Abstract
Classical adaptive filtering algorithms with a diffusion strategy under the mean square error (MSE) criterion can face difficulties in distributed estimation (DE) over networks in a complex noise environment, such as non-zero mean non-Gaussian noise, with the object of ensuring a robust performance. In order to overcome such limitations, this paper proposes a novel robust diffusion adaptive filtering algorithm, which is developed by using a variable center generalized maximum Correntropy criterion (GMCC-VC). Generalized Correntropy with a variable center is first defined by introducing a non-zero center to the original generalized Correntropy, which can be used as robust cost function, called GMCC-VC, for adaptive filtering algorithms. In order to improve the robustness of the traditional MSE-based DE algorithms, the GMCC-VC is used in a diffusion adaptive filter to design a novel robust DE method with the adapt-then-combine strategy. This can achieve outstanding steady-state performance under non-Gaussian noise environments because the GMCC-VC can match the distribution of the noise with that of non-zero mean non-Gaussian noise. The simulation results for distributed estimation under non-zero mean non-Gaussian noise cases demonstrate that the proposed diffusion GMCC-VC approach produces a more robustness and stable performance than some other comparable DE methods.
Highlights
Distributed estimation has become an important technology
We focus on the development of a novel robust diffusion adaptive filtering algorithm based on the generalized MCC (GMCC)-VC, because the center can be located anywhere to obtain good performance for distributed estimation (DE) over network in more common situations
Based on the model mentioned above, we develop the diffusion GMCC with a variable center (GMCC-VC) (DGMCCVC) algorithm for each node k to estimate wo by maximizing a linear combination of local generalized Correntropy with a variable center within the node k’ s neighbor Nk
Summary
Distributed estimation has become an important technology. Its object is to estimate interesting and available parameters from noisy measurements using a cooperation strategy between nodes over networks for distributed network applications, such as environment monitoring, spectrum sensing, and source localization [1,2,3]. The main reason for this is that the zero-mean Gaussian function usually cannot match the error distribution well in this case To overcome this problem, a variable center was introduced into the MCC to define a novel MCC-based criterion [27], called MCC-VC. Inspired by the MCC-VC and considering the property of the GMCC, a GMCC with a variable center (GMCC-VC) was defined by the author [30], and a recursive adaptive filtering algorithm with a sparse penalty term based on GMCC-VC was developed for sparse system estimation under non-zero mean non-Gaussian environments. We focus on the development of a novel robust diffusion adaptive filtering algorithm based on the GMCC-VC, because the center can be located anywhere to obtain good performance for DE over network in more common situations.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call