DSP by CF2084S

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									   Subspace SNR Maximization: The Constrained
           Stochastic Matched Filter

ABSTRACT:
In this paper, we propose a novel approach to perform detection of stochastic signals embedded
in an additive random noise. Both signal and noise are considered to be realizations of zero mean
random processes of which only second-order statistics are known (their covariance matrices).
The method proposed, called constrained stochastic matched filter (CSMF), is an extension of
the stochastic matched filter itself derived from the matched filter. The CSMF is optimal in the
sense that it maximizes the signal-to-noise ratio in a subspace whose dimension is fixed a priori.
In this paper, after giving the reasons for our approach, we show that there is neither an obvious
nor analytic solution to the problem expressed. Then an algorithm, which is proved to converge,
is proposed to obtain the optimal solution. Evaluation of the algorithm’s performance is
completed through estimation of receiver operating characteristic curves. Experiments on real
signals show the improvement brought about by this method and thus its significance.


BLOCK DIAGRAM:




        Stochastic
        signal                                              Maximizes the
                                Constrained
        Embedded in                                         signal-to-noise
                                stochastic                  ratio in a subspace
        additive                matched filter
        random noise            (CSMF)


                                                         Evaluation of the
                                                         algorithm’s performance
                                                         Through ROC
EXISTING SYSTEM:


The Karhunen–Loève transform (KLT) is a principal component analysis used to tackle this
model when noise is white or absent; it provides the best approximation, in the sense that it
minimizes a mean-square error (MSE) for a stochastic signal under the condition that its rank is
fixed and is used, for example, for data compression or filtering. When noise is white, it
determines the -dimension subspace where the SNR is maximum.


DISADVANTAGES:
      It does not consider colored noise, and therefore is not optimum even when it is used with
       a noise suppression filter such as the Wiener filter (which is not a reduced-rank method).


PROPOSED SYSTEM:
we propose a novel approach to perform detection of stochastic signals embedded in an additive
random noise. Both signal and noise are considered to be realizations of zero mean random
processes of which only second-order statistics are known (their covariance matrices). The
method proposed, called constrained stochastic matched filter (CSMF), is an extension of the
stochastic matched filter itself derived from the matched filter. The CSMF is optimal in the sense
that it maximizes the signal-to-noise ratio in a subspace whose dimension is fixed a priori.


ADVANTAGES:


      Evaluation of the algorithm’s performance is completed through estimation of receiver
       operating characteristic curves. Experiments on real signals show the improvement
       brought about by this method and thus its significance.
DOMAIN


       DSP, or Digital Signal Processing, as the term suggests, is the processing of signals by
digital means. A signal in this context can mean a number of different things. Historically the
origins of signal processing are in electrical engineering, and a signal here means an electrical
signal carried by a wire or telephone line, or perhaps by a radio wave. More generally, however,
a signal is a stream of information representing anything from stock prices to data from a remote-
sensing satellite. The term "digital" comes from "digit", meaning a number. The processing of a
digital signal is done by performing numerical calculations.


SOFTWARE REQUIREMENT


Matlab 7.0 And Above


MATLAB is a high-performance language for technical computing. It integrates computation,
visualization, and programming in an easy-to-use environment where problems and solutions are
expressed in familiar mathematical notation. Typical uses include:
      Math and computation
      Algorithm development
      Modeling, simulation, and prototyping
      Data analysis, exploration, and visualization
      Scientific and engineering graphics
      Application development, including Graphical User Interface building
MATLAB is an interactive system whose basic data element is an array that does not require
dimensioning. This allows you to solve many technical computing problems, especially those
with matrix and vector formulations, in a fraction of the time it would take to write a program in
a scalar non-interactive language such as C or FORTRAN.

								
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