摘要
面向编队飞行的高精度定位需求,针对飞机机动性复杂的特点,提出了一种交互式多模型的改进自适应无迹卡尔曼滤波算法(IMM-IAUKF),首先引入改进的Sage-Husa噪声估计器,对测量时变噪声进行实时估计,增强滤波算法的实时性和稳定性;然后针对模型转移概率的先验不确定性造成的滤波精度损失问题,采用模型似然函数对模型转移概率进行自适应修正,提高了IMM算法模型匹配精度。实验结果表明,所提出的IMM-IAUKF相对传统的UKF算法有更高的收敛速度和滤波精度,能够为编队飞行提供高精度定位服务。
In the paper,facing the high-precision localization demand of formation flight,an Interacting Multiple Model Improved Adaptive Unscented Kalman Filter(IMM-IAUKF)algorithm is proposed for the characteristics of complex maneuverability of aircraft.First,an improved Sage-Husa noise estimator is introduced to estimate the time-varying noise of the measurement in real time,so as to enhance the real-time performance and stability of the filtering algorithm;Then,to address the loss of filtering accuracy caused by the a priori uncertainty of the model transition probability,the model likelihood function is used to adaptively correct the model transfer probability,which improves the model matching accuracy of the IMM algorithm.The experimental results show that the proposed IMM-IAUKF has higher convergence speed and filtering accuracy than the traditional UKF algorithm,which can provide high-precision localization service for formation flight.
作者
芦鑫元
王雪冬
LU Xinyuan;WANG Xuedong
出处
《现代导航》
2025年第4期235-241,共7页
Modern Navigation
关键词
编队飞行
交互式多模型
无迹卡尔曼滤波
转移概率矩阵
噪声估计器
Formation Flight
Interacting Multiple Model
Unscented Kalman Filter
Transition Probability Matrix
Noise Estimator
