Nonlinear sequential methods for impact probability estimation

Richard Linares; Puneet Singla; John L. Crassidis

Nonlinear sequential methods for impact probability estimation

Richard Linares
, Puneet Singla
, John L. Crassidis

Department of Mechanical and Aerospace Engineering

SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Orbit determination in application to the estimation of impact probability has the goal of determining the evolution of the state probability density function (pdf) and determining a measure of the probability of collision. Nonlinear gravitational interaction and non-conservative forces can make the pdf far from Gaussian. This work implements three nonlinear sequential estimators: the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF) and the Particle Filter (PF) to estimate the impact probability. Both the EKF and the UKF make the Gaussian assumption and this work investigates the effect of this approximation on the impact probability calculation, while the PF can work for non-Gaussian systems.

Original language	English
Title of host publication	Spaceflight Mechanics 2010 - Advances in the Astronautical Sciences
Subtitle of host publication	Proceedings of the AAS/AIAA Space Flight Mechanics Meeting
Pages	787-806
Number of pages	20
State	Published - 2010
Event	AAS/AIAA Space Flight Mechanics Meeting - San Diego, CA, United States Duration: Feb 14 2010 → Feb 17 2010

Publication series

Name	Advances in the Astronautical Sciences
Volume	136
ISSN (Print)	0065-3438

Conference

Conference	AAS/AIAA Space Flight Mechanics Meeting
Country/Territory	United States
City	San Diego, CA
Period	02/14/10 → 02/17/10

Cite this

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title = "Nonlinear sequential methods for impact probability estimation",

abstract = "Orbit determination in application to the estimation of impact probability has the goal of determining the evolution of the state probability density function (pdf) and determining a measure of the probability of collision. Nonlinear gravitational interaction and non-conservative forces can make the pdf far from Gaussian. This work implements three nonlinear sequential estimators: the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF) and the Particle Filter (PF) to estimate the impact probability. Both the EKF and the UKF make the Gaussian assumption and this work investigates the effect of this approximation on the impact probability calculation, while the PF can work for non-Gaussian systems.",

author = "Richard Linares and Puneet Singla and Crassidis, \{John L.\}",

year = "2010",

language = "English",

isbn = "9780877035602",

series = "Advances in the Astronautical Sciences",

pages = "787--806",

booktitle = "Spaceflight Mechanics 2010 - Advances in the Astronautical Sciences",

note = "AAS/AIAA Space Flight Mechanics Meeting ; Conference date: 14-02-2010 Through 17-02-2010",

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Linares, R, Singla, P & Crassidis, JL 2010, Nonlinear sequential methods for impact probability estimation. in Spaceflight Mechanics 2010 - Advances in the Astronautical Sciences: Proceedings of the AAS/AIAA Space Flight Mechanics Meeting. Advances in the Astronautical Sciences, vol. 136, pp. 787-806, AAS/AIAA Space Flight Mechanics Meeting, San Diego, CA, United States, 02/14/10.

Nonlinear sequential methods for impact probability estimation. / Linares, Richard; Singla, Puneet; Crassidis, John L.
Spaceflight Mechanics 2010 - Advances in the Astronautical Sciences: Proceedings of the AAS/AIAA Space Flight Mechanics Meeting. 2010. p. 787-806 (Advances in the Astronautical Sciences; Vol. 136).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Nonlinear sequential methods for impact probability estimation

AU - Linares, Richard

AU - Singla, Puneet

AU - Crassidis, John L.

PY - 2010

Y1 - 2010

N2 - Orbit determination in application to the estimation of impact probability has the goal of determining the evolution of the state probability density function (pdf) and determining a measure of the probability of collision. Nonlinear gravitational interaction and non-conservative forces can make the pdf far from Gaussian. This work implements three nonlinear sequential estimators: the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF) and the Particle Filter (PF) to estimate the impact probability. Both the EKF and the UKF make the Gaussian assumption and this work investigates the effect of this approximation on the impact probability calculation, while the PF can work for non-Gaussian systems.

AB - Orbit determination in application to the estimation of impact probability has the goal of determining the evolution of the state probability density function (pdf) and determining a measure of the probability of collision. Nonlinear gravitational interaction and non-conservative forces can make the pdf far from Gaussian. This work implements three nonlinear sequential estimators: the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF) and the Particle Filter (PF) to estimate the impact probability. Both the EKF and the UKF make the Gaussian assumption and this work investigates the effect of this approximation on the impact probability calculation, while the PF can work for non-Gaussian systems.

UR - https://www.scopus.com/pages/publications/80053395316

M3 - Conference contribution

AN - SCOPUS:80053395316

SN - 9780877035602

T3 - Advances in the Astronautical Sciences

SP - 787

EP - 806

BT - Spaceflight Mechanics 2010 - Advances in the Astronautical Sciences

T2 - AAS/AIAA Space Flight Mechanics Meeting

Y2 - 14 February 2010 through 17 February 2010

ER -

Nonlinear sequential methods for impact probability estimation

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