Inverse Extended Kalman Filter - Part II: Highly Non-Linear and Uncertain Systems

@article{Singh2022InverseEK,
  title={Inverse Extended Kalman Filter - Part II: Highly Non-Linear and Uncertain Systems},
  author={Himali Singh and Arpan Chattopadhyay and Kumar Vijay Mishra},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.06683}
}
—Recent counter-adversarial system design problems have motivated the development of inverse Bayesian filters. For example, inverse Kalman filter (I-KF) has been recently formulated to estimate the adversary’s Kalman filter tracked estimates and hence, predict the adversary’s future steps. The purpose of this paper and the companion paper (Part I) is to address the inverse filtering problem in non-linear systems by proposing an inverse extended Kalman filter (I-EKF). In a companion paper (Part I… 

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References

SHOWING 1-10 OF 42 REFERENCES

Inverse Extended Kalman Filter -- Part I: Fundamentals

—Recent advances in counter-adversarial systems have gar- nered significant research attention to inverse filtering from a Bayesian perspective. For example, interest in estimating the adversary’s

Inverse Filtering for Hidden Markov Models With Applications to Counter-Adversarial Autonomous Systems

This paper proposes an algorithm based on convex optimization for reconstructing the transition kernel, the observation likelihoods and the observations of a Bayesian filter given noisy observations of finite-state Markov chains observed in noise.

A comparison of three non-linear filters

Online Bayesian inference and learning of Gaussian-process state-space models

Trial-and-error or avoiding a guess? Initialization of the Kalman filter

RKHS based State Estimator for Radar Sensor in Indoor Application

The suitability of the RKHS based Kalman filtering approach is validated using simulations performed over three different target motion models.

Performance Analysis of the Kalman Filter With Mismatched Noise Covariances

The main contribution of this work is that the relationships between the three MSEs are disclosed from two points of views and it is found that for the case with positive (definite) deviation from the truth, the FM SE is the worst and the IMSE is the best.

Process Noise Covariance Design in Kalman Filtering via Bounds Optimization

In terms of estimation accuracy, the Kalman filter with the optimal design outperforms a robust $H_2$ filter designed to cope with an equivalent uncertainty and Monte Carlo simulations illustrate the good convergence, performance, and low sensitivity to initial guesses of the proposed method.

Robust Kalman Filtering Under Model Uncertainty: The Case of Degenerate Densities

In this article, we consider a robust state-space filtering problem in the case that the transition probability density is unknown and possibly degenerate. The resulting robust filter has a

Improved Extended Kalman Filter Design for Passive Tracking

The modifications are to the non-linearities and could in some instances be implemented by the introduction of a time decreasing amplitude dither signal in the extended Kalman filter prior to the output non- linearity.