Highlights of statistical signal and array processing

Hero, Alfred O.; Messer, H.; Goldberg, J.; Thomson, David J.; Amin, Muhammad; Giannakis, Georgios B.; Swami, Ananthram; Tugnait, J.K.; Nehorai, A.; Swindlehurst, A. Lee; Cardoso, J.-F.; Tong, Lang; Krolik, Jeffrey L.

doi:10.1109/79.708539

Cited by 51 publications

(24 citation statements)

References 27 publications

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“…For instance, in array processing and communication systems, frequency-domain processing of cyclostationary signals is quite common [59], [81], [188]. In such applications, when the underlying problem is system identification, the results of this work can be readily applied.…”

Section: Cyclostationary Input Processesmentioning

confidence: 94%

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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Section: Cyclostationary Input Processesmentioning

confidence: 94%

Graphical model driven methods in adaptive system identification

Yellepeddi¹

2016

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“…Traditionally, the shift retrieval problem is solved by maximizing the cross-correlation between the two signals [7]. In this paper, we revisit this classical problem, and show how the basic premise of compressive sensing (CS) [8], [9], [10], [11] can be used in the context of shift retrieval.…”

mentioning

confidence: 99%

Compressive Shift Retrieval

Eldar¹,

Yang²,

Sastry³

2014

IEEE Trans. Signal Process.

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Abstract-The classical shift retrieval problem considers two signals in vector form that are related by a shift. The problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals. Inspired by compressive sensing, in this paper, we seek to estimate the shift directly from compressed signals. We show that under certain conditions, the shift can be recovered using fewer samples and less computation compared to the classical setup. Of particular interest is shift estimation from Fourier coefficients. We show that under rather mild conditions only one Fourier coefficient suffices to recover the true shift.

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“…A solution used in those systems is equalization filters (Haykin, 2000;Hero et al, 1998;Liu and Lin, 2004;Tugnait et al, 2000). In this report, the use of linear time-invariant equalization filters for EMG biopotential amplifiers is described.…”

Section: Introductionmentioning

confidence: 99%

Equalization filters for multiple-channel electromyogram arrays

Clancy

Xia

Christie

et al. 2007

Journal of Neuroscience Methods

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Multiple channels of electromyogram activity are frequently transduced via electrodes, then combined electronically to form one electrophysiologic recording, e.g. bipolar, linear double difference and Laplacian montages. For high quality recordings, precise gain and frequency response matching of the individual electrode potentials is achieved in hardware (e.g., an instrumentation amplifier for bipolar recordings). This technique works well when the number of derived signals is small and the montages are pre-determined. However, for array electrodes employing a variety of montages, hardware channel matching can be expensive and tedious, and limits the number of derived signals monitored. This report describes a method for channel matching based on the concept of equalization filters. Monopolar potentials are recorded from each site without precise hardware matching. During a calibration phase, a time-varying linear chirp voltage is applied simultaneously to each site and recorded. Based on the calibration recording, each monopolar channel is digitally filtered to "correct" for (equalize) differences in the individual channels, and then any derived montages subsequently created. In a hardware demonstration system, the common mode rejection ratio (at 60 Hz) of bipolar montages improved from 35.2 ± 5.0 dB (prior to channel equalization) to 69.0 ± 5.0 dB (after equalization).

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Highlights of statistical signal and array processing

Cited by 51 publications

References 27 publications

Graphical model driven methods in adaptive system identification

Graphical model driven methods in adaptive system identification

Compressive Shift Retrieval

Equalization filters for multiple-channel electromyogram arrays

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