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

作　　者：张宏伟 高志坚[2] 张翊 ZHANG Hongwei;GAO Zhijian;ZHANG Yi(School of Aeronautics and Astronautics,Sun Yat-sen University,Shenzhen 518107,China;ATR Key Laboratory,Shenzhen University,Shenzhen 518060,China)

机构地区：[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

出　　处：《电子与信息学报》2024年第8期3267-3275,共9页Journal of Electronics & Information Technology

基　　金：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)。

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。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 primaldual 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 Multimodel 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 

分 类 号：TN953[电子电信—信号与信息处理]