第05章:线性系统在数字信号处理中的基石

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在"The Scientist and Engineer's Guide to Digital Signal Processing"第五章中,讨论了线性系统在数字信号处理(DSP)中的核心地位。这一章节是理解 DSP 技术的基础,因为大多数 DSP 方法都依赖于称为“叠加”的分而治之策略。叠加原理允许复杂信号被分解成简单的组成部分,每个部分独立处理后再合并结果,这样就把一个难题转化为多个易于管理的小问题。 线性系统是指遵循特定数学规则的系统,它们的行为满足加法和比例法则,即输入信号的线性组合会产生与之对应的输出的线性组合。在工程和科学研究中,许多实际应用都符合线性系统的特性,因此线性系统理论在信号处理领域具有广泛的应用价值。 信号和系统是紧密相关的概念。信号可以看作是一个参数随另一个参数变化的描述,如电子电路中电压随时间的变化,或者图像中亮度随距离的变化。系统则是对输入信号进行处理并产生相应输出的设备或过程。例如,图5-1中的块图展示了输入信号(比如声音或图像)如何通过系统转化为输出信号(比如音频播放或图像处理后的输出图像)。 在本章中,首先定义了什么是线性系统,包括其基本性质。接着,探讨了如何利用线性系统的特性来分析和设计信号处理器。一种常用的方法是将复杂的连续信号分解为简单的波形,如正弦或余弦函数,通过傅里叶变换等技术实现频域处理。通过这样的分解,可以有效地滤波、放大、延迟或压缩信号,这些都是 DSP 中广泛应用的信号处理技术。 此外,线性系统还涉及系统函数和系统矩阵的概念,它们是描述系统行为的重要工具。系统函数表示输入信号到输出信号的映射关系,而系统矩阵则用于表示线性系统在复数域中的行为。通过研究这些概念,工程师能够更好地理解线性系统如何处理不同类型的输入,并设计出适应特定需求的信号处理算法。 第五章深入浅出地介绍了线性系统在 DSP 中的重要性,不仅涵盖了线性系统的基本概念,还涵盖了信号分解、叠加原理以及各种信号处理技术的应用。理解这一章的内容对于任何从事 DSP 工程的人员来说都是至关重要的,因为它为后续的滤波器设计、调制解调、频谱分析等高级 DSP 技术打下了坚实的基础。
2010-12-29 上传
http://www.dspguide.com/pdfbook.htm FOUNDATIONS Chapter 1 - The Breadth and Depth of DSP The Roots of DSP Telecommunications Audio Processing Echo Location Image Processing Chapter 2 - Statistics, Probability and Noise Signal and Graph Terminology Mean and Standard Deviation Signal vs. Underlying Process The Histogram, Pmf and Pdf The Normal Distribution Digital Noise Generation Precision and Accuracy Chapter 3 - ADC and DAC Quantization The Sampling Theorem Digital-to-Analog Conversion Analog Filters for Data Conversion Selecting The Antialias Filter Multirate Data Conversion Single Bit Data Conversion Chapter 4 - DSP Software Computer Numbers Fixed Point (Integers) Floating Point (Real Numbers) Number Precision Execution Speed: Program Language Execution Speed: Hardware Execution Speed: Programming Tips FUNDAMENTALS Chapter 5 - Linear Systems Signals and Systems Requirements for Linearity Static Linearity and Sinusoidal Fidelity Examples of Linear and Nonlinear Systems Special Properties of Linearity Superposition: the Foundation of DSP Common Decompositions Alternatives to Linearity Chapter 6 - Convolution The Delta Function and Impulse Response Convolution The Input Side Algorithm The Output Side Algorithm The Sum of Weighted Inputs Chapter 7 - Properties of Convolution Common Impulse Responses Mathematical Properties Correlation Speed Chapter 8 - The Discrete Fourier Transform The Family of Fourier Transform Notation and Format of the Real DFT The Frequency Domain's Independent Variable DFT Basis Functions Synthesis, Calculating the Inverse DFT Analysis, Calculating the DFT Duality Polar Notation Polar Nuisances Chapter 9 - Applications of the DFT Spectral Analysis of Signals Frequency Response of Systems Convolution via the Frequency Domain Chapter 10 - Fourier Transform Properties Linearity of the Fourier Transform Characteristics of the Phase Periodic Nature of the DFT Compression and Expansion, Multirate methods Multiplying Signals (Amplitude Modulation) The Discrete Time Fourier Transform Parseval's Relation Chapter 11 - Fourier Transform Pairs Delta Function Pairs The Sinc Function Other Transform Pairs Gibbs Effect Harmonics Chirp Signals Chapter 12 - The Fast Fourier Transform Real DFT Using the Complex DFT How the FFT works FFT Programs Speed and Precision Comparisons Further Speed Increases Chapter 13 - Continuous Signal Processing The Delta Function Convolution The Fourier Transform The Fourier Series DIGITAL FILTERS Chapter 14 - Introduction to Digital Filters Filter Basics How Information is Represented in Signals Time Domain Parameters Frequency Domain Parameters High-Pass, Band-Pass and Band-Reject Filters Filter Classification Chapter 15 - Moving Average Filters Implementation by Convolution Noise Reduction vs. Step Response Frequency Response Relatives of the Moving Average Filter Recursive Implementation Chapter 16 - Windowed-Sinc Filters Strategy of the Windowed-Sinc Designing the Filter Examples of Windowed-Sinc Filters Pushing it to the Limit Chapter 17 - Custom Filters Arbitrary Frequency Response Deconvolution Optimal Filters Chapter 18 - FFT Convolution The Overlap-Add Method FFT Convolution Speed Improvements Chapter 19 - Recursive Filters The Recursive Method Single Pole Recursive Filters Narrow-band Filters Phase Response Using Integers Chapter 20 - Chebyshev Filters The Chebyshev and Butterworth Responses Designing the Filter Step Response Overshoot Stability Chapter 21 - Filter Comparison Match #1: Analog vs. Digital Filters Match #2: Windowed-Sinc vs. Chebyshev Match #3: Moving Average vs. Single Pole APPLICATIONS Chapter 22 - Audio Processing Human Hearing Timbre Sound Quality vs. Data Rate High Fidelity Audio Companding Speech Synthesis and Recognition Nonlinear Audio Processing Chapter 23 - Image Formation & Display Digital Image Structure Cameras and Eyes Television Video Signals Other Image Acquisition and Display Brightness and Contrast Adjustments Grayscale Transforms Warping Chapter 24 - Linear Image Processing Convolution 3x3 Edge Modification Convolution by Separability Example of a Large PSF: Illumination Flattening Fourier Image Analysis FFT Convolution A Closer Look at Image Convolution Chapter 25 - Special Imaging Techniques Spatial Resolution Sample Spacing and Sampling Aperture Signal-to-Noise Ratio Morphological Image Processing Computed Tomography Chapter 26 - Neural Networks (and more!) Target Detection Neural Network Architecture Why Does it Work? Training the Neural Network Evaluating the Results Recursive Filter Design Chapter 27 - Data Compression Data Compression Strategies Run-Length Encoding Huffman Encoding Delta Encoding LZW Compression JPEG (Transform Compression) MPEG Chapter 28 - Digital Signal Processors How DSPs are Different from Other Microprocessors Circular Buffering Architecture of the Digital Signal Processor Fixed versus Floating Point C versus Assembly How Fast are DSPs? The Digital Signal Processor Market Chapter 29 - Getting Started with DSPs The ADSP-2106x family The SHARC EZ-KIT Lite Design Example: An FIR Audio Filter Analog Measurements on a DSP System Another Look at Fixed versus Floating Point Advanced Software Tools COMPLEX TECHNIQUES Chapter 30 - Complex Numbers The Complex Number System Polar Notation Using Complex Numbers by Substitution Complex Representation of Sinusoids Complex Representation of Systems Electrical Circuit Analysis Chapter 31 - The Complex Fourier Transform The Real DFT Mathematical Equivalence The Complex DFT The Family of Fourier Transforms Why the Complex Fourier Transform is Used Chapter 32 - The Laplace Transform The Nature of the s-Domain Strategy of the Laplace Transform Analysis of Electric Circuits The Importance of Poles and Zeros Filter Design in the s-Domain Chapter 33 - The z-Transform The Nature of the z-Domain Analysis of Recursive Systems Cascade and Parallel Stages Spectral Inversion Gain Changes Chebyshev-Butterworth Filter Design The Best and Worst of DSP Chapter 34 - Explaining Benford's Law Frank Benford's Discovery Homomorphic Processing The Ones Scaling Test Writing Benford's Law as a Convolution Solving in the Frequency Domain Solving Mystery #1 Solving Mystery #2 More on Following Benford's law Analysis of the Log-Normal Distribution The Power of Signal Processing copyright � 1997-2007 by California Technical Pub
2009-10-01 上传
The Scientist and Engineer Guide to Digital Signal Processing——Second Edition by Steven W. Smith This book was written for scientists and engineers in a wide variety of fields: physics, bioengineering, geology, oceanography, mechanical and electrical engineering, to name just a few. The goal is to present practical techniques while avoiding the barriers of detailed mathematics and abstract theory. To achieve this goal, three strategies were employed in writing this book: First, the techniques are explained, not simply proven to be true through mathematical derivations. While much of the mathematics is included, it is not used as the primary means of conveying the information. Nothing beats a few well written paragraphs supported by good illustrations. Second, complex numbers are treated as an advanced topic, something to be learned after the fundamental principles are understood. Chapters 1-29 explain all the basic techniques using only algebra, and in rare cases, a small amount of elementary calculus. Chapters 30-33 show how complex math extends the power of DSP, presenting techniques that cannot be implemented with real numbers alone. Many would view this approach as heresy! Traditional DSP textbooks are full of complex math, often starting right from the first chapter. xiii Third, very simple computer programs are used. Most DSP programs are written in C, Fortran, or a similar language. However, learning DSP has different requirements than using DSP. The student needs to concentrate on the algorithms and techniques, without being distracted by the quirks of a particular language. Power and flexibility aren't important; simplicity is critical. The programs in this book are written to teach DSP in the most straightforward way, with all other factors being treated as secondary. Good programming style is disregarded if it makes the program logic more clear.