空间机动目标跟踪的改进自适应IMM算法

doi:10.12305/j.issn.1001-506X.2021.12.29

摘要/Abstract

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。

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

Abstract:

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.

Key words: maneuvering target tracking, orbital mechanics, interacting multiple model (IMM), space situational awareness, orbital maneuver

中图分类号:

V448.21

尹聚祺, 杨震, 罗亚中, 周剑勇. 空间机动目标跟踪的改进自适应IMM算法[J]. 系统工程与电子技术, 2021, 43(12): 3658-3666.

Juqi YIN, Zhen YANG, Yazhong LUO, Jianyong ZHOU. Improved adaptive IMM algorithm for space maneuvering target tracking[J]. Systems Engineering and Electronics, 2021, 43(12): 3658-3666.

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半长轴/km	偏心率	轨道倾角/(°)	升交点赤经/(°)	近地点角距/(°)	真近点角/(°)
42 165.14	0.100 2	1.37	359.12	-113.12	184.52

η取值	位置平均误差/m	速度平均误差/(m/s)	机动后开始及收敛时刻/s
0.010	23.522	2.446	304; 801
0.050	16.189	1.014	304; 804
0.100	12.551	0.586	304; 804
0.125	12.444	0.519	305; 807
0.200	11.586	0.482	306; 810
0.300	—	—	—
1.000	—	—	—

算法类型	平均误差		最大峰值误差		滤波结束时刻估计误差
算法类型	位置/m	速度/(m/s)	位置/m	速度/(m/s)	位置/m	速度/(m/s)
RNSTF	39.365	8.601	74.624	66.294	15.180	3.661
CKF	36.326	11.679	60.741	16.995	16.719	8.469
IMM	23.400	2.787	42.536	11.561	11.784	2.037
IMM-SRCKF	23.005	2.619	44.814	11.540	11.661	1.903
IMM-EKF	11.946	0.571	39.962	11.247	5.704	0.049

算法类型	平均误差		最大峰值误差		滤波结束时刻估计误差
算法类型	位置/m	速度/(m/s)	位置/m	速度/(m/s)	位置/m	速度/(m/s)
未加入速率测量	11.946	0.571	39.962	11.247	2.843	0.015
加入速率测量	8.641	0.228	37.055	9.930	2.112	0.010

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