Structured least squares problems and robust estimators

Cataloged from PDF version of article.A novel approach is proposed to provide robust and accurate estimates for linear regression problems when both the measurement vector and the coefficient matrix are structured and subject to errors or uncertainty. A new analytic formulation is developed in terms of the gradient flow of the residual norm to analyze and provide estimates to the regression. The presented analysis enables us to establish theoretical performance guarantees to compare with existing methods and also offers a criterion to choose the regularization parameter autonomously. Theoretical results and simulations in applications such as blind identification, multiple frequency estimation and deconvolution show that the proposed technique outperforms alternative methods in mean-squared error for a significant range of signal-to-noise ratio values

Recovery of sparse perturbations in Least Squares problems

We show that the exact recovery of sparse perturbations on the coefficient matrix in overdetermined Least Squares problems is possible for a large class of perturbation structures. The well established theory of Compressed Sensing enables us to prove that if the perturbation structure is sufficiently incoherent, then exact or stable recovery can be achieved using linear programming. We derive sufficiency conditions for both exact and stable recovery using known results of ℓ 0/ℓ 1 equivalence. However the problem turns out to be more complicated than the usual setting used in various sparse reconstruction problems. We propose and solve an optimization criterion and its convex relaxation to recover the perturbation and the solution to the Least Squares problem simultaneously. Then we demonstrate with numerical examples that the proposed method is able to recover the perturbation and the unknown exactly with high probability. The performance of the proposed technique is compared in blind identification of sparse multipath channels. © 2011 IEEE

Expectation maximization based matching pursuit

A novel expectation maximization based matching pursuit (EMMP) algorithm is presented. The method uses the measurements as the incomplete data and obtain the complete data which corresponds to the sparse solution using an iterative EM based framework. In standard greedy methods such as matching pursuit or orthogonal matching pursuit a selected atom can not be changed during the course of the algorithm even if the signal doesn't have a support on that atom. The proposed EMMP algorithm is also flexible in that sense. The results show that the proposed method has lower reconstruction errors compared to other greedy algorithms using the same conditions. © 2012 IEEE

Structured least squares with bounded data uncertainties

In many signal processing applications the core problem reduces to a linear system of equations. Coefficient matrix uncertainties create a significant challenge in obtaining reliable solutions. In this paper, we present a novel formulation for solving a system of noise contaminated linear equations while preserving the structure of the coefficient matrix. The proposed method has advantages over the known Structured Total Least Squares (STLS) techniques in utilizing additional information about the uncertainties and robustness in ill-posed problems. Numerical comparisons are given to illustrate these advantages in two applications: signal restoration problem with an uncertain model and frequency estimation of multiple sinusoids embedded in white noise. ©2009 IEEE

Compressive sampling and adaptive multipath estimation [Sikiştirmali örnekleme ve uyarlamali çokyollu kestirim]

In many signal processing problems such as channel estimation and equalization, the problem reduces to a linear system of equations. In this proceeding we formulate and investigate linear equations systems with sparse perturbations on the coefficient matrix. In a large class of matrices, it is possible to recover the unknowns exactly even if all the data, including the coefficient matrix and observation vector is corrupted. For this aim, we propose an optimization problem and derive its convex relaxation. The numerical results agree with the previous theoretical findings of the authors. The technique is applied to adaptive multipath estimation in cognitive radios and a significant performance improvement is obtained. The fact that rapidly varying channels are sparse in delay and doppler domain enables our technique to maintain reliable communication even far from the channel training intervals. ©2010 IEEE

Polar compressive sampling: A novel technique using polar codes [Kutupsal sikiştirmali örnekleme: Kutuplaşma kodlari ile yeni bir yöntem]

Recently introduced Polar coding is the first practical coding technique that can be proven to achieve the Shannon capacity for a multitude of communication channels. Polar codes are close to Reed-Muller codes except the fact that they are tuned for the parameters of the channel. Hence, Polar codes are shown to offer better performance, e.g., in the erasure channel. It is known that second order Reed-Muller codes can be used for Compressed Sensing. Inspired by that result, we propose Polar codes as measurement matrices in CS and compare their numerical performances. We also present the algebraic relation between the erasure channel and CS theory, and discuss fast solution techniques. ©2010 IEEE

Sparse signal reconstruction with ellipsoid enlargement [Elli̇psoi̇d geni̇şletmeyle seyrek si̇nyal geri̇ oluşturma]

In this work a novel method for reconstructing sparse x in a noisy full rank linear system such as b = Ax + n is developed. The proposed method depends on enlarging the ellipsiod defined by the data constraintAx - b2 = ε and iteratively resetting the axes where the signal is zero. The proposed method has a higher reconstruction performance compared to standard iterative and ℓ1 norm minimization based sparse recovery methods. Also our method relaxes the sparsity level constraint to be reconstructed by the standard methods for an underdetermined system. © 2011 IEEE

Compressed sensing on ambiguity function domain for high resolution detection [Yüksek çözünürlüklü tespit için belirsizlik fonksiyonu düzleminde sikiştirilmiş algilama]

In this paper, by using compressed sensing techniques, a new approach to achieve robust high resolution detection in sparse multipath channels is presented. Currently used sparse reconstruction techniques are not immediately applicable in wireless channel modeling and radar signal processing. Here, we make use of the cross-ambiguity function (CAF) and transformed the reconstruction problem from time to delay-Doppler domain for efficient exploitation of the delay-Doppler diversity of the multipath components. Simulation results quantify the performance gain and robustness obtained by this new CAF based compressed sensing approach. ©2010 IEEE

A novel technique for a linear system of equations applied to channel equalization [Doǧrusal denklem sistemleri için yeni bir yöntem ve kanal eşitlemeye uygulanmasi]

In many inverse problems of signal processing the problem reduces to a linear system of equations. Accurate and robust estimation of the solution with errors in both measurement vector and coefficient matrix is a challenging task. In this paper a novel formulation is proposed which takes into account the structure (e.g. Toeplitz, Hankel) and uncertainties of the system. A numerical algorithm is provided to obtain the solution. The proposed technique and other methods are compared in a channel equalization example which is a fundamental necessity in communication. ©2009 IEEE