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坚固的立方卡尔曼波器用于移动目标跟踪,缺少测量
Samer Sahl1,2, Enbin Song1, Dunbiao Niu3
1College of Mathematics, Sichuan University, Chengdu 610065, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
这项研究引入了一个强大的立方卡尔曼波器 (RCKF),以解决非线性系统中缺失的测量. RCKF有效地处理异常错误,在状态估计挑战中表现优于标准过器.
科学领域:
- 工程 工程师 工程师 工程师
- 控制系统 控制系统
- 信号处理 信号处理
背景情况:
- 缺失的测量在非线性系统状态估计中带来了重大挑战.
- 像传统的立方卡尔曼波器 (CKF) 这样的传统波器可能受到未知的系统模型和噪声统计数据的限制.
研究的目的:
- 提出一个强大的立方卡尔曼波器 (RCKF),将休伯的M估计与CKF整合起来.
- 为了提高缺少数据的非线性系统的状态估计精度.
主要方法:
- 在传统的立方体卡尔曼波器 (CKF) 中整合休伯的M估计理论.
- 使用共变矩阵预测用于状态估计和控制在动态错误中.
- 与扩展卡尔曼波器 (EKF),无气味卡尔曼波器 (UKF) 和CKF进行比较评估.
主要成果:
- 与EKF,EnKF和CKF相比,RCKF在处理异常错误方面表现优越.
- 使用RCKF实现了显著较低的平均根平均平方误差 (ARMSE) 和平均规范化交叉相关积分 (ANCI).
- 在单变非静态生长模型 (UNGM) 和仅轴承跟踪 (BOT) 实例中观察到一致的性能改善.
结论:
- 拟议的RCKF有效地减轻了因缺失测量和噪声偏差引起的性能下降.
- 在面临数据丢失和动态中断的非线性系统中,RCKF为状态估计提供了强大的解决方案.
- 在具有挑战性的跟踪场景中,RCKF显示出与现有的卡尔曼波器变种相比显著的优势.
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