Particle Filter for Direction Detection with Resampling Algorithm

This article provides a detailed explanation of the particle filter methodology for direction detection and its integrated resampling algorithms. Direction detection serves as a critical technology with broad applications in fields such as facial recognition and autonomous driving systems. Particle filtering operates as a sequential Monte Carlo method that approximates true probability distributions by incorporating noise into particle states, thereby generating accurate estimation outcomes. The resampling algorithm addresses sampling degeneracy issues in particle filters by systematically redistributing particles based on their importance weights, effectively reducing estimation bias. Implementation typically involves key functions like systematic_resample() which redistributes particles proportionally to their weights, and prediction_update() which propagates particles through state transition models with injected process noise. Through these algorithmic components, direction detection systems achieve refined analytical capabilities, significantly improving both performance metrics and estimation precision in practical applications.