摘要
针对使用模型似然函数比对传统交互多模型(interacting multiple model,IMM)算法模型转移概率实时修正存在奇异的问题,基于所提修正函数给出一种改进自适应IMM算法。首先,将白噪声模型与扩展卡尔曼滤波(extended Kalman filter,EKF)算法结合,设计了非机动模型EKF1及机动模型EKF2作为IMM算法模型集。其次,预报模型采用适应椭圆参考轨道的非线性相对轨道动力学方程以提高模型预报精度。最后,分析了速率量测信息对减小机动目标跟踪峰值误差的作用。仿真结果表明,改进的模型转移概率自适应IMM-EKF算法跟踪精度明显提高,且优于比较的现有方法;引入速率量测信息后,最大峰值误差及估计精度得到了改善。
Aiming at the singularity of real-time model transition probability correction using model likelihood function comparison with traditional interacting multiple model(IMM)algorithm,an improved adaptive IMM algorithm is proposed based on the proposed correction function.Firstly,combining the white noise model with the extended Kalman filter(EKF)algorithm,the non maneuvering model EKF1 and maneuvering model EKF2 are designed as the model set of IMM algorithm.Secondly,the prediction model adopts the nonlinear relative orbit dynamic equation suitable for the elliptical reference orbit to improve the prediction accuracy of the model.Finally,the effect of rate measurement information on reducing the peak error of maneuvering target tracking is analyzed.The simulation results show that the tracking accuracy of the improved model transition probability adaptive imm-ekf algorithm is significantly improved and better than the existing methods.After introducing the rate measurement information,the maximum peak error and estimation accuracy are improved.
作者
尹聚祺
杨震
罗亚中
周剑勇
YIN Juqi;YANG Zhen;LUO Yazhong;ZHOU Jianyong(College of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410073,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2021年第12期3658-3666,共9页
Systems Engineering and Electronics
基金
国家自然科学基金(11902347,11972044)
国防科技大学预研项目(ZK18-03-07)资助课题。
关键词
机动目标跟踪
轨道力学
交互多模型
空间态势感知
轨道机动
maneuvering target tracking
orbital mechanics
interacting multiple model(IMM)
space situational awareness
orbital maneuver
