控制理论与应用2018，Vol.35Issue(2)：129-136,8.DOI:10.7641/CTA.2017.70116
鲁棒高斯和集合卡尔曼滤波及其在纯角度跟踪中的应用
Robust Gaussian-sum ensemble Kalman filter and its application in bearings-only tracking
摘要
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.关键词
纯角度跟踪/异常值/非高斯噪声/集合卡尔曼滤波/高斯和Key words
bearings-only tracking/outliers/non-Gaussian noise/ensemble Kalman filter/Gaussian-sum分类
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姜浩楠,蔡远利..鲁棒高斯和集合卡尔曼滤波及其在纯角度跟踪中的应用[J].控制理论与应用,2018,35(2):129-136,8.基金项目
国家自然科学基金项目(61202128),陕西省自然科学基础研究计划项目(2017JQ6056)资助.Supported by the National Natural Science Foundation of China(61202128)and the Shaanxi Province Natural Science Basic Research Project(2017JQ 6056). （61202128）
