Module
Digital Signal Processing
Sampling, Fourier transforms and filters, built up in GNU Radio from first principles.
8 lessons, in the order they are meant to be worked through
***Note: These lessons were constructed using GNU Radio 3.8. The exercises should work across GNU Radio versions. Some blocks may differ slightly.
A. Introduction to GNU Radio and Some Basic DSP
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These are the lessons that appear on the Build a Simple Spectrometer page.
- Lesson 1 (view PDF) - Introduction to GNU Radio basics.
- Lesson 2 (view PDF) - Learning more GNU Radio tools building a multiple waveform source.
- Lesson 3 (view PDF) - Demonstration of Fourier series.
- Lesson 4 (view PDF) - Demonstration of how an FFT block works.
- Lesson 5 (view PDF) - Filter basics.
B. Quadrature sampling
- I/Q sampling, worked through in a notebook. Learn why a receiver uses channels a quarter-cycle apart and what a sample’s imaginary part represents. Runnable Python with the plots already rendered.
8 lessons in this module
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Digital Signal Processing Lectures and Demonstrations
A deep dive into Digital Signal Processing through a series of lectures and demonstrations
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Making Waves with Fourier Series
Students make complex waves by adding various sine cosine waves
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Digital Signal Processing using GNU Radio - Introductory Lab
Build your first GNU Radio flowgraph and explore signals, sampling, and sound.
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Digital Signal Processing using GNU Radio - Software Defined Radio
Connect a radio receiver and investigate amplitude modulation, frequency modulation, and FM reception.
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Digital Signal Processing using GNU Radio - Fourier Analysis
Build signals from sine waves and explore their Fourier transforms.
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Digital Signal Processing using GNU Radio - Digital Filter
Design digital filters and compare their effects on signals and noise.
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Digital Signal Processing using GNU Radio - Fourier Analysis and Radio Astronomy
Use I/Q signals, Fourier transforms, and window functions to build a radio spectrometer.
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Correlation and the Two-Element Interferometer
Convolution, autocorrelation and cross-correlation, and why two horns see what one cannot