Understanding Satellite Characterization Knowledge Gained from Radiometric Data
Abstract
This paper presents a framework for determining satellite characterization knowledge, in the form of estimated parameter uncertainties, from radiometric observation type, quantity, quality, and in combinations. The approach combines complex forward modeling capability with an Unscented Kalman Filter (UKF) to map observation uncertainties into satellite characterization parameter space. These parameters can include size, shape, orientation, material properties, etc., and the observations can include broadband or narrowband spectral radiometry, spatially resolved or non-resolved imagery, and passive or active optical data. In order to demonstrate the effectiveness of the technique the example of using photometric light curve observations to estimate the orientation of a cube is presented. This example is chosen since the orientation uncertainty can be analytically traced from basic radiometry equations and compared to the results of the UKF. The uncertainties can also be tested through Monte Carlo analysis in which simulations are performed 10 times in order to compare observed estimation error sample statistics to the uncertainty predicted by the UKF. There are many optical sensors available and proposed to provide satellite characterization information. Understanding the information content in these data, which this approach provides, allows users to predict the amount and type of data required to obtain desired satellite characterization knowledge as well as provides direction for high pay-off future sensor development efforts.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2011
- Accession Number
- ADA550647
Entities
People
- Andrew Harms
- Charles J. Wetterer
- Chris Sabol
- Kim Luu
- Kris Hamada
- Kyle T. Allfriend
Organizations
- Princeton University