Simple Particle Filter Demonstration
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This article presents an introduction to a simple particle filter demonstration. A particle filter is a sequential Monte Carlo method used for estimating system states. The algorithm operates by propagating a set of random samples (called particles) through the system model. The estimation accuracy improves as the number of particles increases, though this comes with higher computational cost. In this demonstration, we implement a basic example showing how to: - Initialize particles with random weights - Apply prediction and update steps using system dynamics - Resample particles based on likelihood calculations - Estimate system state through weighted averaging The implementation typically involves key functions like particle initialization, importance sampling, and systematic resampling. We'll explain the core algorithm workflow and provide code snippets demonstrating state prediction using motion models and measurement updates using observation likelihoods.
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