RECURSIVE INVERSE ADAPTIVE FILTERING ALGORITHM WITH LOW COMPUTATIONAL COMPLEXITY ON SPARSE SYSTEM IDENTIFICATION - Bercag, Hakan; Kukrer, Osman; Hocanin, Aykut (IEEE, 2018)
This paper studies the performance of Recursive Inverse (RI) adaptive filtering for the identification of sparse systems. A new adaptive algorithm utilizing a modified autocorrelation matrix and a modified weight vector which are both reduced in size, is introduced. This algorithm is called Reduced Complexity Sparse RI (RCS-RI). The low computational complexity is the most significant feature of RCS-RI. Due to the low computational complexity, it performs better by doing faster computations compared with Recursive Inverse (RI) and Zero Attracting Recursive Inverse (ZA-RI) algorithms. Additionally, the convergence of the algorithm is faster compared with the RI algorithm with respect to the steady state Mean Square Error (MSE). The RCS-RI also outperforms the Zero Attracting Variable Step Size Least Mean Square (ZA-VSSLMS) in the steady state Mean Square Deviation (MSD). Its convergence rate and MSD performance in the steady state conditions are approximately equal to that of ZA-RI. Consequently, RCS-RI improves the performance of identifying the sparse system by faster and more efficient computations due to lower complexity and MSE. RCS_RI's steady state MSE is significantly reduced when compared to LMS-type system identification algorithms.
IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) -- DEC 06-08, 2018 -- Louisville, KY
Keywords: Recursive inverse; sparse autocorrelation matrix; system identification