Search results
Results From The WOW.Com Content Network
In many practical signal processing problems, the objective is to estimate from measurements a set of constant parameters upon which the received signals depend. There have been several approaches to such problems including the so-called maximum likelihood (ML) method of Capon (1969) and Burg's maximum entropy (ME) method.
Signal processing is an electrical engineering subfield that focuses on analyzing, modifying and synthesizing signals, such as sound, images, potential fields, seismic signals, altimetry processing, and scientific measurements. [1]
Standard method like Gauss elimination can be used to solve the matrix equation for .A more numerically stable method is provided by QR decomposition method. Since the matrix is a symmetric positive definite matrix, can be solved twice as fast with the Cholesky decomposition, while for large sparse systems conjugate gradient method is more effective.
Pages in category "Statistical signal processing" The following 23 pages are in this category, out of 23 total. This list may not reflect recent changes. B.
In statistical signal processing, the goal of spectral density estimation (SDE) or simply spectral estimation is to estimate the spectral density (also known as the power spectral density) of a signal from a sequence of time samples of the signal. [1] Intuitively speaking, the spectral density characterizes the frequency content of
In the forward prediction case, we have () = with the input signal () as the most up to date sample. The backward prediction case is d ( k ) = x ( k − i − 1 ) {\displaystyle d(k)=x(k-i-1)\,\!} , where i is the index of the sample in the past we want to predict, and the input signal x ( k ) {\displaystyle x(k)\,\!} is the most recent sample.
The question then is whether it is possible to separate these contributing sources from the observed total signal. When the statistical independence assumption is correct, blind ICA separation of a mixed signal gives very good results. [5] It is also used for signals that are not supposed to be generated by mixing for analysis purposes.
Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice Hall. ISBN 0-13-042268-1. Moon, Todd K. (2000). Mathematical Methods and Algorithms for Signal Processing. Prentice-Hall. ISBN 0-201-36186-8