Fingerprint Localization Algorithm Simulation Code

This project presents a comprehensive simulation framework for fingerprint-based localization systems. Fingerprint localization is a positioning methodology leveraging fingerprint recognition principles, where wireless signal characteristics (such as RSSI, CSI, or magnetic field data) serve as unique "fingerprints" for different locations.

With the rapid advancement of wireless network technologies, there is growing demand for precise positioning solutions. The fingerprint localization process involves two key phases: an offline training stage where signal fingerprints are collected and stored in a database, and an online positioning stage where real-time user measurements are compared against the database using similarity algorithms (e.g., K-Nearest Neighbors or probabilistic approaches) to estimate location.

Our simulation implements core components including: 1) Environment modeling for signal propagation, 2) Fingerprint database generation with configurable noise parameters, 3) Multiple matching algorithms (Euclidean distance, Cosine similarity) with performance comparison, and 4) Accuracy evaluation metrics (mean positioning error, cumulative distribution functions). This framework allows researchers to simulate various real-world scenarios, test algorithm robustness under different noise conditions, and optimize fingerprint localization for applications in indoor navigation, asset tracking, and location-based services.