基于CDKF的快速协方差交叉融合跟踪算法研究

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。

Target Tracking and Fusion Algorithm Based on CDKF and Fast Covariance Intersection Fusion

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.