Privacy and Utility Aware Data Sharing for Space Situational Awareness From Ensemble and Unscented Kalman Filtering Perspective

@article{Das2021PrivacyAU,
  title={Privacy and Utility Aware Data Sharing for Space Situational Awareness From Ensemble and Unscented Kalman Filtering Perspective},
  author={Niladri Das and R. Bhattacharya},
  journal={IEEE Transactions on Aerospace and Electronic Systems},
  year={2021},
  volume={57},
  pages={1162-1176}
}
This article presents an optimization-based formulation for privacy-utility tradeoff in the ensemble and unscented Kalman filtering framework, focusing on the space situational awareness. Privacy and utility are defined in terms of lower and upper bound on the state estimation error covariance. The synthetic sensor noise is used to satisfy these bounds and is determined by solving an optimization problem. Given privacy and utility bounds, this article present optimization problem formulations… 

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