1 edition of **A Linear Subspace Approach to Burst Communication Signal Processing** found in the catalog.

A Linear Subspace Approach to Burst Communication Signal Processing

- 72 Want to read
- 14 Currently reading

Published
**2004**
by Storming Media
.

Written in English

- TEC041000

The Physical Object | |
---|---|

Format | Spiral-bound |

ID Numbers | |

Open Library | OL11846125M |

ISBN 10 | 1423516419 |

ISBN 10 | 9781423516415 |

Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured input-output data. linear subspace of R3. Addition and scaling Deﬁnition A subset V of Rn is called a linear subspace of Rn if V contains the zero vector O, and is closed under vector addition and scaling. That is, for X,Y ∈ V and c ∈ R, we have X + Y ∈ V and cX ∈ V. What would be the smallest possible linear subspace V of Rn? The singleton.

In signal processing, reconstruction usually means the determination of an original continuous signal from a sequence of equally spaced samples.. This article takes a generalized abstract mathematical approach to signal sampling and reconstruction. For a more practical approach based on band-limited signals, see Whittaker–Shannon interpolation formula. A Review of Signal Subspace Speech Enhancement and Its Application to Noise Robust Speech Recognition. The objective of this paper is threefold: (1) to provide an extensive review of signal subspace speech enhancement, (2) to derive an upper bound for the performance of these techniques, and (3) to present a co.

Subspace Methods for Directions-of-Arrival Estimation A. Paulraj, B. Ottersten, R. Roy, A. Swindlehurst, G. Xu and T. Kailath 1. Introduction In many practical signal processing problems, the objective is to estimate from noisy measurements a set of constant parameters upon which the . Chapter 8 Subspace Metho ds In tro duction Principal Comp onen t Analysis (PCA) is applied to the analysis of time series data. In this con text w e discuss measures of complexit y and subspace metho ds for sp ectral estimation. Singular Sp ectrum Analysis Em b edding Giv en a single time series x 1 to N w e can form an emb dding.

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Derives, from rst principles, a linear subspace approach to deriving signal processing algorithms for burst communications signals. Unlike stationary or cyclostationary approaches, this method assumes that the signal of interest is nite in length, rather than in nite.

This new approach is then applied to three di erent application areas:Cited by: 1. Model-Based Processing: An Applied Subspace Identification Approach provides expert insight on developing models for designing model-based signal processors (MBSP) employing subspace identification techniques to achieve model-based identification (MBID) and enables readers to evaluate overall performance using validation and statistical Cited by: 2.

The basic subspace algorithms in the book are also implemented in a set of Matlab files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID.

The identified model allows for an optimal control of the process, leading to a significant enhancement of the production Cited by: a linear subspace approach to burst communication signal processing By Daniel Erik Gisselquist, Major Usaf, Major Usaf, Daniel Erik Gisselquist, Major Usaf and E.

Goda Abstract. A subspace approach to blind space-time signal processing for wireless communication systems Abstract: The two key limiting factors facing wireless systems today are multipath interference and multiuser by: SUBSPACE TRACKING FOR SIGNAL PROCESSING INTRODUCTION Research in subspace and component-based techniques were originated in Statistics in the middle of the last century through the problem of linear feature extraction solved by the Karhunen-Loeve Transform (KLT).

Then, it application to signal processing was initiated`. The signal subspace approach to speech processing was originally applied for speech enhancement techniques (such as [8][9][10]) and it has only been used recently as a speech classifier. Among the most powerful and applied mathematical tools, subspace has had a wide impact in signal processing and communications, and it continuously has a magic power for solving future problems.

A signal subspace approach for speech enhancement. Abstract: A comprehensive approach for nonparametric speech enhancement is developed. The underlying principle is to decompose the vector space of the noisy signal into a signal-plus-noise subspace and a noise subspace.

Enhancement is performed by removing the noise subspace and estimating the clean signal from the remaining signal subspace. signal onto two subspaces: the signal-plus-noise subspace, or simply signal subspace (since the signal dominates this sub-space),sesubspacecontainssig-nals from the noise process only, hence an estimate of the clean signal can be made by removing or nulling the components of the signal in the noise subspace and retaining only the compo-nents of the signal in the signal subspace.

based subspace approach has been investigated and tested to estimate signals which are highly corrupted by colored noise; Hu and Loizou [Y. Hu and P. Loizou, “A Generalized Subspace Approach for Enhancing Speech Corrupted by Colored Noise,” IEEE Transactions on Speech and Audio Processing, vol. 11, no.

For the last few decades, speech enhancement based on microphone arrays has primarily utilized prior information about system models, e.g., array geometry and source location. However, estimation of the time delay to align microphone inputs is largely affected by reverberation and microphone mismatch.

Preprocessing time aligning, e.g., fixed beamforming (the first branch of the. The linear model for the clean speech signal assumes that each n-dimensional vector s of the signal can be represented as (1) s = H y = ∑ i = 1 p h i y i, p ⩽ n where H = [h 1, h 2,h p] ∈ R n × p is a model matrix whose columns are orthogonal basis vectors that span the signal subspace and y = [y 1, y 2,y p] T is a zero mean.

IET Signal Processing publishes topics such as algorithm advances in single and multi-dimensional, linear and non-linear, recrusive and non-recursive digital fillers and multi-rate filter banks; the application of chaos theory and neural network based approaches to signal processing.

A perceptually based linear signal estimator for enhancing speech signals degraded by uncorrelated additive noise is developed. The estimator is designed by minimizing the signal distortion while maintaining the residual noise level below some given threshold.

The estimator is shown to be a Wiener filter with adjustable input noise level. This level is determined by the threshold of the. Principal component analysis (PCA) is one of the most widely used dimension reduction techniques.

A related easier problem is termed subspace learning or subspace estimation. Given relatively clean data, both are easily solved via singular value decomposition (SVD).

The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning (RSL) or. In this context, a challenging signal processing problem is the joint space-time equalization of multiple digital signals transmitted over multipath channels.

We propose a blind approach to determine the transmitted signals which does not use training sets to estimate the space-time channel. Itsik Bergel, Amir Leshem, in Academic Press Library in Signal Processing, Decision feedback equalizer.

For cases in which the linear processing discussed above is too far from optimal, Chen et al. [50] proposed using a ZF generalized decision feedback equalizer (GDFE). The authors demonstrate that the ZF-GDFE is a simplified version of the MMSE-GDFE, which was shown to.

new type of linear system identiﬁcation algorithms, called subspace methods. Subspace methods originate in a happy menage-a`-trois between system theory, geo-metry and numerical linear algebra.

Previous papers and books emphasizing di•erent aspects of subspace system identiﬁcation and signal processing and in which. In this paper, we derive a blind subspace channel estimator first and then design linear receivers. Following a channel input/output model that transforms a PPM signal into a sum of seemingly pulse-amplitude modulated signals, a structure similar to a code-division multiple-access (CDMA) system is observed.

Code matrices for each user are. Abstract. In this chapter, we present the signal subspace approach (SSA) for speech enhancement. The SSA is becoming a serious competitor to its already widely used frequency-domain counterparts since it seems to offer a better compromise between signal distortion and the level of the residual noise.The major goal is to find suboptimal receiver signal preprocessing that could substantially reduce receiver decoding metric computation complexity with minor degradation of the STC performance.

The author introduce linear waveform subspace projector reducing the dimensionality and thus the complexity of the receiver signal processing.An in-depth introduction to subspace methods for system identification in discrete-time linear systems thoroughly augmented with advanced and novel results, this text is structured into three parts.

Part I deals with the mathematical preliminaries: numerical linear algebra; system theory.