## Forums Search for: Wiener Filter

## Reducing channel support size using a Wiener Filter

inGreetings everyone, I have a channel with impulse response h, with size h_r x h_c and for computational reasons I will need to reduce the...

Greetings everyone, I have a channel with impulse response h, with size h_r x h_c and for computational reasons I will need to reduce the support size, i.e. "compress" the signal and represent an equivalent channel of smaller support. I tried an approach using the Weiner Filter, where I use it as a "pre-processor" to obtain a "residual" channel, such that the composite channel and Wiener f...

## Hopf or Hoff?

inI just noticed that [widrow] refers to the "Wiener-Hopf" equation, yet in that same chapter's bibliography a reference is cited with M.E.Hoff,...

I just noticed that [widrow] refers to the "Wiener-Hopf" equation, yet in that same chapter's bibliography a reference is cited with M.E.Hoff, Jr. as an author. The Wikipedia page on Widrow also uses "Hoff": http://en.wikipedia.org/wiki/Bernard_Widrow Is this the same guy? These are just spelling differences? I've always seen it as "Wiener-Hopf" equation since I used [widrow] in my...

## Basic Question about the PSD

inHi I am plotting the PSD using Wiener-Khintchin theorem (1). I recall from my signal processing course the the fourier transform is periodic...

Hi I am plotting the PSD using Wiener-Khintchin theorem (1). I recall from my signal processing course the the fourier transform is periodic with peroiod of 2 pi. Should the PSD be periodic since the wiener-khintchin theroem is essentialy a fourier transform? What should the perido be in general? PSD(f)=4*Integral[cos(2*pi*f*t)*Autocorrelation from -inf, +inf] (1) thanks!!

## Multichannel (MISO) Wiener filter design

inProblem: Design one wiener filter that best matches a time snapshot of data taken from multiple channels of equal interest, that is, the domain...

Problem: Design one wiener filter that best matches a time snapshot of data taken from multiple channels of equal interest, that is, the domain of the signal is 2D, specifically with # of time samples > > # channels. Think of a window in a grey scale 2D image. The Easter bunny tells me one approach is: 1. Average the autocorrelation matrices for each channel 2. Average th

## Wiener Hopf Equalizer delay

inCONTEXT:********************** Transmit x(n) receive y(n). Wiener-Hopf (MMSE) FIR Equalizer is... w = inv(Ryy)rxy where Ryy is the received...

CONTEXT:********************** Transmit x(n) receive y(n). Wiener-Hopf (MMSE) FIR Equalizer is... w = inv(Ryy)rxy where Ryy is the received signal autocorrelation and rxy is the channel input-output crosscorrelation: rxy(k) = E(x(n-D)y(n+k)). The D is a delay parameter chosen to make sure the equalizer is causal. It specifies the location of the impulse of the equalized channel. QUESTIO...

## wiener deconvolution

inHi every one I am working on Wiener deconvolution. my aim is to deconvolve the refelectivity function. I am using threshold method in inverse...

Hi every one I am working on Wiener deconvolution. my aim is to deconvolve the refelectivity function. I am using threshold method in inverse filtering for the deconvolution. Hf = H.*(abs(H)> 0)+1/gamma*(abs(H)==0); iHf = 1./Hf; iHf = iHf.*(abs(H)*gamma> 1)+gamma*abs(Hf).*iHf.*(abs(Hf)*gamma

## Forward Linear Prediction

inIs it possible to use the Wiener filter theory to predict a signal with no zero mean? Thanks

Is it possible to use the Wiener filter theory to predict a signal with no zero mean? Thanks

## Wiener filter with smoothed estimation of Noise

inHello all, I implemented a blockwise Wiener Filter with Matlab like this: H = a_priori_SNR./(a_priori_SNR+1) a_priori_SNR = S./N; S =...

Hello all, I implemented a blockwise Wiener Filter with Matlab like this: H = a_priori_SNR./(a_priori_SNR+1) a_priori_SNR = S./N; S = magnitude of fft of clean signal s; S= abs(fft(s)), of current block N = magnitude of fft of noise n; N= abs(fft(n)), of current block Y = magnitude of fft of noisy signal y; Y= abs(fft(y)), of current block Since in real life I have neither the...

## Wiener-Kitchen Theorem

inIf a sausage is brown then it's cooked if it's black it's f*cked. Hardy

If a sausage is brown then it's cooked if it's black it's f*cked. Hardy

## who has matlab implementation of multistage weiner filter?

inI've read J. Scott Goldstein's paper 'A Multistage Representation of the Wiener Filter Based on Orthogonal Projections' and written a test...

I've read J. Scott Goldstein's paper 'A Multistage Representation of the Wiener Filter Based on Orthogonal Projections' and written a test matlab program. But it seems wrong. Who has ever implemented it ?

## Random Variable Generation (Newbie).

inI am trying to understand how to generate random numbers. I came across the following concepts of auto correlation anc Covariance and also the...

I am trying to understand how to generate random numbers. I came across the following concepts of auto correlation anc Covariance and also the power Spectral density using the wiener khichin theorem. Please can anyone explain this concept and its relevance to a communication system if any. Also I know that correlation has to do with how different 2 signals are from each other and varian...

## how to minimize ringing

inHello, I am trying to restore a one dimentional signal using wiener deconvolution with a PSF. Because it's important for me to restore sharpness,...

Hello, I am trying to restore a one dimentional signal using wiener deconvolution with a PSF. Because it's important for me to restore sharpness, but more important, the shape at the transient edges- the ringing is getting in my way. I understand that ringing to some extent is unavoidable, but how can I miminize it? Thank you, Keren

## Concept confused: covariance matrix of sine wav

inHi, experts, What is covariance matrix of x[k]=sin(pi*k*f0)? (it contains no randomness). Should it be dependent on k? nonstationary? Then...

Hi, experts, What is covariance matrix of x[k]=sin(pi*k*f0)? (it contains no randomness). Should it be dependent on k? nonstationary? Then what is the optimal (Wiener) solution of this predictor? x'[k]=w1*x[k-1]+w2*x[k-2]; d[k]=x[k]; e[k]=d[k]-x'[k]; w should satisfy Rx*w=p where Rx is the covariance matrix and seems to be singular. If Rx is dependent on k, how to solve this? Ma...

## MMSE & LMS Equalization Question

inOk so, i'm confusing myself somewhere and its driving myself crazy. I have written matlab implementations of MMSE (wiener) and LMS...

Ok so, i'm confusing myself somewhere and its driving myself crazy. I have written matlab implementations of MMSE (wiener) and LMS (gradient method .. h = h(n-1) + mu*dE/dh) to minimize the following equation: ( s[n] - yhat[n] )^2 where s[n] is the original 'sent' message and yhat[n]=conv(Hhat,y[n]) where y[n] is the signal received at the transmitter (with noise/channel effects). That i...

## Adaptative filter

inHi all, I am to design a special adaptive filter in which the error signal is hihgly correlated with the input signal (in fact, the latter is...

Hi all, I am to design a special adaptive filter in which the error signal is hihgly correlated with the input signal (in fact, the latter is just a delayed version of the error...) Usual resolution of the Wiener equations is therefore not obvious. Of course, since the orthogonality principle is not respected anymore, even the simplest algorithm (stochastic gradient) can not be applied. ...

## Why minimising in the mean-error sense.

inHello group. Quote from Simon Haykin's book "Adaptive Filter Theory", 4th edition, on the Wiener filter (chap. 2): "We now summarize the...

Hello group. Quote from Simon Haykin's book "Adaptive Filter Theory", 4th edition, on the Wiener filter (chap. 2): "We now summarize the essence of the filtering problem with the following statement: Design a linear discrete-time filter whose output y(n) provides an estimate of a desired response d(n), given a set of input samples u(0), u(1), u(2),..., such that the MEAN-SQUARE VALUE ...

## paley-Weiner criterion (causality)

inHello Forum, the Paley-Wiener criterion is the frequency equivalent of the causality condition in the time domain. It states that the...

Hello Forum, the Paley-Wiener criterion is the frequency equivalent of the causality condition in the time domain. It states that the magnitude of the transfer function can be exactly zero only a discrete frequencies but not over a finite band of frequencies... Why not? Is there a more conceptual explanation for that beside looking at the integral and its derivation? Realizable physical...

## speech extraction

inHi.. I was trying to extract speech data from raw data with background traffic. i tried wiener filter, but was not so good. Could you share with...

Hi.. I was trying to extract speech data from raw data with background traffic. i tried wiener filter, but was not so good. Could you share with me your suggestions that will help me to extract speech only from background noise Thank you

## OFDM Channel Estimation Wiener Filtering

Hi all, I am rather new to this and would appreciate if someone would kindly enlighten me on this: My pilots symbols are inserted into a set...

Hi all, I am rather new to this and would appreciate if someone would kindly enlighten me on this: My pilots symbols are inserted into a set of subcarriers (comb type). I've seen the MMSE estimate of the FFT of the channel coefficients given as: H_mmse = R_HHls(R_HlsHls + sigma^2(XX^h)^-1)^-1 Hls where Hls is the channel estimates from the pilot symbols using the least square method. H...

## Periodogram

inI was unaware that the idea was quite so old. Thought maybe Wiener thought of it but i was wrong http://en.wikipedia.org/wiki/Arthur_Schuster...

I was unaware that the idea was quite so old. Thought maybe Wiener thought of it but i was wrong http://en.wikipedia.org/wiki/Arthur_Schuster A German called Arthur Schuster in the 1890s! Schuster is perhaps most widely remembered for his periodogram analysis, a technique which was long the main practical tool for identifying statistically important frequencies present i