ADAPTIVE NOISE CANCELLATION
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The document discusses the implementation of the stable normalized least mean fourth (NLMF) algorithm for adaptive noise cancellation, highlighting its advantages over traditional least mean squares (LMS) and normalized least mean squares (NLMS) methods. It explains the concept of adaptive filters, their operational structure, and the conditions required for ensuring stability during noise cancellation and system identification. The implementation process involves creating an input signal, initializing coefficients, and iteratively refining the filter output based on error computations.
ADAPTIVE NOISE CANCELLATION
- 1. Stable Normalized Least Mean Fourth Algorithm for Adaptive Noise Cancellation Under the esteemed guidance of G. Amjad Khan sir M.Tech., (Ph.D) Asst Prof., Dept of ECE By 129X1D0918 V Sreenivasa Arun Kumar
- 2. Aim of the Project By using NLMF algorithm implement the noise cancellation and system identification Statistically analyze the conditions where NLMF is superior to NLMS
- 3. Why Adaptive Filter To prevent potential loss of information an adaptive filter could be used The quality of output signal is more accurate Ex: Pilot in an Airplane
- 4. What is Meant by Adaptive Filter An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. The adaptive process involves the use of a cost function, which is a criterion for optimum performance of the filter, to feed an algorithm, which determines how to modify filter transfer function to minimize the cost of the next iteration.
- 5. What are different families of algorithms There are two approaches Stochastic Gradient Least Squares Estimation 1. LMS 1. RLS 2. TV LMS 2. Std. RLS 3. NLMS 3. SQ RLS 4. GAL 4. Fast RLS
- 6. Basic structure of Adaptive Filter Error Section Adaption Section Filtering Section
- 7. LMS LMF
- 8. Why LMF over LMS & NLMS Trade-off between Transient and steady state updation. It is given by the equation w(n+1) = w(n) + µe(n) x(n) If µ is large we may not converge towards optimum solution that is wopt. If µ is small a optimum solution wopt can be achieved, but disadvantage is slow updation.
- 10. WHY DO WE PREFER LMS..! computationally less complexity just order of Filter length(m) Numerically good stability Very simple to implement
- 11. Problem statement while using LMF Stability depends on Variance and Distribution type of adaptive filter input. Stability condition is given by 0 < µ < 2 / λmax Stability depends on Noise Variance Stability depends on initial filter weights
- 12. Stability 1. Numerical Stability Division by zero Singular Matrix in Matrix inversion 2. Convergence Stability Eigen Values of input Eigen spread
- 13. NLMF Algorithm NLMF algorithm equation is given by h(n+1) = h(n) + The Normalizing term is fourth order in regressor and second order in the estimation error
- 14. Implementation of NLMF algorithm for Noise cancellation
- 16. Flow Chart Create input signal Initialize coeff matrix = 0 Compute i/p matrix Compute filter o/p Compute error Compute coeff matrix