LRKF Revisited : The Smart Sampling Kalman Filter ( S 2 KF )

@inproceedings{Steinbring2015LRKFR,
  title={LRKF Revisited : The Smart Sampling Kalman Filter ( S 2 KF )},
  author={Jannik Steinbring and Uwe D. Hanebeck},
  year={2015}
}
We consider estimating the hidden state of a discretetime stochastic nonlinear dynamic system based on noisy measurements through Bayesian inference. This is an important problem in many fields of current research such as (extended) object and group tracking [1]—[6], human motion tracking [7], object shape estimation [5], [8], [9], robotics [10], or estimation of extrinsic camera parameters [11]. Bayesian inference is a versatile approach for performing state estimation, but in general one has… CONTINUE READING

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