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不等精度弹道加权融合扩展卡尔曼滤波
WEIGHTED FUSION EXTENDED KALMAN FILTER UNDER UNEQUAL PRECISION TRAJECTORY
【摘要】 在靶场试验中,针对单台设备难以满足对目标的跟踪定位能力,采用多台经纬仪对目标联合进行跟踪并对测量数据进行事后融合处理提高跟踪精度。采用运动学模型对导弹弹道模型逼近,对多台经纬仪的冗余数据进行扩展卡尔曼滤波(Extended Kalman filter, EKF)处理,同时对单台设备的弹道参数进行解算;为了得到高精度的弹道参数,对测量中的异常数据识别并进行剔除,然后对当前时刻的弹道精度进行估计作为融合权值,进而自适应调整当前时刻的状态估计得到高精度的弹道轨迹。仿真结果验证了该算法的有效性。
【Abstract】 In the shooting range test, it is difficult for a single device to meet the tracking and positioning capabilities of the target. Aiming at this problem, this paper uses multiple theodolites to jointly track the target and conducts fusion processing for measured data to improve the tracking accuracy. The kinematics model was used to approximate the missile trajectory model, extended Kalman filter(EKF) processing was performed on the redundant data of multiple theodolites, and the ballistic parameters of a single device were solved at the same time. In order to obtain high-precision ballistic parameters, the abnormal data in the measurement were identified and eliminated. The ballistic accuracy at the current time was estimated as the fusion weight, and the state estimation at the current time was adaptively adjusted to obtain high-precision ballistic trajectory. The simulation results show the effectiveness of this algorithm.
【Key words】 Target tracking; Theodolite; Data fusion; Extended Kalman filter;
- 【文献出处】 计算机应用与软件 ,Computer Applications and Software , 编辑部邮箱 ,2023年05期
- 【分类号】TN713;TJ06
- 【下载频次】28