filter

Derivation of the Particle Filter from Bayesian Filter

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The Particle Filter is a sequential Monte Carlo implementation of the Bayesian Filter. Unlike the Kalman Filter, it does not require linearity or Gaussian assumptions, making it suitable for highly nonlinear systems and non-Gaussian distributions. Instead of representing the belief with a mean...

Derivation of the Kalman Filter from Bayesian Filter

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The Kalman Filter is a specialized implementation of the Bayesian Filter. It is widely used in various engineering fields due to its optimality and computational efficiency when applied to linear systems with Gaussian noise. Below is a step-by-step mathematical derivation of its update equatio...