Particle filters are a set of algorithms used for estimating the state of a dynamic system from noisy observations. They work by representing the probability distribution of the system's state with a set of particles, each representing a possible state, which are updated over time based on the observed data and the system's model. This method is especially useful in situations involving non-linear systems and non-Gaussian noise, making it a key enabling technology in levels of vehicle automation.
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