具有误差修正和智能接口的DSP测试系统设计及研究
发布时间：2018-11-02 17:07
【摘要】：DSP是以数字信号来分析大量信息的一种先进的微处理器器件。其基本理论是将接收到的模拟信号转换为1或0的数字信号,然后对这个信号进行弱化、修正、加强,同时通过芯片进行处理,把得到的数字信号数据转换回模拟信号数据或者现实应用中真实的环境格式。历经近数十年的飞速发展,利用DSP技术的商品已经遍及人们日常学习、工作和生活的方方面面。由此而产生的DSP测试系统设计已引起业界的关注。现今无论在通讯、计算机行业中,甚至在人们日常生活常用的诸多产品行业中,都已逐步运用到这种集成度超高的由DSP芯片构成的系统。实际应用中测试系统往往因受到传感器的工作频带过窄等原因,导致将某些有用的信号频率分量畸形化,最终出现测试数据失真的问题。传统的单片机或者集成电路甚至二者的结合,均无法实时修正上述原因产生的误差。可见,测试系统的响应时间、接收信号的瞬时性及真实性等参数,均为考查该系统性能优劣的重要指标之一。为了能够实时修正误差,则需要采用合适的数据处理芯片及算法使系统能够具有误差修正功能。本文采用TMS320F2812DSP芯片和自适应神经网络动态补偿算法,针对TMS320F2812DSP芯片特有的性能和结构特点,通过设计其外围硬件结构并结合外扩存储器,设计了具有误差修正功能和智能接口的DSP应用系统。将信号的动态输入、输出信号进行校正,创建数学模型。并且同时将信号逆建模,也就是将信号的输出看做是补偿系统的输入,信号的输入信号看做是补偿系统的输出,基于神经网络拟合算法最后获得相应的输入和输出的线性关系。通过实验验证得知:该测试系统能够正确地采集存储数据,并且可以高效弥补信号的动态误差,提高了信号调理的精度和实时性。论文的创新点如下:选用了核心器件TMS320F2812 DSP作为主要控制单元,并且基于其功能特性针对性地搭配性能良好的外围电路。数据处理上,利用自适应神经网络算法,进行逆建模,有效的提高了信号调理的精度和实时性。并将DSP的高速性和误差校正性与测试系统的智能拓展接口有效的整合在一起。本文设计的重点内容:主要是针对TMS320F2812DSP芯片特有的性能进行分析,针对性地设计相应的外围电路,同时对于外接输入的数据进行处理与研究。
[Abstract]:DSP is an advanced microprocessor device which uses digital signal to analyze a lot of information. The basic theory is to convert the received analog signal to a digital signal of 1 or 0, and then weaken, modify, strengthen, and process it by chip. Convert the obtained digital signal data back to analog signal data or real environmental format in real applications. After decades of rapid development, DSP technology has been used in all aspects of people's daily study, work and life. The design of DSP test system has attracted the attention of the industry. Nowadays, no matter in communication, computer industry, and even in many product industries commonly used in people's daily life, it has been gradually applied to this kind of system composed of DSP chips with high level of integration. In practical application, some useful signal frequency components are deformed due to the narrow working frequency band of the sensor, which leads to the distortion of the test data. Traditional microcontroller, integrated circuit or even the combination of the two, can not correct the error caused by the above reasons in real time. It can be seen that the response time of the test system, the instantaneous nature and authenticity of the received signal are all important indexes to test the performance of the system. In order to correct the error in real time, it is necessary to use the appropriate data processing chip and algorithm to make the system have error correction function. In this paper, the TMS320F2812DSP chip and the adaptive neural network dynamic compensation algorithm are adopted. According to the special performance and structural characteristics of the TMS320F2812DSP chip, the peripheral hardware structure is designed and combined with the extended memory. A DSP application system with error correction function and intelligent interface is designed. The dynamic input and output signals are corrected and the mathematical model is created. At the same time, the inverse model of the signal is modeled, that is, the output of the signal is regarded as the input of the compensation system, and the input signal of the signal is regarded as the output of the compensation system. Finally, the linear relationship between the input and the output is obtained based on the neural network fitting algorithm. The experimental results show that the system can collect and store data correctly, and can compensate the dynamic error of signal efficiently, and improve the precision and real-time of signal conditioning. The innovation of this paper is as follows: the core device TMS320F2812 DSP is selected as the main control unit, and based on its functional characteristics, the peripheral circuits with good performance are selected. In data processing, adaptive neural network algorithm is used for inverse modeling, which effectively improves the precision and real time of signal conditioning. The high speed and error correction of DSP are effectively integrated with the intelligent extended interface of the test system. The main content of this paper is to analyze the characteristic performance of TMS320F2812DSP chip, to design the corresponding peripheral circuit, and to process and study the external input data.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP332;TP274
本文编号：2306416
[Abstract]:DSP is an advanced microprocessor device which uses digital signal to analyze a lot of information. The basic theory is to convert the received analog signal to a digital signal of 1 or 0, and then weaken, modify, strengthen, and process it by chip. Convert the obtained digital signal data back to analog signal data or real environmental format in real applications. After decades of rapid development, DSP technology has been used in all aspects of people's daily study, work and life. The design of DSP test system has attracted the attention of the industry. Nowadays, no matter in communication, computer industry, and even in many product industries commonly used in people's daily life, it has been gradually applied to this kind of system composed of DSP chips with high level of integration. In practical application, some useful signal frequency components are deformed due to the narrow working frequency band of the sensor, which leads to the distortion of the test data. Traditional microcontroller, integrated circuit or even the combination of the two, can not correct the error caused by the above reasons in real time. It can be seen that the response time of the test system, the instantaneous nature and authenticity of the received signal are all important indexes to test the performance of the system. In order to correct the error in real time, it is necessary to use the appropriate data processing chip and algorithm to make the system have error correction function. In this paper, the TMS320F2812DSP chip and the adaptive neural network dynamic compensation algorithm are adopted. According to the special performance and structural characteristics of the TMS320F2812DSP chip, the peripheral hardware structure is designed and combined with the extended memory. A DSP application system with error correction function and intelligent interface is designed. The dynamic input and output signals are corrected and the mathematical model is created. At the same time, the inverse model of the signal is modeled, that is, the output of the signal is regarded as the input of the compensation system, and the input signal of the signal is regarded as the output of the compensation system. Finally, the linear relationship between the input and the output is obtained based on the neural network fitting algorithm. The experimental results show that the system can collect and store data correctly, and can compensate the dynamic error of signal efficiently, and improve the precision and real-time of signal conditioning. The innovation of this paper is as follows: the core device TMS320F2812 DSP is selected as the main control unit, and based on its functional characteristics, the peripheral circuits with good performance are selected. In data processing, adaptive neural network algorithm is used for inverse modeling, which effectively improves the precision and real time of signal conditioning. The high speed and error correction of DSP are effectively integrated with the intelligent extended interface of the test system. The main content of this paper is to analyze the characteristic performance of TMS320F2812DSP chip, to design the corresponding peripheral circuit, and to process and study the external input data.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP332;TP274
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