Bhattacharjee, Sankha SubhraSankha SubhraBhattacharjeeGeorge, Nithin V.Nithin V.George2025-08-312025-08-312020-05-01[9781509066315]10.1109/ICASSP40776.2020.90534212-s2.0-85089209304https://d8.irins.org/handle/IITG2025/24160Recently, nearest Kronecker product (NKP) decomposition based Wiener filter and Recursive Least Squares (RLS) have been proposed and was found to be a good candidate for system identification and echo cancellation and was shown to offer better tracking performance along with lower computational complexity, especially for identification of low-rank systems. In this paper, we derive the Least Mean Square (LMS) versions of adaptive algorithms which take advantage of NKP decomposition, namely NKP-LMS and NKP Normalized LMS (NKP-NLMS) algorithms. We compare the convergence and tracking performance along with computational complexity between standard NLMS, standard RLS, NKP based RLS (RLS-NKP), the standard Affine Projection Algorithm (APA) and NKP-NLMS algorithm, to evaluate the efficacy of NKP-NLMS algorithm in the context of system identification. Simulation results show that NKP-NLMS can be a good candidate for system identification, especially for sparse/low rank systems.falseAdaptive filter | Least mean square | Low rank approximation | nearest Kronecker product | System identificationConference Paper476-480May 2020329053421cpConference Proceeding26