自适应空间机动目标跟踪算法

发布时间：2018-02-13 12:54

本文关键词： 空间目标定位机动目标仅测角自适应滤波容积卡尔曼滤波强跟踪滤波渐消因子机动检测　出处：《中国空间科学技术》2017年01期 　论文类型：期刊论文

【摘要】：在空间目标跟踪问题中,目标机动导致的模型不匹配问题会导致滤波算法出现滞后现象。为了对空间机动目标进行快速跟踪,在平方根容积卡尔曼滤波(Square-root Cubature Kalman Filter,SCKF)的基础上,引入强跟踪滤波(StrongTracking Filter,STF)的思想,推导得到了次优渐消因子在SCKF中的等价描述。并通过建立以矩匹配方法为基础的自适应机制,设计了兼顾滤波精确性和鲁棒性的自适应强跟踪平方根容积卡尔曼滤波(Adaptive Strong Tracking Square-root Cubature Kalman Filter,AST-SCKF)算法。仿真结果表明,在目标机动前,AST-SCKF算法和SCKF算法的位置收敛精度相差不足1%;在目标机动后,AST-SCKF算法的位置和速度的收敛精度相对SCKF算法分别提高了95.19%和30.50%,同时,其收敛速度相对SCKF算法分别提高了57.20%和24.68%。
[Abstract]:In the problem of space target tracking, the model mismatch caused by target maneuver will lead to lag phenomenon of filtering algorithm. In order to track space maneuvering target quickly, based on square root volume Kalman filter square-root Cubature Kalman filter (SCKF). By introducing the idea of strong tracking filter (STF), the equivalent description of suboptimal fading factor in SCKF is derived, and an adaptive mechanism based on moment matching method is established. An adaptive Strong Tracking Square-root Cubature Kalman filter AST-SCKF algorithm based on adaptive Strong Tracking Square-root Cubature Kalman filter is designed, which takes into account the accuracy and robustness of the filter. The convergence accuracy of the AST-SCKF algorithm and the SCKF algorithm is less than 1, and the convergence accuracy of the AST-SCKF algorithm is 95.19% and 30.50 higher than that of the SCKF algorithm, respectively, after the target maneuver, and the convergence accuracy of the AST-SCKF algorithm is improved by 95.19% and 30.50 respectively compared with the SCKF algorithm. Compared with SCKF algorithm, the convergence rate is improved by 57.20% and 24.68, respectively.
【作者单位】：火箭军工程大学;
【基金】：国家自然科学基金“基于量子生物地理优化的低空UAV编队在线协同航迹规划方法研究”(61403399)
【分类号】：TN713

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