Chapter 10 - Adaptive Filters and Applications精品.pdfVIP

CHAPTER Adaptive Filters and Applications 10 CHAPTER OUTLINE 10.1 Introduction to Least Mean Square Adaptive Finite Impulse Response Filters 453 10.2 Basic Wiener Filter Theory and Least Mean Square Algorithm 457 10.3 Applications: Noise Cancellation, System Modeling, and Line Enhancement 462 10.3.1 Noise Cancellation462 10.3.2 System Modeling468 10.3.3 Line Enhancement Using Linear Prediction473 10.4 Other Application Examples 476 10.4.1 Canceling Periodic Interferences Using Linear Prediction476 10.4.2 Electrocardiography Interference Cancellation476 10.4.3 Echo Cancellation in Long-Distance Telephone Circuits 479 10.5 Laboratory Examples Using the TMS320C6713 DSK 480 10.6 Summary 485 OBJECTIVES This chapter introduces principles of adaptive filters and the adaptive least mean square algorithm and illustrates how to apply the adaptive filters to solve the real-world application problems such as adaptive noise cancellation, system modeling, adaptive line enhancement, and telephone echo cancellation. 10.1 INTRODUCTION TO LEAST MEAN SQUARE ADAPTIVE FINITE IMPULSE RESPONSE FILTERS An adaptive filter is a digital filter that has self-adjusting characteristics. It is capable of adjusting its filter coefficients automatically to adapt the input signal via an adaptive algorithm. Adaptive filters play an important role in modern digital signal processing (DSP) products in areas such as telephone echo cancellation, noise cancellation, equalization of communications channels, biomedical signal enhancement, active noise control, and adaptive control systems. Adaptive filters work generally for adaptation of signal-changing environments, spectral overlap between noise and signal, and unknown or time-varying noise. For example, when the interference noise is strong and its spectrum overlaps