Mary Knapp

CommCube 1 and 2: A CubeSat series of missions to enhance communication capabilities for CubeSat

CubeSats and small satellites are becoming a way to explore space and to perform science more affordably. As the goals for these spacecraft become more ambitious in terms of physical distance (moving from Low Earth Orbit (LEO) to Geostationary Earth Orbit (GEO) or further), and of the amount of data to relay back to Earth (from Kbits to Mbits), the communication systems currently implemented will not be able to fully support those missions. Two of the possible strategies to solve this issue are the following: 1. Increasing the time available to communicate to the ground by using existing satellite networks as communication relays. 2. Equipping the satellite with a more powerful antenna compatible with the volume and mass constraints imposed by CubeSats and small satellites.

Vector antenna and maximum likelihood imaging for radio astronomy

Radio astronomy using frequencies less than ~100 MHz provides a window into non-thermal processes in objects ranging from planets to galaxies. Observations in this frequency range are also used to map the very early history of star and galaxy formation in the universe. Much effort in recent years has been devoted to highly capable low frequency ground-based interferometric arrays such as LOFAR, LWA, and MWA. Ground-based arrays, however, cannot observe astronomical sources below the ionospheric cut-off frequency of ~10 MHz, so the sky has not been mapped with high angular resolution below that frequency. The only space mission to observe the sky below the ionospheric cut-off was RAE-2, which achieved an angular resolution of ~60 degrees in 1973. This work presents alternative sensor and algorithm designs for mapping the sky both above and below the ionospheric cutoff. The use of a vector sensor, which measures the full electric and magnetic field vectors of incoming radiation, enables reasonable angular resolution (~5 degrees) from a compact sensor (~4 m) with a single phase center. A deployable version of the vector sensor has been developed to be compatible with the CubeSat form factor. Results from simulation as well as ground testing of the vector sensor are presented. A variety of imaging algorithms, including expectation-maximization (EM), space-alternating generalized expectation-maximization (SAGE), projected gradient ascent maximum likelihood (PGAML), and non-negative least squares (NNLS), have been applied to the data. The results indicate that the vector sensor can map the astronomical sky even in the presence of strong interfering signals. A conceptual design for a spacecraft to map the sky at frequencies below the ionospheric cut-off is presented. Finally, the possibility of using multiple vector sensors to form an interferometer is discussed.

Low-mass high-performance deployable optical baffle for CubeSats

An optical moon baffle for stray light attenuation was developed for use on ExoplanetSat, a 3U CubeSat being developed jointly by MIT and Draper Laboratory which aims to detect transiting exoplanets via precision photometry. This paper will discuss the optical and mechanical design of the baffle, as well as the optical performance as demonstrated through the test of a prototype. The baffle collapses to fit into a small volume around ExoplanetSats lens and deploys on orbit to a full length of 12 cm. The baffle is capable of attenuating moonlight by a factor of 105 at a lunar exclusion angle of 30 degrees.

CubeSats to support Mars exploration: Three scenarios for valuable planetary science missions

Planetary science originally tended to rely on “flagship” missions characterized by large satellites and expensive resources. Interplanetary CubeSat missions represent a radical new approach enabling high quality and impact science to be achieved with ultra-small and low-cost nanosatellites. Launched as second-class payloads, deployed in swarm-like or constellation configurations, they offer spatially distributed measurements and temporal resolution not achievable by single-monolithic-satellite platforms. Reduced development times, standardization of components and deployment systems, platform modularity have been drivers to the growth of CubeSats’ launches in Earth orbit in the last decade. Constraints in size, volume and available power on-board, still limit their capabilities for independent planetary exploration. Propulsion, communications, radiation environment protection are top three technological areas to empower for this class of small satellite to support science objectives in the near future. A set of scientific objectives for CubeSats to serve astrobiology goals and support to future human exploration on Mars was selected to the purpose of this work. Missions to accomplish orbital and atmospheric measurement, in situ analyses related to biosignatures detection and environmental characterization have been explored. Three set of mission architectures based on surface penetrators, atmosphere scouts and orbiting fleet, have been assessed in the perspective of the science return value. Mission concepts provided metrics and design options to address the stakeholders’ needs and strategic knowledge gaps, as defined by the NASA Mars Exploration Program Analysis Group’s definition of top-level required investigations.

Covariance estimation in terms of Stokes parameters with application to vector sensor imaging

Vector sensor imaging presents a challenging problem in covariance estimation when allowing arbitrarily polarized sources. We propose a Stokes parameter representation of the source covariance matrix which is both qualitatively and computationally convenient. Using this formulation, we adapt the proximal gradient and expectation maximization (EM) algorithms and apply them in multiple variants to the maximum likelihood and least squares problems. We also show how EM can be cast as gradient descent on the Riemannian manifold of positive definite matrices, enabling a new accelerated EM algorithm. Finally, we demonstrate the benefits of the proximal gradient approach through comparison of convergence results from simulated data.