一种考虑非高斯Lévy量测噪声下的改进分数阶卡尔曼滤波

An improved Kalman filter for fractional order system with non-Gaussian measurement Lévy noise

针对量测噪声模型为非高斯Lévy噪声,研究离散线性随机分数阶系统的卡尔曼滤波设计问题.通过剔除极大值的方法得到近似高斯白噪声的Lévy噪声,基于最小二乘原理,提出一种考虑非高斯Lévy量测噪声下的改进分数阶卡尔曼滤波算法.与传统的分数阶卡尔曼滤波相比,改进的分数阶卡尔曼滤波对非高斯Lévy噪声具有更好的滤波效果.最后,通过模拟仿真验证了所提出算法的正确性和有效性.

Based on the measurement noise as the non-Gaussian Levy noise, a novel Kalman filter for the discrete linear stochastic fractional order system is proposed. By eliminating the maximum, the approximated Gaussian white noise can be obtained. Based on the principle of least square, an improved Kalman filter can be developed for the discrete linear stochastic fractional order system with measurement Levy noise. Compared to the traditional method, the proposed method gets better performance. Finally, simulation results show the effectiveness and usefulness of the proposed algorithm.