摘要
在机动目标跟踪与定位中,结合EKF和自适应理论的优点和目标跟踪的非线性特征,提出了一种非线性系统的基于"当前"统计模型的自适应扩展卡尔曼滤波算法,根据机动目标的测量信息修正加速度方差,消除随机误差和噪声的干扰,提高预测的精度。通过Monte Carlo对比仿真实验表明该算法正确有效,定位精度较高,滤波效果得到改善,同时增强了稳定性,优于一般的EKF和MVEKF算法,为机动目标精确跟踪与定位的实现提供一种新的方法。
In the tracking and orientation of maneuvering targets,a new nonlinear Adaptive Extended Kalman Fiher（AEKF） algorithm based on current statistical model is proposed,due to nonlinear character of the system connect with the excellence of the extended Kalman filtering and the adaptive theory.It expresses variation of acceleration with the information of position and angle to carry out self-adaption,and eliminates random error and noise variance to elevate the accuracy of tracking.Analytic results of Monte Carlo simulation proves the AEKF algorithm is right and feasible,and the accuracy and the stability are both improved.It has better performance than the traditional EKF,Modified Variance EKF（MVEKF） algorithms.The method affordes a new application to the tracking and orientation of maneuvering targets.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第2期31-33,38,共4页
Computer Engineering and Applications
基金
中国科学院“西部之光”人才培养计划基金资助课题(No.2007414)
