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A Weighted Least Squares Algorithm for Quasi-Equiripple FIR and IIR Digit
al Filter Design
Advisor : 高永安Student : 陳志煒
outline
• Introduction• The Weighted Least Squares Method• The Least Squares Weighting Function• The New Algorithm• Termination of Algorithm• Conclusion
Introduction
• In this paper, a novel iterative algorithm for deriving the least squares frequency response weighting function which will produce a quasi-equiripple design is presented
• The algorithm converges very rapidly• Form experience, about 1dB away form the mini
max optimum solution in two iterations and converges to within 0.1dB in six iterations
The Weighted Least Squares Method
• appropriate trigonometrical function
• impulse response of the filter
• desired frequency response
• actual frequency response
The Weighted Least Squares Method
The Weighted Least Squares Method
• Minimized
• Optimum solution
The Least Squares Weighting Function
• The weighting function used in the (k+1) iteration
• Lawson’s Algorithm
The New Algorithm
• Define the i th extremal point of the k th iteration as
• The envelope function
The New Algorithm
• Minimax weighting function
• The constant affect convergent speed
• = average of
The Constant
• The algorithm is said to have converged if
• Peak weighted ripple of optimum minimax design
• Peak weighted ripple of the weighted least squares design
• There is no know analytical method for obtaining that will make the algorithm converge at the maximum speed
The Constant
• Averaging the results of 200 examples
Initial
• May be set equal to unity for all n
• Using the method of rectangular windowing of the Fourier series of
Termination of Algorithm
• It can be terminated after a prespecified number of iterations have been completed
• Another criterion for termination of the algorithm is to check for quasi-equiripple condition
• : average weighted ripple magnitude
Conclusion
• We have present a novel fast convergent weighted least squares algorithm for quasi-equiripple FIR and IIR fil
ter designs
• N=151• =1.5
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