is a set of methods aimed to solve the problem of recovering (reconstructing) precise version of a distorted (transformed) signal, where the distortion (transform) matrix (kernel) or the Point Spread Function (PSF) is unknown.

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Matlab: How to apply constant modulus algorithm in wireless communication

I am trying to apply the Constant Modulus Algorithm which is a blind equalization algorithm in CDMA communication. I am following the code for CMA and a great explanation given in link Matlab : Proper ...
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38 views

Quantitative comparison of scaled-, delayed- and warped-signals

In several signal processing techniques, such as blind source separation (BSS) or deconvolution, one may wish to estimate a signal $x(t)$ and recovers $s.x(t+d)$, where $s$ is a scale factor, $d$ a ...
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Performance of system identification with nonlinear input

Paper : System Identification using Symbolic Chaotic Sequence download link proposes EM-UKS estimator for system identification of a linear FIR channel when excited by non-linear input. In Fig 3 of ...
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148 views

Constant Modulus Algorithm and the gradient operation

CMA is a blind channel equalization algorithm with the details presented above. I am facing 3 difficulties and shall appreciate help Q1: Does $H$ and the bar over $\bar{y_k}$ represent the ...
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35 views

Extract a square-shaped signal which lies in a specific range

I need to extract (estimate) a square-shaped signal from the main signal. This square-shaped signal should be located within a specific range, in fact all we have to do is to estimate its height. A ...
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Need suggestions on the class of blind adaptive filters that can be used in my situation

I have 2 satellite transponders closely situated, transmitting the same signal. At the receiver I receive this composite signal. I have to equalize it in such a way that the demodulator sees only one ...
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150 views

How to deconvolve dependent part of signal from independent part?

I have a problem of the following form. There are two signals, x(t) and y(t). The model for the system is such that: $$x(t) = x'(t) + f(y(t))$$ where $f(y(t))$ is a variable offset introduced by ...
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634 views

Differences using Maximum Likelihood or Maximum a Posteriori for deconvolution/deblur?

Are there any differences if you use Maximum Likelihood or Maximum a Posteriori to estimate the Point Spread Function for image deconvolution?
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385 views

Is there a way to reduce the covariance matrix of several source signals to the dominant source signal?

The problem I have can be seen in the context of DoA estimation or blind source signal separation and similar fields, where several source signals are observed by several antennas (or by an antenna ...
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550 views

Deblurring algorithm to precede thresholding - speed over accuracy

I'm writing an app that recognizes Sudoku puzzles from a camera input. I'd like to remove camera blur from the images to improve recognition. Here is an example image: Since I'm processing a ...
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179 views

How to select Point Spread Function empirically for image deconvolution?

When the captured image is blurring, one way of obtaining a clear image is via image deconvolution technique. In order to perform deconvolution successfully, usually we need to pay attention to the ...
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blur PSF modeling for small stright camera movement

I am interested in reovering images affected by blur of known orientation and known span. Camera movement during capture is very small, blur span is of about 0-4px. What is the most accurate way to ...
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Blind 1D equalization/deconvolution with some knowledge of filter kernel

Let $s_{\rm out}[n]$ be the 1D output signal of a system, $s[n]$ be the input, and $k[n,q]$ be the filter kernel for an element $n$ and for fixed value $q$. Then: $s_{\rm out}[n] = s[n] \ast k[n,q]$ ...