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Mixture generalized minimum error entropy-based distributed lattice Kalman filter.
- Source :
-
Digital Signal Processing . Jun2024, Vol. 149, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- • The mixture generalized minimum error entropy (MGMEE) criterion is designed. • A new MGMEE-DLKF is proposed based on MGMEE criterion and lattice samples. • Complexity analysis and convergence condition of the MGMEE-DLKF are given. The Gaussian kernel function-based Minimum Error Entropy (MEE) criterion is effective for special types non-Gaussian noise. However, non-Gaussian noise distributions and shapes are diverse in practice, the traditional MEE methods are difficult to fit non-Gaussian effectively due to the shape parameters of MEE cannot be adjusted. In this paper, the Mixture Generalized Minimum Error Entropy (MGMEE) criterion is proposed by a mixture generalized Gaussian kernel function. Then, a new Mixture Generalized Minimum Error Entropy-based Distributed Lattice Kalman Filter (MGMEE-DLKF) is proposed for multi-sensor nonlinear systems with non-Gaussian noise. The complexity analysis and convergence condition of proposed MGMEE-DLKF algorithm are derived. In the end, the target tracking simulations are verified for systems with mixture Gaussian noise, Rayleigh distribution noise and α − stable distribution noise. The simulation results demonstrate that the proposed filter has the smallest root mean square error. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10512004
- Volume :
- 149
- Database :
- Academic Search Index
- Journal :
- Digital Signal Processing
- Publication Type :
- Periodical
- Accession number :
- 176923393
- Full Text :
- https://doi.org/10.1016/j.dsp.2024.104508