Resident space object tracking using an interacting multiple model mixing scheme

Quang M. Lam

doi:10.1117/12.2054999

3 June 2014 Resident space object tracking using an interacting multiple model mixing scheme

Quang M. Lam

Proceedings Volume 9085, Sensors and Systems for Space Applications VII; 90850V (2014) https://doi.org/10.1117/12.2054999
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

A multiple model estimation scheme is proposed to enhance the robustness of a resident space object (RSO) tracker subject to its maneuverability uncertainties (unplanned or unknown jet firing activities) and other system variations. The concept is based on the Interacting Multiple Model (IMM) estimation scheme. Within the IMM framework, two Extended Kalman Filter (EKF) models: (i) a 6 State (Position and Velocity of a constant orbiting RSO) EKF and (ii) a 9 state (Position, Velocity, and Acceleration of a maneuvering RSO) EKF are designed and implemented to achieve RSO maneuvering detection and enhanced tracking accuracy. The IMM estimation scheme is capable of providing enhanced state vector estimation accuracy and consistent prediction of the RSO maneuvering status, thus offering an attractive design feature for future Space Situational Awareness (SSA) missions. The design concept is illustrated using the Matlab/Based Simulation testing environment.

Citation Download Citation

Quang M. Lam "Resident space object tracking using an interacting multiple model mixing scheme", Proc. SPIE 9085, Sensors and Systems for Space Applications VII, 90850V (3 June 2014); https://doi.org/10.1117/12.2054999

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available