Digital Signal Processing (DSP) involves manipulating and analyzing signals to extract meaningful information. DSP normally relies on predefined algorithms and mathematical models for signal-processing tasks. This project explores the integration of principal component analysis (PCA) into DSP to enhance its capabilities and adaptability, and also to draw connections to the methods we used in linear algebra.
The objective of this project is to investigate how PCA can be used for tasks such as signal denoising, modulation recognition, and feature extraction. We explore different channel models to assess how our PCA DSP implementation functions with differing conditions.
Please visit pca.ipynb for a step-by-step walkthrough on how we used PCA to decode modulated signals.
You can install the required libararies on Ubuntu using the following commands:
$ pip install matplotlib
$ pip install numpy
$ pip install scipy