Physically constrained maximum likelihood mode filtering

Papp, Joseph C.; Preisig, James C.; Morozov, Andrey K.

doi:10.1121/1.3327799

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…7 Mode filters are generally linear estimators in the form d ¼ Hp. Among the most common choices for H, we find, 4,5 • The matched filter (MF) H MF ¼ W H , where H denotes conjugate transpose.…”

Section: The Normal Mode Modelmentioning

confidence: 99%

Source depth discrimination with a vertical line array

Conan¹,

Bonnel²,

Chonavel³

et al. 2016

The Journal of the Acoustical Society of America

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Source depth estimation with a vertical line array generally involves mode filtering, then matched-mode processing. Because mode filtering is an ill-posed problem if the water column is not well-sampled, concerns for robustness motivate a simpler approach: source depth discrimination considered as a binary classification problem. It aims to evaluate whether the source is near the surface or submerged. These two hypotheses are formulated in terms of normal modes, using the concept of trapped and free modes. Decision metrics based on classic mode filters are proposed. Monte Carlo methods are used to predict performance and set the parameters of a classifier accordingly.

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Section: The Normal Mode Modelmentioning

confidence: 99%

Source depth discrimination with a vertical line array

Conan¹,

Bonnel²,

Chonavel³

et al. 2016

The Journal of the Acoustical Society of America

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“…Papp et al [4], [5] applied this idea to mode processing in the application of underwater acoustic communications. They showed that it is possible to use mode filtering to improve array processing.…”

Section: Introductionmentioning

confidence: 98%

Multi-channel DFE equalization with waveguide constraints for underwater acoustic communication

Blair

Preisig

2010

2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Although the multichannel decision feedback equalizer (DFE) has been shown to be nearly optimal and very effective for handling the difficulties of the underwater communications channel, this technique has been slow to be implemented in operational systems due to its high computational complexity. In this paper, we propose using measurements commonly available to oceanographic systems, such as depth, range, and speed of sound, to create a model of the arrival structure at a receiver with multiple elements. Three structures are presented which take advantage of this model to constrain the complexity of the multichannel DFE: a beamspace approach, a time-aligned beamspace approach, and an approach which uses discrete prolate spheroidal functions. Each of these approaches is integrated into a multichannel, direct adaptation DFE which is implemented using a recursive least squares (RLS) algorithm. The proposed structures are tested using the SPACE08 data set across a range of environmental conditions and using several exponential forgetting factors. It is found that these constrained approaches provide significant computational advantages over the full sensor-space approach and performance advantages over other computationally similar algorithms.

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“…Various methods can be employed to handle limited sample availability, including the use of application-specific physical constraints, e.g., [97], [132]. However, the most widespread general purpose methods are regularization-based.…”

Section: The Sample Limited Regimementioning

confidence: 99%

Graphical model driven methods in adaptive system identification

Yellepeddi¹

2016

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Identifying and tracking an unknown linear system from observations of its inputs and outputs is a problem at the heart of many different applications. Due to the complexity and rapid variability of modern systems, there is extensive interest in solving the problem with as little data and computation as possible.This thesis introduces the novel approach of reducing problem dimension by exploiting statistical structure on the input. By modeling the input to the system of interest as a graph-structured random process, it is shown that a large parameter identification problem can be reduced into several smaller pieces, making the overall problem considerably simpler.Algorithms that can leverage this property in order to either improve the performance or reduce the computational complexity of the estimation problem are developed. The first of these, termed the graphical expectation-maximization least squares (GEM-LS) algorithm, can utilize the reduced dimensional problems induced by the structure to improve the accuracy of the system identification problem in the low sample regime over conventional methods for linear learning with limited data, including regularized least squares methods.Next, a relaxation of the GEM-LS algorithm termed the relaxed approximate graph structured least squares (RAGS-LS) algorithm is obtained that exploits structure to perform highly efficient estimation. The RAGS-LS algorithm is then recast into a recursive framework termed the relaxed approximate graph structured recursive least squares (RAGS-RLS) algorithm, which can be used to track time-varying linear systems with low complexity while achieving tracking performance comparable to much more computationally intensive methods.The performance of the algorithms developed in the thesis in applications such as channel identification, echo cancellation and adaptive equalization demonstrate that the gains admitted by the graph framework are realizable in practice. The methods have wide applicability, and in particular show promise as the estimation and adaptation algorithms for a new breed of fast, accurate underwater acoustic modems.The contributions of the thesis illustrate the power of graphical model structure in simplifying difficult learning problems, even when the target system is not directly structured.Thesis Supervisor: James C. Preisig Title: Scientist Emeritus, Woods Hole Oceanographic Institution AcknowledgmentsPiglet noticed that even though he had a Very Small Heart, it could hold a rather large amount of Gratitude. Winnie the PoohA.A.MilneMy years at MIT have been about so much more than a degree. In the past six years you have introduced to more worlds than I ever knew existed. As this long and wonderful journey ends, I find that my heart can, too, hold so much gratitude, for everything that you have given.Of course, I haven't said who "you" is, so here goes.It is impossible to write any list of acknowledgements without having at the very top my thesis advisor Dr. James Preisig, who has truly been a wide-angle lens f...

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Physically constrained maximum likelihood mode filtering

Cited by 11 publications

References 13 publications

Source depth discrimination with a vertical line array

Source depth discrimination with a vertical line array

Multi-channel DFE equalization with waveguide constraints for underwater acoustic communication

Graphical model driven methods in adaptive system identification

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