Implemented Kalman Filter-Based Blind Multiuser Detection System

This implemented Kalman filter-based blind multiuser detection program serves as a robust computational tool for communication system analysis. The algorithm employs recursive estimation through Kalman filtering to track user signals without requiring training sequences, implementing adaptive weight updates through covariance matrix operations. The simulation framework generates performance curves by computing bit error rates across different SNR levels, utilizing statistical averaging over multiple transmission frames. Through this program, researchers can effectively analyze multiuser detection performance, observe interference cancellation effects, and optimize system parameters for practical applications. The implementation demonstrates significant potential in communication engineering applications, offering valuable insights for both theoretical research and practical system design through its modular code structure featuring signal generation, channel modeling, and detection modules.