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基于CDKF的快速协方差交叉融合跟踪算法研究
宋闯,张航,郝明瑞
0
(复杂系统控制与智能协同技术重点实验室,北京 100074)
摘要:
随着目标抗干扰能力的增强,单一寻的制导方式很难完成对目标的稳定跟踪和精确打击,需采用多种探测器作为传感器,提供多种观测数据以实现对目标的稳定跟踪和精确打击。建立了适当的目标运动模型和观测模型,利用中心差分卡尔曼滤波(CDKF)变换处理模型的非线性问题,避免了求解复杂的雅克比矩阵。对于分布式多传感器融合,传统的方法多采用协方差交叉(CI)融合方法,但是这类方法需要寻优求解。而快速协方差交叉(FCI)则不需要进行寻优过程,且计算量小。在此基础上,提出了用于多传感器目标跟踪的CDKF-FCI融合算法。最后,对算法进行了仿真分析,并进一步验证了提出算法的有效性。
关键词:  中心差分卡尔曼滤波  快速协方差交叉融合  信息融合  目标跟踪
DOI:
基金项目:国防基础科研计划(JCKY2017204B064)
Target Tracking and Fusion Algorithm Based on CDKF and Fast Covariance Intersection Fusion
SONG Chuang,ZHANG Hang,HAO Ming-rui
(Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing 100074, China)
Abstract:
With the enhancement of the anti-jamming ability of the target, it is difficult to achieve stable target tracking and accurate attack by single homing guidance. Therefore, it is necessary to use a variety of detectors as sensors to provide a variety of observation data to achieve stable target tracking and accurate attack. In this paper, an appropriate target motion model and observation model are established, and the non-linear problem of the model is dealt with by using central difference Kalman filter (CDKF) transformation, avoiding solving the complex Jacobian matrix. For distributed multi-sensor fusion, the traditional method mostly uses covariance intersection (CI) fusion method, but this type of method needs the optimal solution. However, Fast cova-riance intersection (FCI) algorithm requires neither optimization process, nor large computation. On this basis, CDKF-FCI fusion algorithm for multi-sensor target tracking is proposed. Finally, the algorithm is simulated and analyzed, and the effectiveness of the algorithm is verified.
Key words:  Central difference Kalman filtering  Fast covariance cross fusion  Information fusion  Target tracking

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