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Essential for the performance of a Kalman filter is that the estimated value is as close as possible to the true value of the state. In addition, it is expected that the estimator correctly specifies the covariance of the estimation error. For both aspects, there are two key metrics: the mean squared error and the normalized estimation error squared.

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Quality Measures

  • 章节

  • 发表日期：

    2026年

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  • Sebastian Dingler,
  • Reiner Marchthaler

摘要

Essential for the performance of a Kalman filter is that the estimated value is as close as possible to the true value of the state. In addition, it is expected that the estimator correctly specifies the covariance of the estimation error. For both aspects, there are two key metrics: the mean squared error and the normalized estimation error squared.