本文選題：自適應(yīng)極大后驗(yàn)估計(jì) + 非線性系統(tǒng)��； 參考：《系統(tǒng)科學(xué)與數(shù)學(xué)》2017年08期

【摘要】：提出一種新的基于自適應(yīng)極大后驗(yàn)(AMAP)估計(jì)的空間目標(biāo)運(yùn)動(dòng)狀態(tài)確定方法,致力于削弱未知干擾對(duì)狀態(tài)估計(jì)的不利影響.針對(duì)帶有干擾的離散時(shí)間非線性隨機(jī)系統(tǒng)設(shè)計(jì)了AMAP估計(jì)算法,采用高斯-牛頓優(yōu)化方法實(shí)現(xiàn)極大后驗(yàn)(MAP)估計(jì),通過(guò)模式切換和加權(quán)融合強(qiáng)化算法的自適應(yīng)能力.基于理論分析導(dǎo)出了狀態(tài)估計(jì)均方誤差(MSE)的表達(dá)式,說(shuō)明所提算法能夠達(dá)到優(yōu)于傳統(tǒng)擴(kuò)展卡爾曼濾波(EKF)和MAP估計(jì)算法的精度.以空間目標(biāo)運(yùn)動(dòng)狀態(tài)確定系統(tǒng)為例,通過(guò)蒙特卡洛仿真驗(yàn)證了AMAP估計(jì)算法的性能優(yōu)勢(shì),不同條件下的對(duì)比研究表明,所提算法具備應(yīng)對(duì)未知干擾的自適應(yīng)能力,能夠有效提升空間目標(biāo)運(yùn)動(dòng)狀態(tài)估計(jì)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.
【作者單位】： 北京控制工程研究所;空間智能控制技術(shù)重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金(61573059,61690215) 北京市自然科學(xué)基金(4162070) 國(guó)家杰出青年科學(xué)基金(61525301)資助課題
【分類號(hào)】：O212;TN713

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