LMS and RLS based Adaptive Filter Design for Different Signals
نویسندگان
چکیده
In this paper Adaptive filter is designed and simulated using different algorithms for noise reduction in different signals. The developed filter has been analyzed using Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Recursive Least Squares (RLS) algorithms for sinusoidal, chirp and saw-tooth signals. The performance of developed filter has been compared interms of Rate of Convergence and Minimum Mean Square Error (MMSE). The models for all algorithms are developed and simulated using MATLABSIMULINK. The simulated results show that RLS algorithm based filter provides better convergence rate at the cost of degraded MMSE as compared to LMS and NLMS. It can also be observed from the results that noise cancellation is better in saw-tooth signal as compared to sinusoidal and chirp signals.
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