Comparative Analysis of LMS, NLMS, and RLS Adaptive Filters in Vehicle Automation Systems under Mixed Noise Conditions

Abstract

In modern vehicle automatic systems, noise interference presents a significant obstacle to the precision and dependability of sensor-based control and communication. This study offers a comparative performance evaluation of three adaptive filtering algorithms—Least Mean Squares (LMS), Normalized LMS (NLMS), and Recursive Least Squares (RLS)—utilized for adaptive noise cancellation (ANC) under mixed noise conditions. A MATLAB-based graphical user interface (GUI) simulation was created to estimate and illustrate the performance of each method across three noise types: Gaussian,  Impulsive and Mixed. The results informed that RLS attained the greatest signal to noise ratio (SNR) enhancement  and with the minimal mean square error (MSE), where as the  NLMS offered a proficient equilibrium between  velocity and computing complexity. This research evaluation that appropriateness of NLMS in real time vehicular control applications and RLS  requiring high accuracy .