Inertial Navigation + GPS Integrated Navigation System
Inertial Navigation System (INS) and GPS integrated navigation, featuring classic algorithms with code implementation insights. Essential knowledge for navigation system developers.
Explore MATLAB source code curated for "经典算法" with clean implementations, documentation, and examples.
Inertial Navigation System (INS) and GPS integrated navigation, featuring classic algorithms with code implementation insights. Essential knowledge for navigation system developers.
MATLAB simulation program for the classic OFDM S&C algorithm with graphical output capabilities and comprehensive implementation details
MATLAB implementations of classic line drawing algorithms in computer graphics, including midpoint line algorithm and other fundamental techniques with detailed code explanations and performance analysis.
Implementation of classical beamforming algorithms. Since several programs downloaded from this site were non-functional, we decided to upload our own version for public download. This verified implementation includes key features like delay-and-sum processing, adaptive filtering, and direction-of-arrival estimation.
Implementation code from the seminal 1999 Nature paper introducing the fundamental non-negative matrix factorization algorithm
The classical Lucas-Kanade-Tomasi algorithm for optical flow motion, including detailed explanations and illustrative images with code implementation insights.
Classic algorithm for image coding using wavelet transform, featuring complete compression and decompression processes with executable demonstration code implementations.
Implementation of classical hill climbing algorithm in MATLAB, featuring code explanations and optimization techniques. Ideal for beginners learning MATLAB and fundamental algorithms.
Overview of key pattern recognition algorithms featuring detailed implementation processes, experimental reports, and code-level explanations of core functions and methodologies
The RANSAC algorithm serves as a fundamental outlier removal technique that leverages intrinsic constraints within feature point sets to eliminate erroneous matches, with practical implementation involving iterative hypothesis generation and consensus evaluation.