基于自适应极大后验估计的空间目标运动状态确定

发布时间：2018-06-30 02:16

本文选题：自适应极大后验估计 + 非线性系统　；参考：《系统科学与数学》2017年08期

【摘要】：提出一种新的基于自适应极大后验(AMAP)估计的空间目标运动状态确定方法,致力于削弱未知干扰对状态估计的不利影响.针对带有干扰的离散时间非线性随机系统设计了AMAP估计算法,采用高斯-牛顿优化方法实现极大后验(MAP)估计,通过模式切换和加权融合强化算法的自适应能力.基于理论分析导出了状态估计均方误差(MSE)的表达式,说明所提算法能够达到优于传统扩展卡尔曼滤波(EKF)和MAP估计算法的精度.以空间目标运动状态确定系统为例,通过蒙特卡洛仿真验证了AMAP估计算法的性能优势,不同条件下的对比研究表明,所提算法具备应对未知干扰的自适应能力,能够有效提升空间目标运动状态估计精度.
[Abstract]:A new adaptive maximum a posteriori (AMAP) estimation method is proposed to determine the moving state of a space target, which aims to reduce the adverse effects of unknown disturbances on state estimation. AMAP estimation algorithm is designed for discrete-time nonlinear stochastic systems with disturbance. The maximum a posteriori (map) estimation is realized by using Gao Si Newton optimization method, and the adaptive ability of the algorithm is enhanced by mode switching and weighted fusion. The expression of mean square error (MSE) of state estimation is derived based on theoretical analysis. It shows that the proposed algorithm can achieve better accuracy than the traditional extended Kalman filter (EKF) and map estimation algorithm. Taking the motion state determination system of space target as an example, the performance advantages of AMAP estimation algorithm are verified by Monte Carlo simulation. The comparative study under different conditions shows that the proposed algorithm has adaptive ability to deal with unknown interference. It can effectively improve the accuracy of motion state estimation of space targets.
【作者单位】：北京控制工程研究所;空间智能控制技术重点实验室;
【基金】：国家自然科学基金(61573059,61690215) 北京市自然科学基金(4162070) 国家杰出青年科学基金(61525301)资助课题
【分类号】：O212;TN713

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