Particle Filter Tracking
2016-08-23
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In Kalman filtering, density propagation changes from one Gaussian to... Another Gaussian distribution. What changes are mean and variance. This is very good if our system is linear and the noise is Gaussian. But if it's not (i.e., clutter data, etc.) -- Kalman will fail this is the input of particle filter - at each stage, we create a new probability density function that can take any shape (not limited to being Gaussian). This shape consists of sampled particles, each with a given weight and cumulative density function. This will further illustrate the continuation of this worksheet. All in all - we are still calculating probabilities and a priori probabilities and from the middle to the back (similar to Kalman), but we use more dynamic (non Gaussian) probability functions to model the system.
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