IEEE Transactions on Automatic Control, Vol.64, No.12, 5260-5264, 2019
Non-Gaussian Filter for Continuous-Discrete Models
We propose a new particle filter for nonlinear continuous-discrete models. The proposed filter is based on the multiple distribution estimation with a bank of extended Kalman-Bucy filters. Compared to the simple application of a particle filter, i.e., the bootstrap filter (Monte Carlo filter), to the continuous-discrete models, the proposed filter retains superior integration and fewer particle impoverishment properties. The performance of the proposed filter is also verified using the benchmark simulation model of satellite re-entry.
Keywords:Mathematical model;Covariance matrices;Estimation;Probability density function;Numerical models;Satellites;Monte Carlo methods;Multiple distribution estimation (MDE);non-Gaussian filter;nonlinear continuous-discrete (CD) model;particle filter (PF);satellite re-entry