carlo technique is used to estimate posterior probability distributions for the location of a calling animal, the locations of acoustic receivers, sound speeds, winds, and the differences in sonic travel time between pairs of receivers from measurements of those differences, while adopting realistic prior distributions of the variables. Other algorithms in the literature appear to be too inefficient to yield distributions for this large number of variables (up to 41) without recourse to a linear approximation. The new technique overcomes the computational inefficiency of other algorithms because it does not sequentially propagate the joint probability distribution of the variables between adjacent data. Instead, the lower and upper bounds of the distributions are propagated. The technique is applied to commonly encountered problems that were previously intractable such as estimating how accurately sound speed and poorly known initial locations of receivers can be estimated from the differences in sonic travel time from calling animals, while explicitly modeling distributions of all the variables in the problem. In both cases, the new technique yields one or two orders of magnitude improvements compared with initial uncertainties. A new nonlinear sequential Monte Carlo technique is used to estimate posterior probability distributions for the location of a calling animal, the locations of acoustic receivers, sound speeds, winds, and the differences in sonic travel time between pairs of receivers from measurements of those differences, while adopting realistic prior distributions of the variables. Other algorithms in the literature appear to be too inefficient to yield distributions for this large number of variables ͑up to 41͒ without recourse to a linear approximation. The new technique overcomes the computational inefficiency of other algorithms because it does not sequentially propagate the joint probability distribution of the variables between adjacent data. Instead, the lower and upper bounds of the distributions are propagated. The technique is applied to commonly encountered problems that were previously intractable such as estimating how accurately sound speed and poorly known initial locations of receivers can be estimated from the differences in sonic travel time from calling animals, while explicitly modeling distributions of all the variables in the problem. In both cases, the new technique yields one or two orders of magnitude improvements compared with initial uncertainties. The technique is suitable for accurately estimating receiver locations from animal calls.

show abstract

Gibbs sampling optimization in underwater sound problems

Michalopoulou

MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295)

View full text Add to dashboard Cite

Fast matching methods for inversion with underwater sound

Cited by 4 publications

References 13 publications

Identification and matching of sparse Delay-Doppler Spread Functions from high-frequency communications signals

Identification and matching of sparse Delay-Doppler Spread Functions from high-frequency communications signals

Probability distributions for locations of calling animals, receivers, sound speeds, winds, and data from travel time differences

Gibbs sampling optimization in underwater sound problems

Contact Info

Product

Resources

About