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Recursive inverse adaptive filtering algorithm

Recursive inverse adaptive filtering algorithm,10.1016/j.dsp.2011.03.001,Digital Signal Processing,Mohammad Shukri Ahmad,Osman Kukrer,Aykut Hocanin

Recursive inverse adaptive filtering algorithm   (Citations: 3)
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In this paper, a new FIR adaptive filtering algorithm is proposed. The approach uses a variable step-size and the instantaneous value of the autocorrelation matrix in the coefficient update equation that leads to an improved performance. Convergence analysis of the algorithm has been presented. Simulation results show that the algorithm performs better than the Transform Domain LMS with Variable Step-Size (TDVSS) in stationary Additive White Gaussian Noise (AWGN) and Additive Correlated Gaussian Noise (ACGN) environments in a system identification setting. It is shown that the algorithm has a performance better than RLS and very similar to RRLS algorithm with a considerable reduction in computational complexity. Additionally, the performance of the proposed algorithm is shown to be superior to that of the Stabilized Fast Transversal Recursive Least Squares (SFTRLS) algorithm under the same conditions.
Journal: Digital Signal Processing , vol. 21, no. 4, pp. 491-496, 2011
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    • ...In [9], the convergence analysis, in the mean and the mean square sense, of the RI algorithm has been presented in detail...
    • ...It was shown in [9] that Eq. (17) can be approximately simplified to...

    Mohammad Shukri Ahmadet al. A 2-D recursive inverse adaptive algorithm

    • ...The RI algorithm [1, 2], was recently proposed to overcome these problems...
    • ...In this paper, we propose a new RI algorithm that is robust to impulsive noise and provides better MSE performance than the recently proposed Robust RLS algorithm [4], with a considerable reduction in the computational complexity [1, 2 ]a s shown in Table1...
    • ...In the recently proposed RI algorithm [1, 2], the coefficient-vector update equation is given by...
    • ...where w(k) is the tap-weight vector of length N , I is an N × N identity matrix, R(k) is an N × N estimate of the tap-input vector autocorrelation matrix, p(k) is the estimate of the cross-correlation vector between the desired output signal and the tap-input vector of length N , and μ(k) is the variable step-size given by [2]...

    Mohammad Shukri Ahmadet al. Robust recursive inverse adaptive algorithm in impulsive noise

    • ...where w(k) is the recursive estimate of w given by [5], [7]:...
    • ...where μ(k) is the variable step-size [5], [7] defined as:...
    • ...(19) where δ w(k) [7] is given by δ w(k )=[ I − μ0Rxx] δ w(k−1)+μ(k)...

    Mohammad Shukri Ahmadet al. The effect of the forgetting factor on the RI adaptive algorithm in sy...

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