Detector Design Using Minimum Total Error Probability Criterion

This program designs a detector using the minimum total error probability criterion and calculates the optimal detection threshold. By systematically varying the threshold within a specified range, the code investigates how the probability of error (Pe) changes with threshold adjustments, generating Pe~threshold curves and analyzing simulation results. The program is fully commented for clarity. Specifically, the implementation allows testing different threshold values by incorporating them into the detection algorithm and computing corresponding Pe values. This enables construction of Pe~threshold curves, facilitating in-depth analysis of how threshold selection impacts detector performance. The code structure includes threshold iteration loops, probability calculation functions, and curve plotting modules. Through extended simulation studies, the program can further investigate detector performance under various noise conditions, providing comprehensive references for practical applications. The algorithm employs statistical decision theory to minimize total error probability, with key functions handling threshold optimization, error probability calculation, and performance visualization. By studying threshold variations, users can better understand detector performance characteristics and obtain accurate guidance for real-world implementations. The modular code design allows easy extension for different signal models and noise distributions.