Satellite Positioning Technology Based on Particle Filter and Kalman Filter Algorithms

In this graduate thesis project, we investigated satellite positioning technology utilizing both Particle Filter and Kalman Filter approaches. The attached materials contain complete MATLAB implementations covering three core components: wireless channel estimation and equalization algorithms, TDOA-based distance measurement techniques, and IMM-KF filtering methods. Each module includes detailed implementations of key algorithms - the channel estimation employs adaptive filtering techniques for multipath mitigation, TDOA calculations implement cross-correlation methods for precise time measurement, and the IMM-KF combines multiple motion models with Kalman filtering for robust trajectory tracking. We hope these well-documented programs, featuring practical implementations of Bayesian estimation and statistical signal processing, will provide substantial value to developers working in wireless positioning technology. This exclusive contribution aims to support the advancement of precise localization systems through shared technical resources.