ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

A constant gain Kalman filter approach for the prediction of re-entry of risk objects

Anilkumar, AK and Ananthasayanam, MR and Subba Rao, PV (2007) A constant gain Kalman filter approach for the prediction of re-entry of risk objects. In: Acta Astronautica, 61 (10). pp. 831-839.

[img] PDF
cost.pdf - Draft Version
Restricted to Registered users only

Download (278kB) | Request a copy
Official URL: http://www.sciencedirect.com/science?_ob=MImg&_ima...


The accurate estimation of the predicted re-entry time of decaying space debris objects is very important for proper planning of mitigation strategies and hazard assessment. This paper highlights the implementation strategies adopted for the online reentry prediction using Kalman filter approach with constant gains with the states being the semi-major axis, eccentricity and ballistic coefficient and using the measurements of the apogee height and perigee height derived from the Two Line Elements provided by agencies like USSPACECOM. Only a very simple model is utilised for the orbit propagation and a basic feature of the present approach is that any unmodellable state and measurement errors can be accounted for by adjusting the Kalman gains which are chosen based on a suitable cost function. In this paper we provide the details of validating this approach by utilising three re-entries of debris objects, namely, US Sat. No. 25947, SROSS-C2 Satellite and COSMOS 1043 rocket body. These three objects re-entered the Earth's atmosphere on 4th March 2000, 12th July 2001 and 19th January 2002, respectively.

Item Type: Journal Article
Publication: Acta Astronautica
Publisher: Elsevier
Additional Information: Copyright of this article belongs to Elsevier.
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 11 Dec 2008 05:53
Last Modified: 19 Sep 2010 04:52
URI: http://eprints.iisc.ac.in/id/eprint/16490

Actions (login required)

View Item View Item