Error whitening criterion for adaptive filtering: theory and algorithms

Error Whitening Wiener Filters: Theory and Algorithms

Error Whitening Criterion For Linear Filter Estimation

Mean Squared Error (MSE) has been the most widely used tool to solve the linear filter estimation or system identification problem. However, MSE gives biased results when the input signals are noisy. This paper presents a novel Error Whitening Criterion (EWC) to tackle the problem of linear system identification in the presence of additive white disturbances. We will motivate the theory behind ...

ADAPTIVE FILTERING WITH ADAPTIVE p-POWER ERROR CRITERION

In a previous paper, we have proposed a novel approach to choose an optimal p-power error criterion for adaptive filtering, by minimizing a Kullback-Leibler information divergence (KL-divergence). However, the method requires exact knowledge of the noise PDF, which reduces its practicality since in real life scenario the noise distribution is usually unknown. In the present paper, we propose a ...

Smoothed least mean p-power error criterion for adaptive filtering

1. Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, China 2. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510640, China 3. School of Automation & Information Engineering, Xi'an University of Technology, Xi'an 710048, China 4. Department of Electrical and Computer Engineering, University of Florida,...

Adaptive Linear Filtering Design with Minimum Symbol Error Probability Criterion

Adaptive digital filtering has traditionally been developed based on the minimum mean square error (MMSE) criterion and has found ever-increasing applications in communications. This paper presents an alternative adaptive filtering design based on the minimum symbol error rate (MSER) criterion for communication applications. It is shown that the MSER filtering is smarter, as it exploits the non...