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考虑坐标耦合的三维变结构多模型机动目标跟踪方法

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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.
3D Coordinate-coupled Variable Structure Multiple Model Estimator for Maneuvering Target Tracking
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.

3D Maneuvering target trackingCoordinate-couplingAdaptive turn rateVariable structure multiple modelNonlinear state estimation

张宏伟、高志坚、张翊

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中山大学航空航天学院 深圳 518107

深圳大学ATR国防科技重点实验室 深圳 518060

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

中山大学青年培育项目中国科学院空间精密测量重点实验室开放基金广东省高等学校科技创新(重点)项目

20lgpy72SPMT20220012020ZDZX1054

2024

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子:1.302
ISSN:1009-5896
年,卷(期):2024.46(8)