Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Circular Orbits and Critical Velocity for Satellites01:16

Circular Orbits and Critical Velocity for Satellites

3.1K
The Moon orbits around the Earth. In turn, the Earth (and other planets) orbit the Sun. The space directly above our atmosphere is filled with artificial satellites in orbit. One can examine the circular orbit, the simplest kind of orbit, to understand the relationship between the speed and the period of planets and satellites with respect to their positions and the bodies that they orbit.
Nicolaus Copernicus (1473-1543) first suggested that the Earth and all other planets orbit the Sun in...
3.1K
Reduced Mass Coordinates: Isolated Two-body Problem01:12

Reduced Mass Coordinates: Isolated Two-body Problem

1.6K
In classical mechanics, the two-body problem is one of the fundamental problems describing the motion of two interacting bodies under gravity or any other central force. When considering the motion of two bodies, one of the most important concepts is the reduced mass coordinates, a quantity that allows the two-body problem to be solved like a single-body problem. In these circumstances, it is assumed that a single body with reduced mass revolves around another body fixed in a position with an...
1.6K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

478
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.
Here, in order to determine the magnitude of velocity and acceleration for point...
478
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.0K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.0K
Pole and System Stability01:24

Pole and System Stability

495
The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's...
495
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

590
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
590

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Biochar and compost amendments promote yield in specialty crops.

Frontiers in plant science·2026
Same journal

A New Method to Bound the Integrity Risk for Residual-Based ARAIM.

IEEE transactions on aerospace and electronic systems·2022
Same journal

Ultra-Wideband Air-to-Ground Propagation Channel Characterization in an Open Area.

IEEE transactions on aerospace and electronic systems·2021
Same journal

Omnidirectional Optical Crosslinks for CubeSats: Transmitter Optimization.

IEEE transactions on aerospace and electronic systems·2021
Same journal

Kalman Filter-based Robust Closed-loop Carrier Tracking of Airborne GNSS Radio-Occultation Signals.

IEEE transactions on aerospace and electronic systems·2020
Same journal

Hankel Matrix Rank as Indicator of Ghost in Bearing-only Tracking.

IEEE transactions on aerospace and electronic systems·2019
Same journal

Information Formulation of the UDU Kalman Filter.

IEEE transactions on aerospace and electronic systems·2019
See all related articles

Related Experiment Video

Updated: Oct 15, 2025

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
06:14

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

5.1K

Adaptive and Dynamically Constrained Process Noise Estimation for Orbit Determination.

Nathan Stacey1, Simone D'Amico1

  • 1Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, 94305 USA.

IEEE Transactions on Aerospace and Electronic Systems
|November 1, 2021
PubMed
Summary
This summary is machine-generated.

This study presents novel algorithms for online process noise covariance estimation in Kalman filters, enhancing robust orbit determination despite model uncertainties. These methods improve accuracy and reliability, especially during measurement outages.

Keywords:
adaptive Kalman filteringasteroidsorbit determinationprocess noise covariance estimationtime correlated noise

More Related Videos

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K
Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
06:22

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

5.2K

Related Experiment Videos

Last Updated: Oct 15, 2025

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
06:14

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

5.1K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K
Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
06:22

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

5.2K

Area of Science:

  • Aerospace Engineering
  • Control Theory
  • Astrodynamics

Background:

  • Orbit determination relies on Kalman filters, but uncertainties in dynamics models introduce process noise.
  • Existing methods for estimating process noise covariance often require offline tuning or a priori knowledge.
  • Current adaptive filtering techniques may be computationally intensive or unsuitable for onboard applications.

Purpose of the Study:

  • To develop new algorithms for online estimation of process noise covariance in discrete-time Kalman filters.
  • To enhance the robustness of orbit determination in the presence of dynamics model uncertainties.
  • To address limitations of existing adaptive filtering methods, including computational cost and applicability to onboard systems.

Main Methods:

  • Developed a novel approach fusing state noise compensation, dynamic model compensation, and covariance matching adaptive filtering.
  • Introduced two adaptive and dynamically constrained process noise covariance estimation techniques.
  • Ensured process noise covariance is positive semi-definite without ad hoc methods.

Main Results:

  • The proposed algorithms accurately estimate process noise covariance online.
  • New techniques demonstrate robustness and ability to extrapolate over measurement outages.
  • Validated through case studies involving a linear system and autonomous spacecraft navigation around an asteroid.

Conclusions:

  • The novel algorithms provide accurate and robust online estimation of process noise covariance for Kalman filters.
  • These methods overcome limitations of traditional techniques, offering advantages for onboard orbit determination.
  • Demonstrated effectiveness in complex scenarios, including spacecraft navigation in asteroid environments.