Robust Gaussian-sum ensemble Kalman filter and its application in bearings-only tracking
JIANG Hao-nan
CAI Yuan-li
Abstract：In order to deal with the situation that measurements are easily contaminated by outliers and non-Gaussian noise,a new nonlinear filtering algorithm called the robust Gaussian-sum ensemble Kalman filter(RGSEnKF)is proposed for the bearings-only tracking problem.Firstly,the measurement update process of the ensemble Kalman filter is reformu-lated by using Huber technique so that outliers can be dealt with efficiently.Further,the improved ensemble Kalman filter is extended within a Gaussian-sum framework,the result is RGSEnKF algorithm which can handle the state estimation prob-lem of nonlinear system corrupted by non-Gaussian noise. Moreover,the new algorithm includes a range-parameterized initialization strategy and a Gaussian merging strategy. The former strategy can reduce the effect of unobservability of range in bearings-only tracking and the latter can prevent the number of Gaussian components from increasing over time. Lots of simulation results validate the effectiveness and robustness of the new algorithm.
Keywords：bearings-only trackingoutliersnon-Gaussian noiseensemble Kalman filterGaussian-sum
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 129-136 )