顺序融合用于多速率多传感器非线性动态系统,具有重尾噪声和缺失的测量
Guiting Hu1, Zhengjiang Zhang2, Luping Xu1
1School of Aerospace Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.
ISA transactions
|November 21, 2024
概括
这项研究引入了适应性顺序融合方法,用于非线性系统中的杂,不完整的数据. 拟议的过器提高了对目标跟踪等动态系统的估计准确性和可靠性.
科学领域:
- 控制系统工程 控制系统工程
- 信号处理 信号处理
- 统计推理 统计推理
背景情况:
- 序列融合估计对于多传感器非线性动态系统至关重要.
- 沉重的尾巴噪声和缺失的测量对传统的过方法构成重大挑战.
- 现有的贝叶斯推理技术经常与非静止噪声和计算错误作斗争.
研究的目的:
- 为多速率,多传感器非线性系统开发强大的顺序融合估计算法.
- 为了应对沉重的尾巴噪声和缺失的测量带来的挑战.
- 提高动态系统中状态估计的准确性和可靠性.
主要方法:
- 一个连续的学生基于t的无气味卡尔曼波器 (SSTUKF) 和它的平方根形式 (SR-SSTUKF) 被开发出来.
- 无气味变换被用来计算Student的t加权积分.
- 基于t测试的适应因子被引入,以减轻非静止噪声和计算错误的不确定性.
主要成果:
- 分析了SR-SSTUKF的计算复杂性和融合.
- 拟议的方法在敏捷目标跟踪场景中证明了有效性和稳定性.
- 模拟结果证实,具有适应因子的SR-SSTUKF显著提高了估计准确性.
结论:
- 拟议的SR-SSTUKF提供了一种可靠和准确的顺序融合估计方法,用于挑战非线性系统.
- 适应性因素有效地抑制不确定性,从而提高性能.
- 该方法对在动态环境中需要强大状态估计的应用具有前景.
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