数字信号处理(Digital Signal Processing，简称DSP)是一门涉及许多学科而又广泛应用于许多领域的新兴学科。20世纪60年代以来，随着计算机和信息技术的飞速发展，数字信号处理技术应运而生并得到迅速的发展。数字信号处理是一种通过使用数学技巧执行转换或提取信息，来处理现实信号的方法，这些信号由数字序列表示。在过去的二十多年时间里，数字信号处理已经在通信等领域得到极为广泛的应用。德州仪器、Freescale等半导体厂商在这一领域拥有很强的实力。

DSP微处理器（芯片）一般具有如下主要特点： 　　（1）在一个指令周期内可完成一次乘法和一次加法； 　　（2）程序和数据空间分开，可以同时访问指令和数据； 　　（3）片内具有快速RAM，通常可通过独立的数据总线在两块中同时访问； 　　（4）具有低开销或无开销循环及跳转的硬件支持； 　　（5）快速的中断处理和硬件I/O支持； 　　（6）具有在单周期内操作的多个硬件地址产生器； 　　（7）可以并行执行多个操作； 　　（8）支持流水线操作，使取指、译码和执行等操作可以重叠执行。 　　当然，与通用微处理器相比，DSP微处理器（芯片）的其他通用功能相对较弱些。 　　DSP优点： 　　对元件值的容限不敏感，受温度、环境等外部参与影响小； 　　容易实现集成；VLSI 　　可以分时复用，共享处理器； 　　方便调整处理器的系数实现自适应滤波； 　　可实现模拟处理不能实现的功能：线性相位、多抽样率处理、级联、易于存储等； 　　可用于频率非常低的信号。 　　DSP缺点： 　　需要模数转换； 　　受采样频率的限制，处理频率范围有限； 　　数字系统由耗电的有源器件构成，没有无源设备可靠。 　　但是其优点远远超过缺点。

DSP技术的应用
　　语音处理：语音编码、语音合成、语音识别、语音增强、语音邮件、语音储存等。 　　图像/图形：二维和三维图形处理、图像压缩与传输、图像识别、动画、机器人视觉、多媒体、电子地图、图像增强等。 　　军事；保密通信、雷达处理、声呐处理、导航、全球定位、跳频电台、搜索和反搜索等。 　　仪器仪表：频谱分析、函数发生、数据采集、地震处理等。 　　自动控制：控制、深空作业、自动驾驶、机器人控制、磁盘控制等。 　　医疗：助听、超声设备、诊断工具、病人监护、心电图等。 　　家用电器：数字音响、数字电视、可视电话、音乐合成、音调控制、玩具与游戏等。 　　生物医学信号处理举例： 　　  CT机示例
CT：计算机X射线断层摄影装置。（其中发明头颅CT英国EMI公司的豪斯菲尔德获诺贝尔奖。） 　　CAT:计算机X射线空间重建装置。出现全身扫描，心脏活动立体图形，脑肿瘤异物，人体躯干图像重建。 　　心电图分析。
基于DSP的智能视频监控系统
传统的视频监视系统是简单的非智能闭路电视（CCTV）系统，其缺点十分明显。这样的系统或者需要安保人员实时监视画面以捕捉关键事件，或者需要在事后对视频记录进行回放并进行人工分析，耗时耗力，成本高而效率低。近几年，DSP在智能视频监控系统方面的应用不断完善，正在逐渐取代传统的模拟非智能系统。 　　iSuppli公司2006年的一份分析报告曾指出，IP视频监控系统市场到2010年将增长近十倍。 IP监控的创新技术之一是“智能摄像机”，它拥有强大的数字信号处理器，能探测威胁并触发自动响应。可见，DSP芯片是智能监控的核心。

DSP产业在约40年的历程中经历了三个阶段：第一阶段，DSP意味着数字信号处理，并作为一个新的理论体系广为流行；随着这个时代的成熟，DSP进入了发展的第二阶段，在这个阶段，DSP代表数字信号处理器，这些DSP器件使我们生活的许多方面都发生了巨大的变化；接下来又催生了第三阶段，这是一个赋能(enablement)的时期，我们将看到DSP理论和DSP架构都被嵌入到SoC类产品中。” 第一阶段，DSP意味着数字信号处理 。 80年代开始了第二个阶段，DSP从概念走向了产品，TMS32010所实现的出色性能和特性备受业界关注。方进先生在一篇文章中提到，新兴的DSP业务同时也承担着巨大的风险，究竟向哪里拓展是生死攸关的问题。当设计师努力使DSP处理器每MIPS成本降到了适合于商用的低于10美元范围时，DSP在军事、工业和商业应用中不断获得成功。到1991年，TI推出价格可与16位微处理器不相上下的DSP芯片，首次实现批量单价低于5美元，但所能提供的性能却是其5至10倍。 到90年代，多家公司跻身DSP领域与TI进行市场竞争。TI首家提供可定制 DSP——cDSP，cDSP 基于内核 DSP的设计可使DSP具有更高的系统集成度，大加速了产品的上市时间。同时，TI瞄准DSP电子市场上成长速度最快的领域。到90年代中期，这种可编程的DSP器件已广泛应用于数据通信、海量存储、语音处理、汽车电子、消费类音频和视频产品等等，其中最为辉煌的成就是在数字蜂窝电话中的成功。这时，DSP业务也一跃成为TI最大的业务，这个阶段DSP每MIPS的价格已降到10美分到1美元的范围。 21世纪DSP发展进入第三个阶段，市场竞争更加激烈，TI及时调整DSP发展战略全局规划，并以全面的产品规划和完善的解决方案，加之全新的开发理念，深化产业化进程。成就这一进展的前提就是DSP每MIPS价格目标已设定为几个美分或更低。


DSP未来发展
　　１、数字信号处理器的内核结构进一步改善，多通道结构和单指令多重数据(SIMD)、特大指令字组(VLIM)将在新的高性能处理器中将占主导地位，如Analog Devices的 ADSP-2116x。  ADSP产品
２、DSP 和微处理器的融合：　 　　微处理器是低成本的，主要执行智能定向控制任务的通用处理器能很好执行智能控制任务，但是数字信号处理功能很差。而DSP的功能正好与之相反。在许多应用中均需要同时具有智能控制和数字信号处理两种功能，如数字蜂窝电话就需要监测和声音处理功能。因此，把DSP和微处理器结合起来，用单一芯片的处理器实现这两种功能，将加速个人通信机、智能电话、无线网络产品的开发，同时简化设计，减小PCB体积，降低功耗和整个系统的成本。例如，有多个处理器的Motorola公司的DSP5665x，有协处理器功能的Massan公司FILU-200，把MCU功能扩展成DSP和MCU功能的TI公司的TMS320C27xx以及Hitachi公司的SH-DSP，都是DSP和MCU融合在一起的产品。互联网和多媒体的应用需要将进一步加速这一融合过程。 　　3、DSP 和高档CPU的融合： 　　大多数高档GPP如Pentium 和PowerPC都是SIMD指令组的超标量结构，速度很快。LSI Logic 公司的LSI401Z采用高档CPU的分支预示和动态缓冲技术，结构规范，利于编程，不用担心指令排队，使得性能大幅度提高。Intel公司涉足数字信号处理器领域将会加速这种融合。 　　4、DSP 和SOC的融合： 　　  SOC
SOC(System-On-Chip)是指把一个系统集成在一块芯片上。这个系统包括DSP 和系统接口软件等。比如Virata公司购买了LSI Logic公司的ZSP400处理器内核使用许可证，将其与系统软件如USB、10BASET、以太网、UART、GPIO、HDLC等一起集成在芯片上，应用在xDSL上，得到了很好的经济效益。因此，SOC芯片近几年销售很好，由1998年的1.6亿片猛增至1999年的3.45亿片。1999年，约39%的SOC产品应用于通讯系统。今后几年，SOC将以每年31%的平均速度增长，到2004年将达到13亿片。毋庸置疑，SOC将成为市场中越来越耀眼的明星。 　　5、DSP 和FPGA的融合： 　　FPGA是现场编程门阵列器件。它和DSP集成在一块芯片上，可实现宽带信号处理，大大提高信号处理速度。据报道，Xilinx 公司的Virtex-II FPGA对快速傅立叶变换(FFT)的处理可提高30倍以上。它的芯片中有自由的FPGA可供编程。Xilinx公司开发出一种称作Turbo卷积编译码器的高性能内核。设计者可以在FPGA中集成一个或多个Turbo内核，它支持多路大数据流，以满足第三代(3G)WCDMA无线基站和手机的需要，同时大大  WCDMA无线基站
节省开发时间，使功能的增加或性能的改善非常容易。因此在无线通信、多媒体等领域将有广泛应用。

Digital signal processing (DSP) is concerned with the representation of signals by a sequence of numbers or symbols and the processing of these signals. Digital signal processing and analog signal processing are subfields of signal processing. DSP includes subfields like: audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for communications, control of systems, biomedical signal processing, seismic data processing, etc.
The goal of DSP is usually to measure, filter and/or compress continuous real-world analog signals. The first step is usually to convert the signal from an analog to a digital form, by sampling it using an analog-to-digital converter (ADC), which turns the analog signal into a stream of numbers. However, often, the required output signal is another analog output signal, which requires a digital-to-analog converter (DAC). Even if this process is more complex than analog processing and has a discrete value range, the application of computational power to digital signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression.[1]
DSP algorithms have long been run on standard computers, on specialized processors called digital signal processors (DSPs), or on purpose-built hardware such as application-specific integrated circuit (ASICs). Today there are additional technologies used for digital signal processing including more powerful general purpose microprocessors, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial apps such as motor control), and stream processors, among others.[
DSP domains
In DSP, engineers usually study digital signals in one of the following domains: time domain (one-dimensional signals), spatial domain (multidimensional signals), frequency domain, autocorrelation domain, and wavelet domains. They choose the domain in which to process a signal by making an informed guess (or by trying different possibilities) as to which domain best represents the essential characteristics of the signal. A sequence of samples from a measuring device produces a time or spatial domain representation, whereas a discrete Fourier transform produces the frequency domain information, that is the frequency spectrum. Autocorrelation is defined as the cross-correlation of the signal with itself over varying intervals of time or space.
Signal sampling
Main article: Sampling (signal processing)
With the increasing use of computers the usage of and need for digital signal processing has increased. In order to use an analog signal on a computer it must be digitized with an analog-to-digital converter. Sampling is usually carried out in two stages, discretization and quantization. In the discretization stage, the space of signals is partitioned into equivalence classes and quantization is carried out by replacing the signal with representative signal of the corresponding equivalence class. In the quantization stage the representative signal values are approximated by values from a finite set.
The Nyquist–Shannon sampling theorem states that a signal can be exactly reconstructed from its samples if the sampling frequency is greater than twice the highest frequency of the signal. In practice, the sampling frequency is often significantly more than twice the required bandwidth.
A digital-to-analog converter is used to convert the digital signal back to analog. The use of a digital computer is a key ingredient in digital control systems.
Time and space domains
Main article: Time domain
The most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Digital filtering generally consists of some linear transformation of a number of surrounding samples around the current sample of the input or output signal. There are various ways to characterize filters; for example:
?        A "linear" filter is a linear transformation of input samples; other filters are "non-linear". Linear filters satisfy the superposition condition, i.e. if an input is a weighted linear combination of different signals, the output is an equally weighted linear combination of the corresponding output signals.
?        A "causal" filter uses only previous samples of the input or output signals; while a "non-causal" filter uses future input samples. A non-causal filter can usually be changed into a causal filter by adding a delay to it.
?        A "time-invariant" filter has constant properties over time; other filters such as adaptive filters change in time.
?        Some filters are "stable", others are "unstable". A stable filter produces an output that converges to a constant value with time, or remains bounded within a finite interval. An unstable filter can produce an output that grows without bounds, with bounded or even zero input.
?        A "finite impulse response" (FIR) filter uses only the input signals, while an "infinite impulse response" filter (IIR) uses both the input signal and previous samples of the output signal. FIR filters are always stable, while IIR filters may be unstable.
Filters can be represented by block diagrams which can then be used to derive a sample processing algorithm to implement the filter using hardware instructions. A filter may also be described as a difference equation, a collection of zeroes and poles or, if it is an FIR filter, an impulse response or step response.
The output of a digital filter to any given input may be calculated by convolving the input signal with the impulse response.
Frequency domain
Main article: Frequency domain
Signals are converted from time or space domain to the frequency domain usually through the Fourier transform. The Fourier transform converts the signal information to a magnitude and phase component of each frequency. Often the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared.
The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to determine which frequencies are present in the input signal and which are missing.
In addition to frequency information, phase information is often needed. This can be obtained from the fourier transform. With some applications, how the phase varies with frequency can be a significant consideration.
Filtering, particularly in non-realtime work can also be achieved by converting to the frequency domain, applying the filter and then converting back to the time domain. This is a fast, O(n log n) operation, and can give essentially any filter shape including excellent approximations to brickwall filters.
There are some commonly used frequency domain transformations. For example, the cepstrum converts a signal to the frequency domain through Fourier transform, takes the logarithm, then applies another Fourier transform. This emphasizes the frequency components with smaller magnitude while retaining the order of magnitudes of frequency components.
Frequency domain analysis is also called spectrum- or spectral analysis.
Z-domain analysis
Whereas analog filters are usually analysed on the s-plane; digital filters are analysed on the z-plane or z-domain in terms of z-transforms.
Most filters can be described in Z-domain (a complex number superset of the frequency domain) by their transfer functions. A filter may be analysed in the z-domain by its characteristic collection of zeroes and poles.
Applications
The main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, RADAR, SONAR, seismology, and biomedicine. Specific examples are speech compression and transmission in digital mobile phones, room matching equalization of sound in Hifi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, computer-generated animations in movies, medical imaging such as CAT scans and MRI, MP3 compression, image manipulation, high fidelity loudspeaker crossovers and equalization, and audio effects for use with electric guitar amplifiers.
Implementation
Digital signal processing is often implemented using specialised microprocessors such as the DSP56000, the TMS320, or the SHARC. These often process data using fixed-point arithmetic, although some versions are available which use floating point arithmetic and are more powerful. For faster applications FPGAs[3] might be used. Beginning in 2007, multicore implementations of DSPs have started to emerge from companies including Freescale and Stream Processors, Inc. For faster applications with vast usage, ASICs might be designed specifically. For slow applications, a traditional slower processor such as a microcontroller may be adequate. Also a growing number of DSP applications are now being implemented on Embedded Systems using powerful PCs with a Multi-core processor

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