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Fast tracking and noise-immunised RLS algorithm based on Kalman filter

Fast tracking and noise-immunised RLS algorithm based on Kalman filter

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A new least-squares algorithm based on the Kalman filter is presented. The algorithm has a self-perturbing term added to the covariance matrix, which keeps the gain vector from going infinitely small. It not only has a fast tracking capability, but also is immunised against measurement noise. The effectiveness of the algorithm are confirmed through computer simulations.

Inspec keywords: signal processing; Kalman filters; noise; least squares approximations; covariance matrices; filtering theory

Other keywords: least-squares algorithm; self-perturbing term; fast tracking RLS algorithm; Kalman filter; measurement noise; covariance matrix; noise-immunised RLS algorithm; recursive least squares; fast tracking capability; gain vector

Subjects: Signal processing and detection; Interpolation and function approximation (numerical analysis); Information theory; Interpolation and function approximation (numerical analysis)

References

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      • E. Mosca . (1995) Optimal, predictive, and adaptive control.
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      • J. Jiang , R. Cook . Fast parameter tracking RLS algorithm with high noise immunity. Electron. Lett. , 22 , 2043 - 2045
    3. 3)
      • C.G. Goodwin , K.S. Sin . (1984) Adaptive filtering, prediction and control.
    4. 4)
      • D.J. Park , B.E. Jun . Self-perturbing recursive least squares algorithm with fast trackingcapability. Electron. Lett. , 6 , 558 - 559
http://iet.metastore.ingenta.com/content/journals/10.1049/el_19961574
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