NASA SBIR 2006 Solicitation

SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Scientific Systems Company, Inc.
500 West Cummings Park, Suite 3000
Woburn, MA 01801 - 6580
(781) 933-5355

PRINCIPAL INVESTIGATOR/PROJECT MANAGER (Name, E-mail, Mail Address, City/State/Zip, Phone)
Raman K Mehra
rkm@ssci.com
500 West Cummings Park, Suite 3000
Woburn, MA 01801 - 6580
(781) 933-5355

TECHNICAL ABSTRACT (Limit 2000 characters, approximately 200 words)
Recent work on distributed multi-spacecraft systems has resulted in a number of architectures and algorithms for accurate estimation of spacecraft and formation states. The estimation accuracy achievable during spacecraft formation operation depends not only on the algorithms, but also on their actual implementation and communication related delays. Typically, the algorithms are implemented on a real-time multi-tasking processor that allocates on-board computational resources to multiple tasks according to some scheduling policy. The processor's task scheduler may induce delays and preempt measurement processing and estimation tasks in favor of other tasks. Hence, estimation accuracy and in general the performance of any embedded algorithm can be significantly lower than expected during execution. The goal of this project is to develop distributed spacecraft state estimation algorithms that account for real-time multi-tasking processor constraints and delays in the availability of measurements and make the best use of limited onboard computing resources. We bring together new advances in advanced Kalman filtering techniques to develop an innovative framework for the design of embedded distributed state estimation algorithms and software. We will deliver to NASA JPL a novel ``any time'' Kalman Filter (AKF) architecture and software for distributed state estimation that, i) selects and uses the best measurements under given CPU constraints, and ii) continues to improve the accuracy of estimates by opportunistically using any additional CPU resources that become available.

POTENTIAL NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
Embedded control and estimation systems are used in a wide range of commercial and military applications. This SBIR effort uses a novel Anytime Kalman Filter (AKF) approach to develop state estimation algorithms for implementation using real-time multi-tasking operating systems, which has vast commercial potential for industrial (electric power networks, distributed process control) transportation and environmental monitoring applications.

POTENTIAL NON-NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
This SBIR directly addresses concerns in embedded control systems for formation flying spacecraft. NASA's Next Generation Air Transportation System will use real-time multi-tasking environment for control, estimation and integrated vehicle health monitoring tasks. Other NASA applications include embedded aircraft and jet engine control systems.

NASA's technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.

TECHNOLOGY TAXONOMY MAPPING

Architectures and Networks Attitude Determination and Control Guidance, Navigation, and Control On-Board Computing and Data Management Sensor Webs/Distributed Sensors Software Tools for Distributed Analysis and Simulation Telemetry, Tracking and Control