This research project focuses on the issues of convergence performance degradation and weight estimation bias in finite-complexity nonlinear kernel adaptive filters caused by input noise interference. Compared to traditional linear adaptive filters, nonlinear kernel adaptive filters are more significantly affected by input noise. Existing studies primarily concentrate on bias compensation methods for linear systems, leaving research on nonlinear systems largely unexplored. This three-year project systematically investigates input noise bias compensation methods for nonlinear system identification from multiple perspectives. In the first year, the project will focus on the effects of white Gaussian input noise on nonlinear kernel adaptive filters. It will develop bias compensation techniques for Kernel Least Mean Squares (KLMS) algorithms with finite computational complexity, such as those based on random Fourier features or Nyström mapping. The study will begin by examining how input noise impacts weight updates in filters and proceed to propose bias compensation mechanisms by designing appropriate objective functions. In the second year, the research will extend to distributed sensor networks, exploring collaboration and information fusion methods among nodes in distributed environments. Since the input signals at different nodes may exhibit varying degrees of bias due to differing noise intensities, diffusion algorithms will be designed to effectively integrate information from neighboring nodes, reducing the overall network bias effect. Specific methods will include applying multi-scale kernel functions to capture both local and global characteristics, and leveraging social network theoretical models to develop interference suppression mechanisms in information propagation. Furthermore, the study will investigate dynamic weighting strategies among nodes to further enhance the system's robustness in distributed environments. In the third year, the focus will shift to addressing bias compensation issues for nonlinear kernel adaptive filters under colored input noise. Due to the temporal correlation and specific spectral characteristics of colored noise, its impact on filter performance is more complex. At this stage, an adaptive kernel bandwidth adjustment mechanism will be developed to enable the filter to dynamically adapt kernel function parameters based on noise characteristics, thereby improving system robustness
Keywords: Bias compensation, Kernel Least Mean Square (KLMS), Impulse noise
Keywords: Data Selection; Maximum Correntropy, Risk-aware; Fuzzy Risk-measurement; Impulsive Noise
Keywords: Data Selection; Maximum Correntropy, Risk-aware; Fuzzy Risk-measurement; Impulsive Noise
Keywords: Adaptive Filter, Impulse Noise, Boost Algorithm, Ensemble Learning, Fuzzy C-Means, Data-Selection.
Keywords: Adaptive filter, frequency shift, narrowband power-line communication (NB-PLC), cyclostationary impulsive noise, IEEE 1901.2, cyclostationary signal analysis, cyclic frequency offse
Keywords: Combined adaptive filter, acoustic echo cancellation, sub-band adaptive filter, impulsive noise, variable tap-length
Keywords: Wavelet OFDM, Powerline Communication, Impulsive Interference, Adaptive Receiver, IEEE P1901