Explore MATLAB source code curated for "高斯噪声" with clean implementations, documentation, and examples.
Energy detection implementation based on foundational research by Urkowitz (1967), Digham (2003), and Ghasemi (2007). This code generates original signals (single-tone or BPSK baseband), adds AWGN with specified SNR, and performs energy detection with statistical analysis of detection performance.
This MATLAB-based simulation evaluates spread spectrum system performance and bit error rate (BER) under various interference conditions including Gaussian noise, single-tone interference, multi-tone interference, and different duty cycle jamming scenarios, with implementation details for signal generation and analysis algorithms.
MATLAB implementation for adding multiple noise types to images: salt-and-pepper noise, Gaussian noise, multiplicative noise, and more, with code examples and algorithm explanations.
Simulation of GPS anti-jamming antennas using power inversion algorithms with Gaussian noise generation functions and MATLAB implementation details
Simulation and analysis of 8PSK bit error rate performance in AWGN channels with theoretical validation and implementation insights
Comparative Analysis of Bit Error Rate Performance in 64QAM Systems with Gaussian Noise: Experimental Measurements versus Theoretical Calculations
Implementing Gaussian noise, uniform noise, and salt-and-pepper noise addition to images, followed by denoising using alpha-trimmed mean filtering and adaptive median filtering algorithms, with comprehensive performance evaluation of denoising effectiveness
This implementation demonstrates FSK modulation technique, enabling simulation of frequency hopping system performance and bit error rate under various interference conditions including Gaussian noise and single-tone interference. The code provides comprehensive signal generation, modulation/demodulation processing, and BER calculation functionalities.
Various algorithms for adding noise to images, including salt-and-pepper noise and Gaussian noise. Since MATLAB's built-in Gaussian noise function has normalization issues that may produce unexpected results, it's generally recommended to implement custom noise addition methods.
A MATLAB-based denoising program implementing Gaussian noise elimination with excellent performance, featuring modular code structure for easy customization and algorithm integration.