This paper presents a new spline adaptive filtering (SAF) algorithm based on signed regressor (SR) of input signal. The algorithm is called SR-SAF normalized least mean squares (SR-SAF-NLMS). The SR-SAF-NLMS is established through $L_{1}$-norm constraint to the proposed cost function. In this algorithm, the polarity of the input signal is used to adjust the weight coefficients and control point vectors. Therefore, the computational complexity, especially the number of multiplications, is significantly reduced. Furthermore, the performance of the SR-SAF-NLMS is close to the conventional SAF-NLMS. The good performance of the proposed algorithm is demonstrated through several simulation results in different scenarios.
Tavakoli, H. and Shams Esfand Abadi, M. (2023). Spline NLMS Adaptive Filter Algorithm based on the Signed Regressor of Input Signal. Scientia Iranica, (), -. doi: 10.24200/sci.2023.62258.7738
MLA
Tavakoli, H. , and Shams Esfand Abadi, M. . "Spline NLMS Adaptive Filter Algorithm based on the Signed Regressor of Input Signal", Scientia Iranica, , , 2023, -. doi: 10.24200/sci.2023.62258.7738
HARVARD
Tavakoli, H., Shams Esfand Abadi, M. (2023). 'Spline NLMS Adaptive Filter Algorithm based on the Signed Regressor of Input Signal', Scientia Iranica, (), pp. -. doi: 10.24200/sci.2023.62258.7738
CHICAGO
H. Tavakoli and M. Shams Esfand Abadi, "Spline NLMS Adaptive Filter Algorithm based on the Signed Regressor of Input Signal," Scientia Iranica, (2023): -, doi: 10.24200/sci.2023.62258.7738
VANCOUVER
Tavakoli, H., Shams Esfand Abadi, M. Spline NLMS Adaptive Filter Algorithm based on the Signed Regressor of Input Signal. Scientia Iranica, 2023; (): -. doi: 10.24200/sci.2023.62258.7738
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