基于特殊车辆多源信息融合的移动初始对齐方法
Zhenjun Chang1, Zhili Zhang1, Zhaofa Zhou1
1College of Missile Engineering, Xi'an Research Institute of High Technology, Xi'an 710025, China.
Entropy (Basel, Switzerland)
|March 28, 2025
概括
本研究介绍了车辆惯性导航系统的强大的移动基底自主调整,通过多源数据融合和容错策略,提高了无GPS战场环境中的准确性和适应性.
科学领域:
- 导航系统工程 导航系统工程
- 机器人技术和自主系统
- 传感器融合和信号处理
背景情况:
- 传统的固定基地对齐方法对于动态战场条件是不够的.
- 车辆惯性导航系统需要快速初始调整,特别是在全球导航卫星系统 (GNSS) 拒绝的环境中.
- 复杂的战场场景对导航系统的准确性和可靠性构成重大挑战.
研究的目的:
- 为车辆惯性导航系统开发一种强大的移动基底自主调整方法.
- 为了提高在GNSS拒绝条件下的导航参数计算和系统可靠性.
- 提高导航系统在复杂操作场景中的适应性和容错性.
主要方法:
- 实施了基于联邦卡尔曼波器的多传感器融合架构,集成计时器,激光多普勒速度计和Strapdown惯性导航系统 (SINS) 数据.
- 开发了一种双模式故障诊断和隔离机制,用于快速识别传感器故障和系统重新配置.
- 提出了一个环境适应性的动态对齐策略,以智能选择基于实时条件的最佳对齐模式.
主要成果:
- 显著提高了车辆安装在运动期间对齐的准确性.
- 实现了准确的识别,有效的隔离和随机偶然的传感器故障的重建.
- 在复杂的操作场景中证明了增强的系统适应性和稳定性.
结论:
- 拟议的方法提供了一个创新的解决方案,用于在GNSS被拒绝的环境中快速部署车辆.
- 耐故障机制和适应性策略为下一代智能导航系统提供了关键的见解.
- 这项研究解决了在具有挑战性的军事应用中对自主调整的迫切需求.
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