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Regularized Robust Estimators for Time Varying Uncertain Discrete-Time Systems

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Distributed Estimation Over an Adaptive Incremental Network Based on the Affine Projection Algorithm

Ali H. Sayed

We study the problem of distributed estimation based on the affine projection algorithm (APA), which is developed from Newton's method for minimizing a cost function. The proposed solution is formulated to ameliorate the limited convergence properties of l ...

IEEE2010

Nonideal sampling and regularized interpolation of noisy data

Sathish Ramani

Conventional sampling (Shannon's sampling formulation and its approximation-theoretic counterparts) and interpolation theories provide effective solutions to the problem of reconstructing a signal from its samples, but they are primarily restricted to the ...

EPFL2009

Generalized linear prediction method in phase-shifting interferometry in the presence of noise

Pramod Rastogi, Abhijit Patil, Rajesh Langoju

The effectiveness of phase-shifting interferometry (PSI) techniques employing piezoelectric device PZT in the estimation of phase depends largely on the accuracy with which the phase shifts are imparted to the device and the noise influencing the measureme ...

Elsevier2007

Regularized Robust Filters for Time-Varying Uncertain Discrete-Time Systems

Ali H. Sayed

This note develops robust filters for time-varying uncertain discrete-time systems. The developed filters are based on a data regularization solution and they enforce a minimum state-error variance property. Simulation results confirm their superior perfor ...

IEEE2004

New algorithmic methods for real-time transportation problems

Frank Crittin

Two of the most basic problems encountered in numerical optimization are least-squares problems and systems of nonlinear equations. The use of more and more complex simulation tools on high performance computers requires solving problems involving an incre ...

EPFL2004

A robust minimum-variance filter for time varying uncertain discrete-time systems

Ali H. Sayed

A robust filter is designed for uncertain discrete time models. The filter is based on a regularized solution and guarantees minimum state error variance. Simulation results confirm its superior performance over other robust filter designs. ...

2003

A uniqueness result concerning a robust regularized least-squares solution

Ali H. Sayed

In solving a robust version of regularized least squares with weighting, a certain scalar-valued optimization problem is required in order to determine the regularized robust solution and the corresponding robustified weighting parameters. This letter esta ...

Elsevier2002

The Scrambled Net Filter

Thomas Gsponer

The standard Kalman filter is a powerful and widely used tool to perform prediction, filtering and smoothing in the fields of linear Gaussian state-space models. In its standard setting it has a simple recursive form which implies high computational effici ...

2002

The Scrambled Net Filter

Thomas Gsponer

2002

The analysis of kernel ridge regression learning algorithm.

Alexei Pozdnoukhov

The paper presents Kernel Ridge Regression, a nonlinear extension of the well known statistical model of ridge regression. New insights on the method are also presented. In particular, the connection between ridge regression and local translation-invariant ...

IDIAP2002