综合导航系统中非线性过算法的综述
Jiaqian Si1, Yanxiong Niu1, Botao Wang2
1School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
本研究审查了非线性过算法,以获得最佳的导航状态估计. 它分析了扩展的卡尔曼过,无气味的卡尔曼过,立方卡尔曼过,粒子过和神经网络过,以提高系统性能.
科学领域:
- 导航系统工程 导航系统工程
- 信号处理 信号处理
- 控制理论 控制理论
背景情况:
- 非线性过算法对于准确的导航状态估计至关重要.
- 集成导航系统需要强大而可靠的性能.
研究的目的:
- 调查集成导航的非线性过算法发展情况.
- 分析各种过技术的原理,应用和挑战.
- 为这些算法提供比较分析和未来前景.
主要方法:
- 扩展卡尔曼过 (EKF) 的审查.
- 对无气味卡尔曼过 (UKF) 的审查.
- 对立方卡尔曼过 (CKF) 的审查.
- 对颗粒过 (PF) 的审查.
- 对神经网络过 (NNF) 的审查.
- 适应性/强大的卡尔曼过 (KF) 的分析.
主要成果:
- 详细检查EKF,UKF,CKF,PF和NNF的原则和应用.
- 在这些非线性过算法中识别现有的问题和局限性.
- 进行比较分析,突出每个方法的优缺点.
结论:
- 非线性过算法显著影响导航系统的准确性和稳定性.
- 需要进一步的研究来应对现有的挑战,并提高算法性能.
- 对比分析为为特定导航应用选择合适的算法提供了基础.
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