一种改进的立方卡尔曼波器状态预测方法,基于主动拒绝跟踪系统的设计
Zongzheng Sun1, Xinjian Niu1, Kai Jia1
1School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
The Review of scientific instruments
|August 6, 2024
概括
这项研究引入了一种改进的立方卡尔曼波器 (CKF) 用于主动拒绝系统目标跟踪. 改进的方法提高了状态估计的准确性,并确保在操纵过程中快速响应.
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:
- 主动拒绝系统需要精确的目标跟踪才能有效运行.
- 传统的追踪方法面临着机动目标和系统不确定性的挑战.
研究的目的:
- 开发一个改进的立方卡尔曼波器 (CKF),用于在主动拒绝系统中增强目标跟踪.
- 通过提前补偿来提高系统控制和跟踪性能.
- 提高状态估计的准确性,并确保快速响应能力.
主要方法:
- 改进的立方卡尔曼波器 (CKF) 方法用于目标跟踪.
- 使用神经网络来估计目标速度加速模型参数,修改CKF过程参数.
- 使用有限的下界方法来保持波器的增益,并确保满足精度要求.
主要成果:
- 改进的CKF方法在模拟和实验测试中表现出卓越的性能.
- 通过神经网络集成,状态估计的准确性得到了显著提高.
- 在目标机动过程中,系统的快速响应能力得到保留,满足精度要求.
结论:
- 提议的改进的CKF方法有效地解决了主动拒绝系统中的跟踪和指向挑战.
- 神经网络的集成和有限的下界方法优化了跟踪性能.
- 该方法确保系统满足设计要求的精度和响应性.
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