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Survey of nonlinear state estimation in aerospace systems with Gaussian priors

  • Coelho, Milca F. (LAETA/UBI - AeroG, Laboratory of Avionics and Control, Department of Aerospace Sciences, University of Beira Interior) ;
  • Bousson, Kouamana (LAETA/UBI - AeroG, Laboratory of Avionics and Control, Department of Aerospace Sciences, University of Beira Interior) ;
  • Ahmed, Kawser (LAETA/UBI - AeroG, Laboratory of Avionics and Control, Department of Aerospace Sciences, University of Beira Interior)
  • Received : 2020.01.24
  • Accepted : 2020.06.07
  • Published : 2020.11.25
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Abstract

Nonlinear state estimation is a desirable and required technique for many situations in engineering (e.g., aircraft/spacecraft tracking, space situational awareness, collision warning, radar tracking, etc.). Due to high standards on performance in these applications, in the last few decades, there was an increasing demand for methods that are able to provide more accurate results. However, because of the mathematical complexity introduced by the nonlinearities of the models, the nonlinear state estimation uses techniques that, in practice, are not so well-established which, leads to sub-optimal results. It is important to take into account that each method will have advantages and limitations when facing specific environments. The main objective of this paper is to provide a comprehensive overview and interpretation of the most well-known methods for nonlinear state estimation with Gaussian priors. In particular, the Kalman filtering methods: EKF (Extended Kalman Filter), UKF (Unscented Kalman Filter), CKF (Cubature Kalman Filter) and EnKF (Ensemble Kalman Filter) with an aerospace perspective.

Keywords

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