摘要
针对空间机动目标的相对导航问题展开讨论,提出了基于UKF的空间机动目标相对导航自适应滤波方法。文章从机动目标的检测和滤波器参数的调整两方面入手,首先使用统计学方法处理机动判据归一化新息加权平方和,以避免虚警并使目标检测更为有效,然后设计一套放大状态估计协方差矩阵的滤波器参数自调整策略,实现在检测到目标机动后滤波器能够快速收敛并持续稳定跟踪。仿真验证所设计的滤波器较传统的UKF能够更好地完成机动目标的相对导航任务。
Aim. The introduction of the full paper reviews a number of papers in the open literafure and then proposes the research mentioned in the title, which is explained in sections 1 and 2. Section 1 briefs the dynamic equations and measurement equations of relative motion. The core of section 2 consists of： （ 1 ） we use statistical technique to process the normalized innovation squared to avoid false alarm better; eqs. （23）, （24） and （25） are worth noticing; （2） we design the covariance matrix of auto-magnified state estimate in order to accelerate the convergence of the filter as soon as the target＇ s maneuver is detected ; eqs. （26） and （27） are worth noticing. Simulation results, presented in Figs. 2 and 3, show preliminarily that our new auto-adaptation unscented Kalman filter is indeed better for tracking maneuvering target in special relative navigation than traditional unscented Kalman filter.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2011年第4期564-568,共5页
Journal of Northwestern Polytechnical University
基金
空间智能控制技术国防科技重点实验基金(SIC07020301)资助
关键词
相对导航
机动目标
机动检测
UKF
自适应滤波
tracking （position）, Kalman filtering, navigation, algorithms, statistics, special relative navigation,maneuvering target, maneuver detection, unscented Kalman filter, auto-adaptation filter
