Introduction to Statistical Signal Processing Project – Spring 2025 (IIIT Hyderabad)

This repository contains the project files and report for the Introduction to Statistical Signal Processing course at IIIT Hyderabad, Spring 2025.

Project Overview

1. ECG Signal Filtering using Adaptive Filters

This project focuses on filtering a noisy ECG signal using various adaptive filters like the Recursive Least Squares (RLS), Least Mean Squares (LMS), Steepest Descent Algorithm in both stationary and non-stationary conditions. The goal was to analyze the performance of each filter in denoising the ECG signal under different statistical conditions.

2. Kalman Filter for Motion Tracking

The second problem involves applying the Kalman filter for tracking the motion of a vehicle from a fixed, stationary reference point. The Kalman filter is used for state prediction and update using measurements. This helps estimate the trajectory of the vehicle in the presence of noise and uncertainty.

Contents

• project files/problem1 – Code for adaptive filtering of ECG signals
• project files/problem2 – Code for Kalman filter-based motion estimation
• 2023112005_ISSP_ProjectReport.pdf – Final project report explaining the results.