Analysis on extended kernel recursive least squares algorithm

Pingping Zhu; José C. Prı́ncipe

doi:10.1109/ijcnn.2013.6706865

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Analysis on extended kernel recursive least squares algorithm

Pingping Zhu José C. Prı́ncipe

Year: 2013 Vol: 57 Pages: 1-4

DOI: 10.1109/ijcnn.2013.6706865

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Abstract

In this paper, the extended kernel recursive least squares (Ex-KRLS) algorithm is reviewed and analyzed. We point out that the Theorem 1 in [10] is not always correct for general cases. Furthermore, the Ex-KRLS algorithm for tracking model is just a random walk KRLS algorithm. Finally, this algorithm is explained as a special Kalman filter in the reproducing kernel Hilbert space.

Keywords:

Kernel (algebra) Algorithm Kalman filter Reproducing kernel Hilbert space Recursive least squares filter Hilbert space Kernel adaptive filter Mathematics Computer science Adaptive filter Filter (signal processing) Artificial intelligence Discrete mathematics Digital filter

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Topics

Advanced Adaptive Filtering Techniques

Physical Sciences → Engineering → Computational Mechanics

Target Tracking and Data Fusion in Sensor Networks

Physical Sciences → Computer Science → Artificial Intelligence

Blind Source Separation Techniques

Physical Sciences → Computer Science → Signal Processing

Analysis on extended kernel recursive least squares algorithm

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