摘要
EKF滤波算法是进行目标被动定位与跟踪的重要方法之一,为了克服EKF初始化困难和减小线性化误差,对传感器的最大探测距离与最小距离所构成的距离区间实施分区划分,划分成若干个目标可能存在的小区段。针对每一个小区段,使用扩展卡尔曼滤波,完成滤波协方差的旋转变换,实现单站纯方位目标状态的估计和跟踪。经过大量仿真计算表明,该算法对滤波初值的设置有较大的选择范围,具有很好的稳定性,精度较高,实施水下目标被动跟踪具有重要意义。
The algorithm of EKF is one of the important algorithms of target passive localization and tracking during the course of the target snugly attacking. To overcome the initialization difficulty and decrease the linearization error, the range zone between the sensor maximum range and minimume range was divided into some small zones where the target may be in. The algorithm of EKF was used in each small zone and the covariance rotated matrix was implemented to be applied single platform bearings-only target states estimating and tracking. It is showed through lots of computer simulations that the algorithm has more large choosing scope for the filter initialization values, is very stable and high precision, and is significant to be applied in the underwater target passive tracking.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2006年第7期1802-1805,1814,共5页
Journal of System Simulation
关键词
单站纯方位目标跟踪
初始化
线性化
多模型
协方差旋转
Single platform bearings-only target tracking
initialization
linearization
multiple model
covariance rotated
