TAILIEUCHUNG - Advanced DSP and Noise reduction P6

WIENER FILTERS Wiener Filters: Least Square Error Estimation Block-Data Formulation of the Wiener Filter Interpretation of Wiener Filters as Projection in Vector Space Analysis of the Least Mean Square Error Signal Formulation of Wiener Filters in the Frequency Domain Some Applications of Wiener Filters The Choice of Wiener Filter Order Summary W iener theory, formulated by Norbert Wiener, forms the foundation of data-dependent linear least square error filters. Wiener filters play a central role in a wide range of applications such as linear prediction, echo cancellation, signal restoration, channel equalisation and system identification. The coefficients. | Advanced Digital Signal Processing and Noise Reduction Second Edition. Saeed V. Vaseghi Copyright 2000 John Wiley Sons Ltd ISBNs 0-471-62692-9 Hardback 0-470-84162-1 Electronic 6 WIENER FILTERS Wiener Filters Least Square Error Estimation Block-Data Formulation of the Wiener Filter Interpretation of Wiener Filters as Projection in Vector Space Analysis of the Least Mean Square Error Signal Formulation of Wiener Filters in the Frequency Domain Some Applications of Wiener Filters The Choice of Wiener Filter Order Summary Wiener theory formulated by Norbert Wiener forms the foundation of data-dependent linear least square error filters. Wiener filters play a central role in a wide range of applications such as linear prediction echo cancellation signal restoration channel equalisation and system identification. The coefficients of a Wiener filter are calculated to minimise the average squared distance between the filter output and a desired signal. In its basic form the Wiener theory assumes that the signals are stationary processes. However if the filter coefficients are periodically recalculated for every block of N signal samples then the filter adapts itself to the average characteristics of the signals within the blocks and becomes block-adaptive. A block-adaptive or segment adaptive filter can be used for signals such as speech and image that may be considered almost stationary over a relatively small block of samples. In this chapter we study Wiener filter theory and consider alternative methods of formulation of the Wiener filter problem. We consider the application of Wiener filters in channel equalisation time-delay estimation and additive noise reduction. A case study of the frequency response of a Wiener filter for additive noise reduction provides useful insight into the operation of the filter. We also deal with some implementation issues of Wiener filters. Least Square Error Estimation 179 Wiener Filters Least Square

TÀI LIỆU LIÊN QUAN
31    426    56
TỪ KHÓA LIÊN QUAN
TAILIEUCHUNG - Chia sẻ tài liệu không giới hạn
Địa chỉ : 444 Hoang Hoa Tham, Hanoi, Viet Nam
Website : tailieuchung.com
Email : tailieuchung20@gmail.com
Tailieuchung.com là thư viện tài liệu trực tuyến, nơi chia sẽ trao đổi hàng triệu tài liệu như luận văn đồ án, sách, giáo trình, đề thi.
Chúng tôi không chịu trách nhiệm liên quan đến các vấn đề bản quyền nội dung tài liệu được thành viên tự nguyện đăng tải lên, nếu phát hiện thấy tài liệu xấu hoặc tài liệu có bản quyền xin hãy email cho chúng tôi.
Đã phát hiện trình chặn quảng cáo AdBlock
Trang web này phụ thuộc vào doanh thu từ số lần hiển thị quảng cáo để tồn tại. Vui lòng tắt trình chặn quảng cáo của bạn hoặc tạm dừng tính năng chặn quảng cáo cho trang web này.