论文标题
涉及随机微分方程的连续二藻非线性系统的状态估计方法
State Estimation Methods for Continuous-Discrete Nonlinear Systems involving Stochastic Differential Equations
论文作者
论文摘要
在这项工作中，我们介绍了涉及随机微分方程的连续二零非线性系统中状态估计的方法。我们介绍扩展的卡尔曼滤波器，无气味的卡尔曼滤波器，集合卡尔曼滤波器和粒子过滤器。我们在MATLAB中实施状态估计方法。我们在修改后的四型系统模拟中评估了方法的性能。我们为非建立系统实施状态估计方法，即使用明确的数值集成方案。扩展的卡尔曼滤波器的实现利用约瑟夫稳定形式进行数值稳定性。我们根据模拟视野的平均绝对百分比误差来评估状态估计方法的准确性。我们表明，每种方法都成功地估算了模拟修改后的四个坦克系统的状态和未衡量的干扰。最后，我们提出结论。
In this work, we present methods for state estimation in continuous-discrete nonlinear systems involving stochastic differential equations. We present the extended Kalman filter, the unscented Kalman filter, the ensemble Kalman filter, and a particle filter. We implement the state estimation methods in Matlab. We evaluate the performance of the methods on a simulation of the modified four-tank system. We implement the state estimation methods for non-stiff systems, i.e., using an explicit numerical integration scheme. The implementation of the extended Kalman filter utilises the Joseph stabilising form for numerical stability. We evaluate the accuracy of the state estimation methods in terms of the mean absolute percentage error over the simulation horizon. We show that each method successfully estimates the states and unmeasured disturbances of the simulated modified four-tank system. Finally, we present conclusions.
