考虑坐标耦合的三维变结构多模型机动目标跟踪方法
3D Coordinate-coupled Variable Structure Multiple Model Estimator for Maneuvering Target Tracking
张宏伟 1高志坚 2张翊1
作者信息
- 1. 中山大学航空航天学院 深圳 518107
- 2. 深圳大学ATR国防科技重点实验室 深圳 518060
- 折叠
摘要
在3维空间机动目标跟踪过程中,目标运动先验未知和坐标耦合误差会引起运动模型-模式失配,而模型-模式失配会引起状态估计有偏.该文根据目标运动速度正交条件修正状态转移矩阵,利用原始-对偶正则约束空间测量到球面可行域,结合自适应转弯率模型和无迹卡尔曼滤波(UKF),进行模型状态滤波并融合状态估计的一致输出,推导3维变结构多模型无迹卡尔曼滤波(VSMMUKF)算法.实验结果表明,相比多模重要性无迹卡尔曼滤波(MIUKF)算法,VSMMUKF计算量相当,能够更准确地拟合3维空间点目标机动运动.相比于交互多模型最大最小粒子滤波(IMM-MPF)算法,VSMMUKF跟踪固定翼无人机(UAV)的滤波精度提升了2.8%～59.9%,整体算法负担减小了1个数量级.
Abstract
In the 3D maneuvering target tracking,unknown prior and coordinate coupling errors can cause model-mode mismatch and state estimation bias.In this paper,the state transition matrices are modified based on the target velocity-orthogonal condition,the spherical feasible domain is approximated by using the primal-dual regularization,and the adaptive turn rate model is combined in the frame of Unscented Kalman Filtering(UKF)to estimate the model-conditioned state,attaining the consistent output processing.3D Variable Structure Multi-Model UKF(VSMMUKF)algorithm is derived.Simulation results show that,compared to the Multimode Importance UKF(MIUKF)algorithm,VSMMUKF can more accurately fit the maneuvering motion of 3D spatial point target with the comparable computational complexity;Compared to the Interactive Multi-model Maximum Minimum Particle Filtering(IMM-MPF)algorithm,the filtering accuracy of VSMMUKF for tracking a fixed-wing Unmanned Aerial Vehicle(UAV)has improved by 2.8%～59.9%,and the overall computation burden has reduced an order of magnitude.
关键词
3维机动目标跟踪/坐标耦合/自适应转弯率/变结构多模型/非线性状态估计Key words
3D Maneuvering target tracking/Coordinate-coupling/Adaptive turn rate/Variable structure multiple model/Nonlinear state estimation引用本文复制引用
基金项目
中山大学青年培育项目(20lgpy72)
中国科学院空间精密测量重点实验室开放基金(SPMT2022001)
广东省高等学校科技创新(重点)项目(2020ZDZX1054)
出版年
2024
NETL
NSTL
万方数据