Implementation of a Generic SIR Filter for Nonlinear Non-Gaussian State Estimation
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Resource Overview
This demonstration illustrates the implementation of a generic Sequential Importance Resampling (SIR) filter - also known as particle, bootstrap, or Monte Carlo filter - for estimating hidden states in nonlinear, non-Gaussian state space models, complete with algorithm explanations and code implementation details.
Detailed Documentation
This demonstration showcases how to implement a generic Sequential Importance Resampling (SIR) filter, commonly referred to as particle filter, bootstrap filter, or Monte Carlo filter, for estimating hidden states in nonlinear, non-Gaussian state space models. The implementation covers fundamental principles and procedural steps, accompanied by detailed code examples and practical application scenarios. Key algorithmic components include importance sampling for particle propagation, weight calculation based on likelihood functions, and systematic resampling to mitigate particle degeneracy. Through studying this demonstration, you will gain practical skills for applying filter algorithms to real-world problems while developing a comprehensive understanding of SIR filter operational mechanisms and advantages, including its capability to handle complex probability distributions through discrete particle approximations.
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