Anders Nervold

Sensor and computing resource management for a small satellite

A small satellite in a low-Earth orbit (e.g., approximately a 300 to 400 km altitude) has an orbital velocity in the range of 8.5 km/s and completes an orbit approximately every 90 minutes. For a satellite with minimal attitude control, this presents a significant challenge in obtaining multiple images of a target region. Presuming an inclination in the range of 50 to 65 degrees, a limited number of opportunities to image a given target or communicate with a given ground station are available, over the course of a 24-hour period. For imaging needs (where solar illumination is required), the number of opportunities is further reduced. Given these short windows of opportunity for imaging, data transfer, and sending commands, scheduling must be optimized. In addition to the high-level scheduling performed for spacecraft operations, payload-level scheduling is also required. The mission requires that images be post-processed to maximize spatial resolution and minimize data transfer (through removing overlapping regions). The payload unit includes GPS and inertial measurement unit (IMU) hardware to aid in image alignment for the aforementioned. The payload scheduler must, thus, split its energy and computing-cycle budgets between determining an imaging sequence (required to capture the highly-overlapping data required for super-resolution and adjacent areas required for mosaicking), processing the imagery (to perform the super-resolution and mosaicking) and preparing the data for transmission (compressing it, etc.). This paper presents an approach for satellite control, scheduling and operations that allows the cameras, GPS and IMU to be used in conjunction to acquire higher-resolution imagery of a target region.

A Curriculum-Integrated Small Spacecraft Program for Interdisciplinary Education

Space generates inspiration, aspiration, and passion in many students, traits that are often lacking in the traditional college classroom. By utilizing a meaningful space project with a tangible product, which serves a valuable purpose in the curriculum, instructors can generate passion in their students with regards to the topics being explored. Additionally, it can fuel interest in aerospace science and commerce, guiding more students towards valuable STEM degrees and job opportunities, which can lead to future growth and fresh blood in the aging aerospace employee pool. OpenOrbiter is a student-run research project at the University of North Dakota that can serve as a basis for developing this type of integrated interdisciplinary education. To date, it has involved over 200 students. When the design specifications, called the Open Prototype for Educational NanoSats (OPEN), are published, a cross-departmental effort towards building a CubeSat for as little as

Above the cloud computing: applying cloud computing principles to create an orbital services model

5,000 (payload excluded) in parts cost is possible. This cross-departmental effort can span across both undergraduate and graduate programs and include a large number of college departments. The professors in these departments can create suitable projects that involve the small spacecraft in their curriculum. This paper evaluates both qualitative and quantitative benefits that this type of integrated approach has in fostering interest in STEM degrees, increasing students’ enthusiasm for class materials.

OpenOrbiter: A Low-Cost, Educational Prototype CubeSat Mission Architecture

Large satellites and exquisite planetary missions are generally self-contained. They have, onboard, all of the computational, communications and other capabilities required to perform their designated functions. Because of this, the satellite or spacecraft carries hardware that may be utilized only a fraction of the time; however, the full cost of development and launch are still bone by the program. Small satellites do not have this luxury. Due to mass and volume constraints, they cannot afford to carry numerous pieces of barely utilized equipment or large antennas. This paper proposes a cloud-computing model for exposing satellite services in an orbital environment. Under this approach, each satellite with available capabilities broadcasts a service description for each service that it can provide (e.g., general computing capacity, DSP capabilities, specialized sensing capabilities, transmission capabilities, etc.) and its orbital elements. Consumer spacecraft retain a cache of service providers and select one utilizing decision making heuristics (e.g., suitability of performance, opportunity to transmit instructions and receive results – based on the orbits of the two craft). The two craft negotiate service provisioning (e.g., when the service can be available and for how long) based on the operating rules prioritizing use of (and allowing access to) the service on the service provider craft, based on the credentials of the consumer. Service description, negotiation and sample service performance protocols are presented. The required components of each consumer or provider spacecraft are reviewed. These include fully autonomous control capabilities (for provider craft), a lightweight orbit determination routine (to determine when consumer and provider craft can see each other and, possibly, pointing requirements for craft with directional antennas) and an authentication and resource utilization priority-based access decision making subsystem (for provider craft). Two prospective uses for the proposed system are presented: Earth-orbiting applications and planetary science applications. A mission scenario is presented for both uses to illustrate system functionality and operation. The performance of the proposed system is compared to traditional self-contained spacecraft performance, both in terms of task performance (e.g., how well / quickly / etc. was a given task performed) and task performance as a function of cost. The integration of the proposed service provider model is compared to other control architectures for satellites including traditional scripted control, top-down multi-tier autonomy and bottom-up multi-tier autonomy.