An extended Kalman filter based fuzzy adaptive equalizer for powerline channel

Fuzzy logic is the principles of imprecise knowledge. Fuzzy adaptive equalizers are adaptive equalizers that apply the concepts of fuzzy logic. The main merit of applying fuzzy adaptive equalizers in powerline channel equalization is that linguistic information (fuzzy IF-THEN rules) and numerical information (input-output pairs) can be combined into the equalizers. The adaptive algorithms adjust the parameters of the membership functions, which characterize the fuzzy concepts in the IF-THEN rules, by minimizing some criterion function. In this paper, we introduce a new type of fuzzy adaptive equalizer using extended Kalman filter (EKF) algorithm for powerline channel equalization. The performance for this type of fuzzy adaptive equalizer is compared with two other types of fuzzy adaptive equalizers using recursive least squares (RLS) and least mean squares (LMS) adaption algorithm. The simulation results show that extended Kalman filter based fuzzy adaptive equalizer has faster convergent speed compared to the other two fuzzy adaptive equalizers. The bit error rate of extended Kalman filter based fuzzy adaptive equalizer is close to that of the optimal equalizer.