Space Object Material Characterization from Polarized Light Curves
Abstract
Space situational awareness (SSA) involves both knowing a resident space object’s (RSO’s) information, usually orbital position, and assessing how this information can affect its future state in relation to other RSOs. One of the main challenges in SSA is to characterize RSOs. This typically involves estimating a range of attributes, including RSO shape, mass, albedo area and material composition. These are all important to develop more accurate dynamics models for drag and solar radiation pressure (SRP) in order to provide better tracking capabilities, especially for longterm state propagations, thereby increasing SSA capabilities. This work explores the problem of surface material estimation of RSOs using polarized monochromatic light curves for providing enhanced SSA. Material composition is essential in providing accurate SRP models. The specular reflection of unpolarized light off a flat surface creates a reflection that is partially polarized. This polarization depends on the index of refraction and on the extinction coefficient of the reflecting material. Thus, the degree of polarization contains information about the surface material that is not contained in the unpolarized light curve. An initial polarized bidirectional reflectance distribution function has been developed to generate polarized light curves. A multiple model adaptive estimation approach is proposed to identify the surface material from a bank of candidate materials when specular reflection occurs. Detailed analyses of surface material observability will be done under the proposed work in order to assess the accuracy of the proposed approach. Furthermore, studies of the non Gaussian nature of the polarized measurements will be performed. The advantages of the proposed approach for material estimation over existing approaches, such as spectrometers, is that MEMS level polarimeters can be used, and unpolarized measurements are simply byproducts of the polarized measurements.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 14, 2022
- Source ID
- FA95501910409
Entities
People
- John Crassidis
Organizations
- Air Force Office of Scientific Research
- Research Foundation for the State University of New York
- United States Air Force