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- The efficiency and accuracy of the filter depend mostly on the number of particles used in the estimation and on the propagation function used to re-allocate these particles at each iteration.
- In vector notation this is expressed as 6 Where 7 is an input signal vector.
- The key idea is to use samples, also called particles, to represent the posterior distribution of the state given a sequence of sensor measurements.
- Variance reduction is always realized by introducing the correlation into the sample set.
- If the functions and are linear, and if both and are Gaussian, the Kalman filter finds the exact Bayesian filtering distribution.