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相关概念视频

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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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预测惯性导航系统定位错误的方法,使用基于粒子群优化算法的反向传播神经网络.

Yabo Wang1, Ruihan Jiao2, Tingxiao Wei2

  • 1Wuhan Second Ship Research and Design Institute, Wuhan 430205, China.

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概括
此摘要是机器生成的。

本研究介绍了一种粒子群优化逆向传播神经网络 (PSO-BPNN),用于减轻全球定位系统/紧缩惯性导航系统 (GPS/SINS) 在GPS中断期间的位置错误. 当GPS信号不可用时,PSO-BPNN有效地减少了导航不准确性.

关键词:
在GPS拒绝拒绝.GPS/SINS 综合导航系统的导航系统.反向传播神经网络的神经网络.粒子群集优化 粒子群集优化

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科学领域:

  • 导航系统工程 导航系统工程
  • 在导航中使用人工智能
  • 地理学工程 工程地质学

背景情况:

  • 全球定位系统/紧缩惯性导航系统 (GPS/SINS) 集成导航系统对于准确定位至关重要.
  • 拒绝GPS信号是一个重大挑战,导致集成系统的位置错误增加.
  • 减轻GPS拒绝的现有方法通常在准确性和适应性方面存在局限性.

研究的目的:

  • 开发和评估一种用于减少GPS/SINS在GPS拒绝期间的位置错误的新方法.
  • 提出一个粒子集群优化-反向传播神经网络 (PSO-BPNN) 模型作为定位的GPS替代品.
  • 与标准的逆向传播神经网络 (BPNN) 相比,评估PSO-BPNN模型的有效性.

主要方法:

  • 开发了一个PSO-BPNN模型来预测和补偿位置错误.
  • 该模型使用Strapdown惯性导航系统 (SINS) 数据 (位置,速度,态度) 和导航时间作为输入.
  • 通过实际的船舶实验来验证性能,比较PSO-BPNN与BPNN.

主要成果:

  • 与标准BPNN相比,PSO-BPNN模型显示位置错误的显著减少.
  • 拟议的方法有效地弥补了模拟GPS信号拒绝期间的导航不准确性.
  • 实验结果证实了PSO-BPNN在无GPS环境中的优越性能.

结论:

  • 在GPS拒绝期间,PSO-BPNN是减轻GPS/SINS位置错误的可行和有效方法.
  • 这种方法在具有挑战性的信号环境中提供了更好的导航精度和可靠性.
  • 这些发现有助于提高自主导航系统的稳定性.