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相关概念视频

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Newton's second law is applied to obtain the linear momentum in a control volume in a fluid system. According to this law, the rate of change of linear momentum is equal to the sum of external forces acting on the system. When a control volume matches the fluid system at a specific moment, the forces acting on both are identical. Reynolds transport theorem helps explain this by breaking down the system's linear momentum into two components: the rate of change of linear momentum within...
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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The first two kinematic equations have time as a variable, but the third kinematic equation is independent of time. This equation expresses final velocity as a function of the acceleration and distance over which it acts. The fourth kinematic equation does not have an acceleration term and provides the final position of the object at time t in terms of the initial and final velocities. This equation is useful when the value of the constant acceleration is unknown.
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In uniform circular motion, the particle executing circular motion has a constant speed, and the circle is at a fixed radius. However, not all circular motion occurs at a constant speed. A particle can travel in a circle and speed up or slow down, showing an acceleration in the direction of motion. In that case, the motion is called non-uniform circular motion, and an additional acceleration is introduced, which is in the direction tangential to the circle. 
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相关实验视频

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一般化交互多模型卡尔曼过算法用于在非高斯噪音下操纵目标跟踪.

Jie Wang1, Jiacheng He1, Bei Peng1

  • 1School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, PR China.

ISA transactions
|September 22, 2024
PubMed
概括

高斯混合模型卡尔曼波器 (GIMM-KF) 在非高斯噪声下增强了目标跟踪. 这种强大的算法提高了对机动目标的准确性和稳定性.

关键词:
高斯混合物模型模型的高斯混合物模型.相互作用的多个模型模型.卡尔曼过器可以过.非高斯式噪声 (Non-Gaussian noise) 是一种非高斯式噪声.

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科学领域:

  • 信号处理 信号处理
  • 估计理论估计理论
  • 目标追踪 目标追踪

背景情况:

  • 传统的交互多模型卡尔曼过 (IMM-KF) 与非高斯噪声作斗争.
  • 在非理想的噪音条件下,机动目标状态估计具有挑战性.

研究的目的:

  • 开发一个强大的状态估计算法,用于在非高斯噪声中操纵目标.
  • 提高目标追踪系统的准确性,稳定性和稳定性.

主要方法:

  • 引入高斯混合模型 (GMM) 来建模非高斯测量噪声.
  • 提出了高斯混合IMM (GIMM) 框架,用于融合运动和噪声模型切换.
  • 开发了GIMM-KF算法,将GIMM与卡尔曼过 (KF) 结合起来.

主要成果:

  • GIMM-KF算法准确地估计了在非高斯噪声下机动目标的状态.
  • 与现有方法相比,模拟和实验表明性能优越.
  • 在GIMM-KF显示增强的准确性,稳定性和强度.

结论:

  • GIMM-KF算法有效地解决了传统IMM-KF在非高斯噪声环境中的局限性.
  • 这种方法在强大的目标跟踪方面取得了重大进展.
  • 拟议的方法已被验证用于需要可靠状态估计的实际应用.