<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Kalman Filter (KF) is the most widely used estimator to estimate and track the states of target. It works well when noise parameters and system models are well defined in advance. Its performance degrades and starts diverging when noise parameters (mainly measurement noise) changes. In the open literature available researchers has used the concept of Fractional Order Kalman Filter (FOKF) to stabilize the KF. However in the practical application there is a variation in the measurement noise, which will leads to divergence and degradation in the FOKF approach. An Innovation Adaptive Estimation (IAE) based FOKF algorithm is presented in this paper. In order to check the stability of the proposed method, Lyapunov theory is used. Position tracking simulation has been performed for performance evaluation, which shows the better result and robustness.</p></abstract>

Kalman Filter (KF) is the most widely used estimator to estimate and track the states of target. It works well when noise parameters and system models are well defined in advance. Its performance degrades and starts diverging when noise parameters (mainly measurement noise) changes. In the open literature available researchers has used the concept of Fractional Order Kalman Filter (FOKF) to stabilize the KF. However in the practical application there is a variation in the measurement noise, which will leads to divergence and degradation in the FOKF approach. An Innovation Adaptive Estimation (IAE) based FOKF algorithm is presented in this paper. In order to check the stability of the proposed method, Lyapunov theory is used. Position tracking simulation has been performed for performance evaluation, which shows the better result and robustness.

Innovation-based fractional order adaptive Kalman filter

Department of Electronics and Communication Engineering, Indian Institute of information Technology Allahabad

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Innovation-based fractional order adaptive Kalman filter"}

Kalman Filter (KF) is the most widely used estimator to estimate and track the states of target. It works well when noise parameters and system models are well...

Innovation-based fractional order adaptive Kalman filter

Data publikacji: 20 mar 2020

Zakres stron: 60 - 64

Otrzymano: 11 gru 2019

DOI: https://doi.org/10.2478/jee-2020-0009

Słowa kluczowe
adaptive estimation, fractional calculus, maximum likelihood estimation, tracking

© 2020 Ravi Pratap Tripathi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Innovation-based fractional order adaptive Kalman filter

Data publikacji: 20 mar 2020

Zakres stron: 60 - 64

Otrzymano: 11 gru 2019

DOI: https://doi.org/10.2478/jee-2020-0009

Słowa kluczoweadaptive estimation, fractional calculus, maximum likelihood estimation, tracking

© 2020 Ravi Pratap Tripathi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
adaptive estimation, fractional calculus, maximum likelihood estimation, tracking