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Noise Cancellation in Monte Carlo Simulation

Affiliations

  • Department of ECE, Vignan’s Institute of Information Technology, Beside VSEZ, Duvvada, Gajuwaka, Visakhapatnam - 530046, Andhra Pradesh, India
  • Department of ECE, K L University, Green Fields, Vaddeswaram, Guntur District - 522502, Andhra Pradesh, India
  • Department of CSE, Vignan’s Institute of Information Technology, Beside VSEZ, Duvvada, Gajuwaka, Visakhapatnam - 530046, Andhra Pradesh, India

Abstract


For processing signals and in control application filters are essential, linear optimums discrete time filters such as wiener filter and Kalman filter are on orthogonal principle. For non stationary cases of having a presence of noise, adaptive wiener filter has to be applied using Monte Carlo Simulation 250 samples were used for 50 runs. Coefficients of linear filter are used to estimate the additive white noise. Error is calculated and RMS value of each error is added to the sample for desired signal. FIR wiener filters of order 6, 12, 24 were chosen for adaptive operators .Simulation results were quite encourage in the sense that noise was suppressed to maximum extends. Adaptive methods noisy higher number of samples and more than 100 runs are linear to yield better results.

Keywords

Noise Cancellation, Monte Carlo Simulation, Wiener Filter, Optimal Filter, Wiener-Hopf Equations, Wide-Sense Stationary Random Processes, Discrete Wiener Filter, Discrete Kalman Filter.

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