Common Filtering Algorithms Implementation

In this project, we have implemented several common filtering algorithms using MATLAB. The implemented algorithms include median filtering, recursive average filtering, and first-order lag filtering. Each algorithm is accompanied by comprehensive code explanations: median filtering operates by sorting a sliding window of data points and selecting the middle value to effectively remove impulse noise; recursive average filtering (also known as moving average) calculates the average of recent data samples using a buffer that updates with each new input; first-order lag filtering implements a low-pass filter using exponential smoothing with a tunable time constant parameter. These algorithms enable effective data processing and purification to obtain more accurate results. Whether processing real-time streaming data or offline datasets, these filtering algorithms can run directly and produce excellent performance. The implementation includes proper handling of edge cases and parameter optimization guidance. We believe these filtering algorithms are highly valuable for data processing tasks, significantly enhancing data quality and accuracy through robust mathematical foundations and practical MATLAB coding techniques.