AIAA SPACE 2009 Conference & Exposition 14-17 September 2009, Pasadena, California AIAA 2009-6821 Space Algorithm Testbeds - Small Business Pipeline for Technology Innovation Roberta M. Ewart and Jie Z. Jacquot Development Planning Directorate, Space and Missile Systems Center, El Segundo, CA 90245 Patricia H. Lew The Aerospace Corporation, El Segundo, CA 90245 Small businesses, through the Small Business Innovation Research (SBIR) Program, have demonstrated the ability to create highly capable algorithms for space applications. In order to harness the innovation resident in these algorithms, the Space and Missile Systems Center Developmental Planning Directorate (SMC/XR) Third Generation Infrared Surveillance (3GIRS) office created an algorithm testbed, the Algorithm Development Lab (ADL). The primary objective of the ADL is to provide a neutral environment for generating, implementing, testing, and assessing the performance of ground processing algorithms for data from the type of Wide Field-of-View (WFOV) sensors planned for 3GIRS. Test chamber data and synthetic data as well as on-orbit data and multi-sensor data will be made available in the ADL. An initial set of baseline algorithms and an initial set of baseline algorithm performance evaluation tools are currently implemented. The current ADL testbed uses C++. However, wrappers are written so that Matlab routines can be called. The government will protect the intellectual properties of participants through various procedures. The ADL provides a unique opportunity for small businesses to test their algorithms on rarely accessible on-orbit data. With baseline processing algorithms in place, small businesses do not have to worry about developing algorithms for the entire processing chain and can focus on tasks within the scope of an SBIR. I. Incorporating SBIRs into Future Government Systems The SBIR program is designed to stimulate technological innovation and develop marketable products through federal funding. Under the program, companies explore the technical merit and feasibility of a technology (Phase I), and when feasible, develop it and prepare it for commercialization (Phase II). Since each investment is small, the program allows the government to explore many different ideas and novel technologies. Companies that participate in the program are often associated with research institutions; hence the SBIR program is a great way for the government to benefit from recent developments in academia. Because of the SBIR program s focus on developing technological products that can be commercialized, the economic benefits and marketability of each proposal is considered during the evaluation process and generally the products reach a Technology Readiness Level (TRL) of 5 or higher at the end of Phase II. It would be to the government s technical and financial advantage to incorporate products developed through SBIRs in its future systems. II. Algorithm Development Lab as a Government Algorithm Testbed Department of Defense (DOD) satellites provide the military and other government users with a variety of services including missile warning, surveillance, navigation, communication, and weather analysis. Increasingly, DOD satellites, such as the Global Positioning System (GPS), are also being used for personal and commercial purposes. Many DOD satellites launched within the past few decades, such as the Defense Chief Scientist, Space and Missile Systems Center, roberta.ewart@losangeles.af.mil Presidential Management Fellow, Space and Missile Systems Center, jie.jacquot@losangeles.af.mil 1 of 6 This material is declared a work of the U.S. Government and American is not subject Institute to copyright of protection Aeronautics in the and United Astronautics States.
Support Program (DSP), have long exceeded their expected life spans. However, according to a GAO study, a majority of current satellite programs encounter schedule delays and cost overruns. 1 The report mentioned factors such as regular delays in software development and the lack of thorough testing as some of the drivers behind the problems. A fair amount of research has been done under the SBIR program in the area of algorithm development for space applications. Instead of improving current ways of solving data processing problems, these algorithms explore brand new approaches. In order to objectively and systematically compare the pros and cons of different ground processing algorithms, including those developed through SBIRs as well as by governmental agencies and defense contractors, the 3GIRS office created an algorithm testbed, the Algorithm Development Lab (ADL). The ADL provides a neutral environment both in terms of hardware and software for generating, implementing, testing, and assessing the performance of ground processing algorithms. The current ADL testbed uses C++. However, wrappers are written so that Matlab routines can be called. The government will protect the intellectual properties of small businesses as well as defense contractors using procedures such as providing each company with a unique removable hard drive to load its code. As part of the 3GIRS program, the ADL will mainly be concerned with processing data collected via WFOV full earth staring sensors. Performance assessments using the ADL will help the government optimize future system designs. The 3GIRS office will use the ADL to determine necessary investments for its objective system as well as to determine contract requirements and procurement strategies. III. WFOV Full Earth Staring Sensor Data Processing Algorithms A missile defense and missile warning system is used to find, fix, track, and identify ballistic objects. To this end, such objects need to be first extracted and discriminated from their cluttered environments. Clutters in the environments arise from atmospheric and manmade structures. Sample atmospheric structures that may obscure ballistic and flying objects include clouds, terrain features, aurora, stratospheric warming, temperature inversions, and polar mesospheric clouds. As part of 3GIRS, the ADL is primarily concerned with WFOV data from a geo-synchronous orbit. Many challenges exist for processing this particular type of data. Geo-synchronous IR satellites with full earth WFOV staring sensors may observe multiple ballistic missile launch events at the same time against a densely cluttered background. The targets may be small and only occupy one or two pixels on the image plane. They may also be dim and not easily distinguishable from background clutter. These small dim targets may be moving with sub-pixel/frame velocities. Other challenges faced by WFOV data processing include: having a huge number of pixels to process, having large ground foot print in the data, and having to calibrate large format FPA sensors. In order to derive missile warning information from WFOV data, a number of algorithms have to be used to process mission data. This data processing can be divided into two stages: front-end processing and back-end processing. Front-end processes include those that are satellite/sensor specific while back-end processes include those that use information from multiple sensors such as multi-source detection and event characterization. The ADL mainly looks at the front-end processes. Conventionally, these include sensor calibration, noise suppression, jitter suppression, clutter suppression and thresholding. The collection of these algorithms is called Exceedance Generation Processing (EGP), where exceedance pixels refer to those classified as potential target pixels after the thresholding step. Other front-end processes include: Closely Spaced Objects (CSO) decomposition, sub-pixel target location determination (centroiding), Line Of Sight (LOS) determination and representative return (rep return) formation. The collection of these algorithms is called Object Dependent Processing (ODP). CSO decomposition separates closely-spaced objects that merged on the focal plane. LOS determination calculates the attitude of the focal plane. The location, time, and intensity of a point target plus associated error estimates is referred to as a rep return. Figure 1 shows the data processing chain. IV. Algorithm Development Lab Overview The ADL is located in the Aerospace Corporation, a Federally-Funded Research and Development Corporation. The primary hardware components of the ADL include: MatLab workstations, a signal processing server, runtime workstations, analysis workstations and a storage media used for archiving. Figure 2 shows the hardware configuration in the ADL. The primary software elements in the ADL include: a signal pro- 2 of 6
Figure 1. Mission Data Processing Chain cessing software that runs on Linux PCs, a signal processing algorithm database, an analysis tool software, the Red Hat Linux operating system (version 5) and system support files (e.g. device drivers etc.). Figure 3 shows the software architecture overview in the ADL. New signal processing algorithms may be developed, implemented, and tested using dedicated Matlab workstations in the ADL. Algorithms will be archived in the Algorithm Archive block shown in Figure 3. Data collected on-orbit or in test chambers as well as those synthetically generated will be deposited in the Sensor Data Archive block. Synthetic data will be generated using different sensor parameters (including optics, FPA, and readout electronics), backgrounds, target types, environments, and engagement geometries. The Signal Processing Run-Time Environment (SPRTE) shown in the center of figure 3 is the heart of data processing in the ADL. Figure 4 shows the components of SPRTE. Archived data is inserted in the Shared Memory block via the sensor data ingest process. A collection of algorithms is also inserted in the same block via their Application Program Interfaces (API). APIs facilitate the data exchange between neighboring processing steps. New algorithms can be plugged in via the appropriate APIs. Test chamber data and synthetic data as well as on-orbit data and multi-sensor data will be available in the ADL. These different types of data will have different formats and an ingest processing algorithm converts them into a standard format. The ADL can generate multiple output formats (including ASCII, binary and image formats). A set of baseline signal processing algorithms for signal calibration, jitter suppression, clutter suppression, and CSO decomposition and a set of baseline performance analysis tools are currently available in the ADL. The ADL provides a unique opportunity for small businesses to test their algorithms on rarely accessible on-orbit data and increase their TRLs. With baseline processing algorithms in place, small businesses also do not have to worry about developing algorithms for the entire processing chain and can instead focus on tasks within the scope of an SBIR. V. ADL Application to Commercially Hosted IR Payload (CHIRP) In June 2008, the Space and Missile Systems Center (SMC) at the Los Angeles Air Force Base awarded Americom Government Services (AGS) a contract to host an experimental IR sensor on a geo-synchronous 3 of 6
Figure 2. ADL Hardware Architecture Overview satellite. CHIRP Flight Demonstration program serves as part of the risk reduction effort for 3GIRS. CHIRP will demonstrate staring WFOV IR sensor technology by testing a 1/4-earth, 1-eye, WFOV IR sensor on an SAIC-developed payload with an Orbital Science Enhanced STAR 2.4 satellite bus. The intended launch window for this satellite is May - September 2010. The demonstration operation will last for one year 2. 3 CHIRP will be able to provide ADL with vital on-orbit IR data commensurate with 3GIRS performance. Data will be collected against cooperative targets and targets of opportunity under a wide range of weather and seasonal conditions. 2 Using these on-orbit data along with synthetic simulated sensor data, the ADL can then test, evaluate and compare a variety of signal processing algorithms and determine the optimum processing algorithms for IR data from WFOV staring sensors. These data will also play an important role in building the set of analysis tools used to evaluate processing algorithms. With data from CHIRP, the ADL will also be able to assess calibration algorithms for WFOV sensors and bus constraints for WFOV sensors, including LOS stability and thermal stability requirements. The Ground Network for CHIRP consists of the CHIRP Mission Operations Center (CMOC), the CHIRP Mission Analysis Center (CMAC) and the Algorithm Application Laboratory (AAL) at the Aerospace Fusion Center (AFC). CMOC performs the telemetry, tracking and commanding (TT& C), archives ephemeris data and mission data, and feeds near real time data to CMAC and AFC. CMAC performs mission planning and tasking, sensor characterization, mission data archiving, and data analysis and sends processed results to AFC. AFC fuses CHIRP IR data with other types of available data, implements algorithms developed and evaluated in the ADL, processes mission data, and archives on-orbit data. Capabilities developed in the ADL that are relevant to CHIRP will be hosted in the AAL 2. 4 Thorough testing and demonstration in the ADL will ensure that algorithms incorporated in the CHIRP Ground Network, and eventually 3GIRS are optimal and serve mission objectives. Figure 6 shows the high level layout for the CHIRP Ground Network. VI. Conclusion Small businesses have demonstrated the ability to create highly capable algorithms for space applications. In order to harness the innovation resident in these algorithms, the Space and Missile Systems Center Devel- 4 of 6
Figure 3. ADL Software Architecture Overview opmental Planning Directorate (SMC/XR) Third Generation Infrared Surveillance (3GIRS) office created an algorithm testbed, the Algorithm Development Lab (ADL). The primary objective of the ADL is to provide a neutral environment for generating, implementing, testing, and assessing the performance of ground processing algorithms for data from the type of WFOV sensors planned for 3GIRS. Test chamber data and synthetic data as well as on-orbit data and multi-sensor data will be made available in the ADL. An initial set of baseline algorithms and an initial set of baseline algorithm performance evaluation tools are currently implemented. The current ADL testbed uses C++. However, wrappers are written so that Matlab routines can be called. Performance assessments using the ADL will help the government optimize future system designs. The 3GIRS office will use the ADL to determine necessary investments for future systems as well as to determine contract requirements and procurement strategies. The ADL provides a unique opportunity for small businesses to test their algorithms on rarely accessible on-orbit data. With baseline processing algorithms in place, small businesses also do not have to worry about developing algorithms for the entire processing chain and can focus on tasks within the scope of an SBIR. Acknowledgments We thank all those from the 3GIRS office who contributed material to this paper. References 1 GAO-03-825R, Satellite Acquisition Programs, 2 June 2003. 2 Maj C. Phillips, CHIRP Overview Brief for Col Wussler, 5 June 2009. 3 J. Simonds and A. Mitchell, DoD experiments on commercial spacecraft, 2009 IEEE Aerospace Conference, Big Sky, MT, 7-14 March 2009. 4 Capt R. Orcutt and A. Ghafourian, 3GIRS ADL Architecture, OPIR Research & Development Advisory Board Meeting, Colorado Springs, CO, 10 June 2009. 5 of 6
Figure 4. Components of SPRTE 6 of 6