基于改进集合卡尔曼滤波方法的目标运动轨迹多源异步数据融合方法研究

Research on Multi-source and Asynchronous Data Fusion of Target Trajectory Based on the Modified Ensemble Kalman Filter Method

该文构建了一个改进的多源异步观测数据情景下基于非线性运动学本构方程的集合卡尔曼滤波理论模型,该模型可以精确反演出目标运动状态参数(速度、加速度)以对目标后续运动进行预测。并基于集合卡尔曼滤波实现了多源观测数据融合,利用高精度观测数据修正低精度观测数据,修正后的数据精度可通过集合卡尔曼滤波提供的统计学信息进行标定,为非线性情形下目标轨迹多源异步数据融合问题提供了新的解决思路。

A modified Ensemble Kalman Filter （EnKF） theory model based on kinematic equations is proposed to realize the historical fitting analysis and trajectory prediction of the target trajectory in the multi-source observation data scenario. This model is applied to accurately calculate the target motion state parameters （velocity and acceleration）, then the target＇s follow-up movement is predicted. The multi-source observation data fusion is realized by using the EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF.