A robust correntropy based subspace tracking algorithm in impulsive noise environments |
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| Affiliation: | 1. Shenzhen Key Laboratory of Antennas and Propagation, Shenzhen University, Shenzhen, 518060, China;2. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China;1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, PR China;2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, PR China;3. IBM Research China, Haidian District, Beijing, PR China;1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, Anhui, China;2. Department of Automation, University of Science and Technology of China, Hefei 230027, Anhui, China;1. Dept. of Physics, Nelson Mandela Metropolitan University, P.O. Box 77000, Port Elizabeth, 6031, South Africa;2. Square Kilometre Array South Africa, MeerKAT Engineering Office, The Park, Pinelands, 7405, Cape Town, South Africa |
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| Abstract: | The maximum correntropy criterion (MCC) demonstrates the inherent robustness to outliers in adaptive filtering. By employing the MCC based cost function in projection approximation subspace tracking (PAST) algorithm, the MCC-PAST algorithm is deduced and utilized for the subspace tracking under impulsive noise environments. To handle the fast varying subspaces circumstances, the variable forgetting factor (VFF) technique is developed and incorporated into the MCC-PAST algorithm. To assess the robustness of the proposed MCC-PAST with VFF algorithm, SαS processes are employed to comprehensively model different scenarios of impulsive noises. The simulation results show the proposed MCC-PAST algorithm with VFF performs better than the other two PAST algorithms developed for subspace tracking in impulsive noise environments, namely, the robust PAST algorithm and the robust Kalman filter based algorithm with variable number of measurements (KFVNM), especially when the noise is extremely impulsive or the GSNR (generalized signal to noise ratio) is relatively low. |
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| Keywords: | Impulsive noise Maximum correntropy criterion Projection approximation subspace tracking Variable forgetting factor |
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