Download PDF by A. McCormick, A. K. Nandi (auth.), Asoke Kumar Nandi (eds.): Blind Estimation Using Higher-Order Statistics

By A. McCormick, A. K. Nandi (auth.), Asoke Kumar Nandi (eds.)

ISBN-10: 1441950788

ISBN-13: 9781441950789

ISBN-10: 1475729855

ISBN-13: 9781475729856

In the signal-processing study neighborhood, loads of development in higher-order information (HOS) all started within the mid-1980s. those final fifteen years have witnessed loads of theoretical advancements in addition to actual purposes. Blind Estimation UsingHigher-Order Statistics makes a speciality of the blind estimation region and files a number of the significant advancements during this box.
Blind Estimation utilizing Higher-Order Statistics is a welcome boost to the few books just about HOS and is the 1st significant e-book dedicated to masking blind estimation utilizing HOS. The ebook offers the reader with an advent to HOS and is going directly to illustrate its use in blind sign equalisation (which has many functions together with (mobile) communications), blind approach id, and blind assets separation (a frequent challenge in sign processing with many purposes together with radar, sonar and communications). there's additionally a bankruptcy dedicated to strong cumulant estimation, a huge challenge the place HOS effects were encouraging.
Blind Estimation utilizing Higher-Order Statistics is a useful reference for researchers, execs and graduate scholars operating in sign processing and similar areas.

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Extra resources for Blind Estimation Using Higher-Order Statistics

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Such approaches has been attempted with nonlinear filters like Volterra models and neural network architectures. The decision feedback equaliser (DFE) is another nonlinear structure that is well known from non-blind equalisation. 4. The main disadvantage is that the the feedback path propagates decision errors, which creates problems in start-up mode. However, a study of these structures is outside the scope of our text, as we shall confine ourselves to linear equalisers. 2. 4: Structure of decision feedback equaliser.

22) For complex baseband channels, as we have generally assumed our systems to be, real and imaginary part of the signal are processed separately by the nonlinearity. 23) where x re ( n) then used in rameters are with respect and Xim( n) denote real and imaginary part. The error signal is the gradient descent algorithm. At each iteration, the filter pachanged in the direction of the negative gradient. The gradient to the equaliser filter w is defined as \7 w J (n) = 8J{n) [ 8wo{n) ... 24) where J(n) is the cost function that we want to minimise.

It decides which of the symbols in the finite alphabet of the discrete data source is closest to x(n). The result is an estimate x(n) that equals x(n) when the decision error rate is zero (eye is open) x(n) = Q(x(n)) = Q(h(n) * w(n) * x(n)) . ) symbols. The only constraint on the input pdf is that it must be non-Gaussian. This is because the equalisation will rely on higher-order statistics. These are shown to be identically zero, and therefore not useful , for Gaussian signals. This model will be used as a basis for the approaches described in subsequent sections.

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Blind Estimation Using Higher-Order Statistics by A. McCormick, A. K. Nandi (auth.), Asoke Kumar Nandi (eds.)


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