A delta least squares lattice algorithm for fast sampling
نویسندگان
چکیده
Most shift operator-based adaptive algorithms exhibit poor numerical behavior when the input discrete time process is obtained from a continuous time process by fast sampling. This includes the shift operator based least squares lattice algorithm. In this paper, we develop a delta least squares lattice algorithm. This algorithm has low computational complexity compared to the delta Levinson RLS algorithm and shows bet.ter numerical properties compared to the shift least squares lattice algorithm. Computer simulations show that the new algorithm also outperforms an existing delta least squares lattice algorithm.
منابع مشابه
Harmonics Estimation in Power Systems using a Fast Hybrid Algorithm
In this paper a novel hybrid algorithm for harmonics estimation in power systems is proposed. The estimation of the harmonic components is a nonlinear problem due to the nonlinearity of phase of sinusoids in distorted waveforms. Most researchers implemented nonlinear methods to extract the harmonic parameters. However, nonlinear methods for amplitude estimation increase time of convergence. Hen...
متن کاملLattice and QR Decomposition-Based Algorithms for Recursive Least Squares Adaptive Nonlinear Filters*
This paper presents a lattice structure for adaptive Volterra systems. The stucture is applicable to arbitrary planes of support of the Volterra kernels. A fast least squares lattice and a fast QR-lattice adaptive nonlinear filtering algorithms based on the lattice structure are also presented. These algorithms share the fast convergence property of fast least squares transversal Volterra filte...
متن کاملConvergence Models for Lattice Joint Process Estimators and Least Squares Algorithms
A simple model characterizing the convergence properties of an adaptive digital lattice filter using gradient algorithms has been reported [ 11. This model is extended to the least mean square (LMS) lattice joint process estimator [SI, and to the least squares (LS) lattice and “fast” Kalman algorithms [9] -[16]. The models in each case are compared with computer simulation. The single-stage LMS...
متن کاملEcho Cancellation of Voiceband Data Signals Using Recursive Least Squares and Stochastic Gradient Algorithms
The convergence properties of adaptive least squares (LS) and stochastic gradient (SG) algorithms are studied in the context of echo cancellation of voiceband data signals. The algorithms considered are the SG transversal, SG lattice, LS transversal (fast Kalman), and LS lattice. It is shown that for the channel estimation problem considered here, LS algorithms converge in approximately 2N iter...
متن کاملA Fast Algorithm For Computing The A-optimal Sampling Distributions In A Big Data Linear Regression
It was demonstrated in Peng and Tan (2018) that the A-optimal sampling distributions in a big data linear regression model has the same running time O(np) as the full data least squares estimator. In this article, we construct a fast algorithm to compute the sampling distributions with o(np) time and establish the relative error bounds. AMS 2000 subject classifications: Primary 62G05; secondary...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- IEEE Trans. Signal Processing
دوره 47 شماره
صفحات -
تاریخ انتشار 1998