Location:Training / Embedded Development / DSP / Digital Signal Processing Beihang University Wang Jun / Discrete periodic signal DFS

This course mainly explains the theory, principles and implementation methods of digital signal processing. It is a basic course for electronic information majors. When studying, you need to pay attention to integrating theory with engineering practice. The course uses engineering examples of sound signal spectrum analysis, filter design, performance simulation, and hardware implementation throughout the course to cultivate students' system analysis, system design, and system implementation abilities. Its prerequisite courses include: advanced mathematics (mathematical analysis), linear algebra, complex variable functions, circuit analysis, signals and systems, etc. At the same time, it is the basis for learning subsequent professional courses in electronic information.

This course focuses on the connection with the prerequisite course "Signals and Systems" and the correlation of knowledge points. Compare the content related to signals and systems, analyze the differences and connections between continuous, discrete, and digital signals, and compare the similarities and differences between digital signal processing and continuous signal processing, which will help you understand related knowledge. From the unit pulse representation of the discrete time series, the convolutional representation of the LTI system is derived. From the complex exponential representation of discrete time series, Fourier series, Fourier transform and Z transform are derived. From the characteristic function of the LTI system, the frequency response of the LTI system is derived. From the relationship between Fourier transform and Z transform, the relationship between the zero and pole points of the rational system function and the frequency response is derived. From continuous and discrete, periodic and aperiodic, the discrete Fourier transform (DFT) is derived. From the relationship between Z transform and S transform, the IIR filter design method is derived. From the Fourier transform properties, the FIR filter design method is derived. From the difference equation, the filter structure and implementation method are derived. Finally, engineering applications of digital signal processing such as signal spectrum analysis methods are given. In addition, the course sorts out the correlations between relevant knowledge points, and you can compare relevant chapters of the course. For example: there are inherent similarities and connections between time domain sampling and frequency domain sampling, time domain cycles and frequency domain cycles, DFS and DTFT, and DFT knowledge points. Through comparative learning, learning efficiency can be improved.

This course focuses on experimentation and practice, using audio signal processing as an example, adopting a problem-based experimental teaching model, and equipped with modular experimental routines such as signal spectrum analysis, filter design, filter implementation, and comprehensive course experiments, including analog filters. , Matlab/FPGA/DSP digital filter, etc. Analog filter examples connect relevant course content such as electronic circuits, signals and systems to increase the continuity of knowledge. Examples of digital filters to strengthen the connection and difference between "digital signal processing" and "signals and systems".