DSP for MATLAB and LabVIEW IV: LMS Adaptive Filtering by Forester W. Isen

By Forester W. Isen

This booklet is quantity IV of the sequence DSP for MATLABв„ў and LabVIEWв„ў. quantity IV is an introductory remedy of LMS Adaptive Filtering and purposes, and covers fee services, functionality surfaces, coefficient perturbation to estimate the gradient, the LMS set of rules, reaction of the LMS set of rules to narrow-band indications, and numerous topologies similar to ANC (Active Noise Cancelling) or process modeling, Noise Cancellation, Interference Cancellation, Echo Cancellation (with unmarried- and dual-H topologies), and Inverse Filtering/Deconvolution. the complete sequence includes 4 volumes that jointly conceal easy electronic sign processing in a realistic and obtainable demeanour, yet which still contain all crucial origin arithmetic. because the sequence name implies, the scripts (of which there are greater than 2 hundred) defined within the textual content and provided in code shape (available through the web at www.morganclaypool.com/page/isen) will run on either MATLABв„ў and LabVIEWв„ў. desk of Contents: creation To LMS Adaptive Filtering / utilized Adaptive Filtering

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2nd through N−th Coeff Est. MSE First Coeff Est. 9: Performance Surface with plot of successive coefficient estimates utilizing a weighting function to accelerate the slower-converging of the two coefficients. Note that convergence is extremely rapid since the value of μ for the y-intercept variable has been adjusted to greatly speed the convergence of that variable toward its value of best adjustment. 8. AN IMPROVED GRADIENT SEARCH METHOD 19 Using the river analogy, we can see that instead of moving the way gravity would dictate, which would be along a nearly direct path toward the river (with only a slight bias downstream), and then suddenly turning to head directly downstream, the algorithm immediately headed directly for the point of minimized cost function, (the river mouth in our analogy).

C2Strt, N oI ts, tstDT ype, CosF rq) embodies such an algorithm, where P C1 and P C2 are the Plant coefficients to be modeled, c1Strt and c2Strt are the initial guesses or starting values for the coefficient estimates, NoI ts is the number of iterations to perform, Mu has the usual meaning, and tstDType selects the type of test signal. 11. LMS ADAPTIVE FIR SUMMARY 37 tstDType as 0 for white noise, 1 for a unit step (DC), 2 for the Nyquist Limit frequency, 3 for the half-band frequency, and 4 for a cosine of frequency CosFrq (may be passed as [] if a cosine is not being used as a test signal).

26) is to divide by a factor related to the signal power in the filter, to which is added a small number in case the signal power in the filter should be zero. We thus get ci [n + 1] = ci [n] + xi [n]2 + · Err[n] · xi [n] (where = 2μ) and refer to this as the Normalized Least Mean Square, or NLMS, algorithm. Note that in Eq. 27), the power is averaged over the entire input signal. In the NLMS method, only signal values in the filter are taken into account. The NLMS is thus more adaptive to current signal conditions.

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