GPS C/A Code Analysis and Correlation Functions

Task 1:

• Generates C/A codes for three GPS satellites (PRN A, PRN B, PRN C) using specified SV numbers.
• Plots the first 20 chips of each generated C/A code.

Task 2:

• Calculates auto-correlation function of C/A code for PRN A.
• Plots auto-correlation function over a specified range and identifies characteristics.

Task 3:

• Calculates cross-correlation functions between C/A code of PRN A and codes of PRN B and PRN C.
• Plots cross-correlation functions for different chip shifts (0 and 132 chips) and analyzes results.

Task 4:

• Creates accumulated code by adding shifted versions of C/A codes for PRN A, PRN B, and PRN C.
• Calculates cross-correlation function of accumulated code with original code of PRN A and plots it.

Task 5:

• Adds white noise with specified standard deviation to accumulated code generated in Task 4.
• Calculates cross-correlation function of noisy signal with original code of PRN A and plots it.
• Compares outcome with Task 4 to observe impact of noise on correlation functions.

Task 6:

• Discusses characteristics of correlation functions concerning GPS receiver tracking performance.
• Examines correlation peak recognition under different levels of noise (specified in two cases).

This repository includes MATLAB functions for generating C/A codes, computing auto-correlation and cross-correlation functions, and visualizing results through plots. Tasks aim to analyze correlation properties of GPS C/A codes and their behavior under noise conditions, providing insights into GPS signal processing and receiver tracking performance.