曲线拟合与卡尔曼滤波器的滤波精度评估

曲线拟合和卡尔曼滤波是2种运动目标轨迹后置处理的重要方法,利用曲线拟合方法对复杂的运动轨迹进行平滑处理时,为了提高拟合效果,往往选取阶数较高,但易造成方程求解复杂,使得拟合曲线稳定性变差。为了提高测量系统的定位精度和对机动目标的跟踪适应能力,采用卡尔曼滤波算法对弹道进行处理,对卡尔曼滤波参数进行了优化设置,设定滤波增益的下限,避免滤波出现发散现象,使得滤波器稳定性好,而且收敛和稳定时间短。仿真结果表明:采用该方法后卡尔曼滤波对于强机动特征的轨迹有较好的平滑效果和跟踪能力,而且能满足工程上应用的精度要求。

Accuracy Assessment of Filtering Based on Curve Fitting and Kalman Filter

Curve fitting and Kalman filtering are two important methods usually used in post-processing of target trajectory.When smoothing complex trajectory by using curve fitting,higher orders are selected in order to improve the fitting result,which can easily lead to complex equation solving,making the fitting curve stability deteriorate.In order to improve the positioning accuracy of the measurement system and the adaptability to maneuvering target tracking,a Kalman fitter algorithm is used to process the trajectory.Kalman filter parameters are set optimally and the lower filter gain is limited,which solves the problem of filtering instability.As a consequence,the filter can converge and stabilize in a short time.The proposed method provides good performances in aspects of smoothing effect and tracking of strong maneuvering targets.