"基于LMS的自适应滤波器设计与Matlab实现及性能比较"

The document "LMS Filter Design and Matlab Implementation" provides a comprehensive analysis of traditional filtering and adaptive filtering techniques, focusing on the characteristics of random noise. It discusses the basic principles and performance of both types of filters, as well as the current status and future prospects of filtering technology. The paper systematically explains the basic Wiener filtering principle and the basic structural model of adaptive filters, leading to the introduction of the Least Mean Squares (LMS) algorithm based on the steepest descent method. Under the Mean Squared Error (MSE) criterion, a fixed-length adaptive minimum mean square lateral filter is designed and implemented through MATLAB programming. The performance of the filter is then verified using image restoration, demonstrating a noticeable improvement in image quality under the MSE criterion. Finally, a comparison is made between the performance of adaptive LMS filtering and frequency domain Wiener recursive filtering. The paper also provides a brief analysis of the adaptive Wiener filtering function wiener2 in MATLAB. Overall, this study contributes to the understanding of filter design and implementation techniques, highlighting the effectiveness of adaptive LMS filtering in improving image quality. It also sheds light on the potential applications and benefits of filtering technology in various fields. Key words: degraded images, Wiener filtering, adaptive filtering, steepest descent method, LMS.