a) State the principle of statistical orthogonality, defining any terms you use. Show how it can...

 

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a) State the principle of statistical orthogonality, defining any terms you use. Show how it can be used to derive the Wiener finite impulse response (FIR) filter. b) Explain the differences between the least mean squares (LMS) and recursive least squares (RLS) adaptive filter algorithms in terms of their computational complexity and performance. Explain the difference between the minimum variance spectral estimation algorithm and the amplitude and phase estimation (APES) algorithm in terms of how they are derived. Define any terms that you use. There is no need to derive algorithms. c) In a practical situation what is a simple method for calculating a value for the step size u associated with a least mean square (LMS) adaptive filter d) algorithm? Define any terms that you use. Explain why this method is useful in practice. e) What is a whitened matched filter? Explain briefly how it is used. a) State the principle of statistical orthogonality, defining any terms you use. Show how it can be used to derive the Wiener finite impulse response (FIR) filter. b) Explain the differences between the least mean squares (LMS) and recursive least squares (RLS) adaptive filter algorithms in terms of their computational complexity and performance. Explain the difference between the minimum variance spectral estimation algorithm and the amplitude and phase estimation (APES) algorithm in terms of how they are derived. Define any terms that you use. There is no need to derive algorithms. c) In a practical situation what is a simple method for calculating a value for the step size u associated with a least mean square (LMS) adaptive filter d) algorithm? Define any terms that you use. Explain why this method is useful in practice. e) What is a whitened matched filter? Explain briefly how it is used.

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A Orthogonality between two vectors does not mean they are independent the concept of independence is really large eg it can be related to the independence between two random variables It does mean th... View the full answer