Robust Nonlinear Smoother Based on Multivariate Laplace Distribution
Outliers pose a significant threat to the stable operation of navigation and control systems with their unpre-dictability.To address the performance degradation of the traditional Gaussian-based smoothers caused by heavy-tailed measurement noise,a nonlinear smoother based on multivariate Laplace distribution is proposed.Laplace distribution is used to model the measurement noise containing random outlier disturbances,and the model parameters of the multivariate Laplace distribution are identified online based on the variational inference,so as to improve the robustness and adaptability of the smoother under heavy-tailed noise.Simulation results demonstrate that the proposed smoother effectively improves the target-tracking accuracy in heavy-tailed noise environments,which significantly outperforms the traditional Gaussian-based smoothers.
heavy-tailed distribution noisemultivariate Laplace distributionnonlinearsmoother
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