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Related Concept Videos

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Frequency-Domain Interpretation of PD Control01:24

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Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
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Time and frequency -Domain Interpretation of PI Control01:27

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Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
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Time and frequency -Domain Interpretation of Phase-lead Control01:24

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Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
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Time and frequency -Domain Interpretation of Phase-lag Control01:21

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Impulse01:13

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According to Newton’s second law of motion, the rate of change of the momentum of an object is the net external force acting on it. The total change in momentum between two timepoints thus depends on both the external force acting on it and the time over which it acts. Describing this mathematically, the total change of an object’s motion is proportional to the force vector and the time over which it is applied. This product is called impulse.
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The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements
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Multirate Kalman Filter Rejects Impulse Noise in Frequency-Domain-Multiplexed Tracker Measurements.

Robert A MacLachlan1, Ralph L Hollis1, Branislav Jaramaz1

  • 1The Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

Proceedings of IEEE Sensors. IEEE International Conference on Sensors
|August 28, 2018
PubMed
Summary
This summary is machine-generated.

Frequency domain multiplexing (FDM) struggles with impulse noise. A multi-rate Kalman filter effectively detects and removes these disturbances, adapting to changing noise levels for consistent measurement accuracy.

Keywords:
Kalman filterelectromagnetic interferenceelectromagnetic trackersfrequency domain multiplexingimpulse noisemultirate filteringposition tracking

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Area of Science:

  • Signal Processing
  • Measurement Science
  • Instrumentation

Background:

  • Frequency domain multiplexing (FDM) enables simultaneous measurements in systems like optical and electromagnetic position trackers.
  • While FDM effectively rejects periodic interference, impulse disturbances pose a significant challenge, corrupting the entire spectrum briefly.
  • Existing impulse suppression methods like nonlinear blanking are difficult to implement with large dynamic ranges and small problematic noise levels.

Purpose of the Study:

  • To develop an improved method for rejecting impulse disturbances in FDM systems.
  • To enhance the robustness and accuracy of simultaneous measurements in the presence of transient noise.
  • To adaptively manage measurement noise levels for consistent output quality.

Main Methods:

  • Implementation of a multi-rate Kalman filter designed to upsample predictions to the input rate.
  • Detection and blanking of impulse departures from the predicted signal.
  • Utilizing noise levels from adjacent unused channels to estimate measurement noise for adaptive filtering.

Main Results:

  • The multi-rate Kalman filter successfully detects and blanks out impulse disturbances.
  • Adaptive noise estimation allows the Kalman filter to adjust its adaptation rate based on noise levels.
  • Consistent output noise levels are maintained, even during extended noise bursts.

Conclusions:

  • The described multi-rate Kalman filter offers an effective solution for impulse rejection in FDM systems.
  • Adaptive noise management ensures stable performance across varying noise conditions.
  • This approach improves the reliability of simultaneous measurements in challenging environments.