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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.
A new method to determine the motion state of the space target based on the adaptive maximum a posteriori (AMAP) estimation is proposed, which aims to weaken the adverse effect of the unknown interference on the state estimation.An AMAP is designed for the discrete-time nonlinear system with disturbance (MAP) estimation by using Gauss-Newton optimization method and the adaptive ability of the algorithm by means of mode switching and weighted fusion.The expression of mean square error of state estimation (MSE) is derived based on theoretical analysis, which shows that The proposed algorithm can achieve better performance than traditional Extended Kalman Filter (EKF) and MAP estimation algorithm.Using Monte Carlo simulation to verify the performance advantage of AMAP estimation algorithm, taking space target motion state determination system as an example, under different conditions Comparative studies show that the proposed algorithm has the adaptive ability to deal with unknown interference and can effectively improve the accuracy of spatial target motion estimation.