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Kalman Filtering and Information Fusion pp 95–118Cite as

Real-Time State Estimator Without Noise Covariance Matrices Knowledge

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Abstract

The digital filtering technology has been widely applied in a majority of signal processing applications. For the linear systems with state-space model, Kalman filter provides optimal state estimates in the sense of minimum mean squared errors and maximum likelihood estimation.

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Authors and Affiliations

  1. School of Automation, Beijing Institute of Technology, Beijing, China

    Hongbin Ma, Liping Yan, Yuanqing Xia & Mengyin Fu

Authors
  1. Hongbin Ma
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  2. Liping Yan
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  3. Yuanqing Xia
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  4. Mengyin Fu
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Correspondence to Hongbin Ma .

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Ma, H., Yan, L., Xia, Y., Fu, M. (2020). Real-Time State Estimator Without Noise Covariance Matrices Knowledge. In: Kalman Filtering and Information Fusion. Springer, Singapore. https://doi.org/10.1007/978-981-15-0806-6_6

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