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The conceptual design and specifications are developed for an onboard experiment data support facility (OEDSF) to provide end to end processing of data from various payloads on board space shuttles. Classical data processing requirements are defined and modeled. Onboard processing requirements are analyzed. Specifications are included for an onboard processor.
Synthetic aperture radar (SAR) imaging was the most suitable technique for achieving high resolution imagery through atmospheric cloud cover. This feature was important for planetary as well as earth orbiting satellite systems, and for military aircraft applications. Such applications require onboard data processing or wide-band data transmission systems in order to handle the large amounts of raw data produced by such systems. A number of techniques were utilized in implementing SAR processors. Frequency filtering and Fourier transform operations using both optical and digital techniques were utilized in SAR processing.
Although designed to remove organic contaminants from a variety of waste water streams, the planned U.S.- and present Russian-provided water processing systems onboard the International Space Station (ISS) have capacity limits for some of the more common volatile cleaning solvents used for housekeeping purposes. Using large quantities of volatile cleaning solvents during the ground processing and in-flight operational phases of a crewed spacecraft such as the ISS can lead to significant challenges to the water processing systems. To understand the challenges facing the management of water processing capacity, the relationship between cabin atmospheric quality and humidity condensate loading is presented. This relationship is developed as a tool to determine the cabin atmospheric loading that may compromise water processing system performance. A comparison of cabin atmospheric loading with volatile cleaning solvents from ISS, Mir, and Shuttle are presented to predict acceptable limits to maintain optimal water processing system performance.
The processing of time in the Orbiter System Services software and the associated facilities provided to the user community are described. The descriptions are directed toward showing the functional intent of the design rather than the actual implementation. Simplified flow diagrams are included. Based upon detailed analysis of a preliminary review copy of the Approach and Landing Test (ALT) System Software Detailed Design Specification and the Program Listings for Version 17 Prime, the processing of time has the potential for error free operations. The processing of time is not expected to change between ALT and the Operational Flight Test (OFT) other than differences in value of some constants for control and limit checking. Due to the dynamic nature of onboard time processing and its criticality to the successful operation of the orbiter, it is recommended that a comprehensive list of external variables, their locations, initial values, and a 'where used' listing be produced, as a by-product of the link edit process, for all non-HAL coding. In addition, a careful review of the verification test procedures for the System Services time-related software is recommended.
The Advanced Topographic Laser Altimetry System (ATLAS) is the sole instrument on the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2). Without some method of reducing the transmitted data, the volume of ATLAS telemetry would far exceed the normal X-band downlink capability or require many more ground station contacts. The ATLAS Onboard Flight Science Receiver Algorithms (hereinafter Receiver Algorithms or Algorithms) control the amount of science data that is telemetered from the instrument, limiting the data volume by distinguishing surface echoes from background noise, and allowing the instrument to telemeter data from only a small vertical region about the signal. This is accomplished through the transfer of the spacecraft's location and attitude to the instrument every second, use of an onboard Digital Elevation Model, implementation of signal processing techniques, and use of onboard relief and surface type reference maps. Extensive ground testing verified the performance of the Algorithms. On-orbit analysis shows that the Algorithms are working as expected from the ground testing; they are performing well and meeting the mission requirements.
The Information Adaptive System (IAS) is an element of the NASA End-to-End Data System (NEEDS) Phase II and is focused toward onboard image processing. The IAS is a data preprocessing system which is closely coupled to the sensor system. Some of the functions planned for the IAS include sensor response nonuniformity correction, geometric correction, data set selection, data formatting, packetization, and adaptive system control. The inclusion of these sensor data preprocessing functions onboard the spacecraft will significantly improve the extraction of information from the sensor data in a timely and cost effective manner, and provide the opportunity to design sensor systems which can be reconfigured in near real-time for optimum performance. The purpose of this paper is to present the preliminary design of the IAS and the plans for its development.
The history of attitude support activities is traced. Support for a conventional mission and for the Solar Maximum Mission (SMM) are compared. The trend is towards more onboard data processing, with the ground computer playing a supportive, rather than governing role. This eliminates the need for large amounts of data transfer, enabling rapid delivery of ancillary data products. In SMM, attitude is included in the telemetry stream. The need for ground support is unlikely to diminish in the near future, since onboard computations still have to be checked.
Employing deep-learning and artificial-intelligence (AI) techniques onboard spacecraft can dramatically improve priority data selection to ensure more effective use of the available downlink. However, deployment of effective deep-learning models requires significant training on the ground, which may not be feasible, due to limited data available in an unexplored environment. Therefore, this research explores building robust classification models for onboard data processing where training data is highly limited using transfer-learning techniques. In this paper, we focus on the use case of hyperspectral imaging for remote sensing, a domain where the high dimensionality of the data from the sensor can rapidly saturate the downlink bandwidth. With this bottleneck, there is an impending need to autonomously and robustly classify data onboard to optimize downlink of high-impact measurements, thus maximizing the scientific utility per bit transmitted to the ground. This paper examines the use of deep neural networks onboard for hyperspectral image classification in a communication-limited scenario to analyze how the models perform with limited training data. The use of transfer learning can ameliorate the issue of poor generalization by transferring features learned from training on a large source dataset for one classification task to the target classification task with limited training data. For two deep-learning models from literature, we compare the accuracy of the models trained using transfer learning to models trained from scratch using a random weight initialization with varying amounts of training data. We demonstrate the feasibility and performance of running inference of the deep-learning models on representative flight-like hardware.
Spacecraft landings on small bodies (asteroids and comets) can require target accuracies too stringent to be met using ground-based navigation alone, especially if specific landing site requirements must be met for safety or to meet science goals. In-situ optical observations coupled with onboard navigation processing can meet the tighter accuracy requirements to enable such missions. Recent developments in deep space navigation capability include a self-contained autonomous navigation system (used in flight on three missions) and a landmark tracking system (used experimentally on the Japanese Hayabusa mission). The merging of these two technologies forms a methodology to perform autonomous onboard navigation around small bodies. This paper presents an overview of these systems, as well as the results from Monte Carlo studies to quantify the achievable landing accuracies by using these methods. Sensitivity of the results to variations in spacecraft maneuver execution error, attitude control accuracy and unmodeled forces are examined. Cases for two bodies, a small asteroid and on a mid-size comet, are presented.
Advancement of our predictive capabilities will require new scientific knowledge, improvement of our modeling capabilities, and new observation strategies to generate the complex data sets needed by coupled modeling networks. New observation strategies must support remote sensing from a variety of vantage points and will include "sensorwebs" of small satellites in low Earth orbit, large aperture sensors in Geostationary orbits, and sentinel satellites at L1 and L2 to provide day/night views of the entire globe. Onboard data processing and high speed computing and communications will enable near real-time tailoring and delivery of information products (i.e., predictions) directly to users.
MIDAS is a third-generation, fast, low cost, multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from large regions with present and projected sensors. MIDAS, for example, can process a complete ERTS frame in forty seconds and provide a color map of sixteen constituent categories in a few minutes. A principal objective of the MIDAS Program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughput. The need for advanced onboard spacecraft processing of remotely sensed data is stated and approaches to this problem are described which are feasible through the use of charge coupled devices. Tentative mechanizations for the required processing operations are given in large block form. These initial designs can serve as a guide to circuit/system designers.
We report the design, development, and testing of the Sample Processor for Life on Icy Worlds (SPLIce) system, a microfluidic sample processor to enable autonomous detection of signatures of life and measurements of habitability parameters in Ocean Worlds. This monolithic fluid processing-and-handling system (Figure 1; mass 0.5 kg) retrieves a 50-L-volume sample and prepares it to supply a suite of detection instruments, each with unique preparation needs. SPLIce has potential applications in orbiter missions that sample ocean plumes, such as found in Saturns icy moon Enceladus, or landed missions on the surface of icy satellites, such as Jupiters moon Europa. Answering the question Are we alone in the universe? is captivating and exceptionally challenging. Even general criteria that define life very broadly include a significant role for water [1,2]. Searches for extinct or extant life therefore prioritize locations of abundant water whether in ancient (Mars), or present (Europa and Enceladus) times. Only two previous planetary missions had onboard fluid processing: the Viking Biology Experiments [3] and Phoenixs Wet Chemistry Laboratory (WCL) [4]. SPLIce differs crucially from those systems, including its capability to process and distribute L-volume samples and the integration autonomous control of a wide range of fluidic functions, including: 1) retrieval of fluid samples from an evacuated sample chamber; 2) onboard multi-year storage of dehydrated reagents; 3) integrated pressure, pH, and conductivity measurement; 4) filtration and retention of insoluble particles for microscopy; 5) dilution or vacuum-driven concentration of samples to accommodate instrument working ranges; 6) removal of gas bubbles from sample aliquots; 7) unidirectional flow (check valves); 8) active flow-path selection (solenoid-actuated valves); 9) metered pumping in 100 nL volume increments. The SPLIce manifold, made of three thermally fused layers of precision-machined cyclo-olefin polymer, supports all fluidic components (Figure 1) and integrated microchannels (125 x 250 m). Fluid is pumped by a stepper-motor-driven pump (Lee Co.). The functionality of the integrated MEMS pressure sensor (Honeywell) and passive check valves (Figure 2) were tested in conjunction with our newly designed integral bubble traps (Figure 3) and hydrophobic membrane-based concentrator (Figure 4). The concentrator (initially tested as a standalone component) demonstrated 5-fold vacuum-evaporative concentration. Polyethylene fused bead beds (PEFBBs; 50 porosity) store drylyophilized buffers, calibrants, and fluorescent dyes, and also promote mixing of sample with calibrant, dye, or H2O. Software-controlled automated tests demonstrated successful 1) fluid delivery to each component 2) valve and pump synchronization 3) sample aliquot delivery to instrument interface ports, and 4) rehydration of vacuum-dried fluorescent dye. In Figure 5, fluorescein on PEFBBs was rehydrated for 15 min using a pump-delivered water aliquot; it is displaced as H2O enters the bottom of the channel and pushes the dye into a check valve. Ultimately, SPLIce will fluorescently label amino acids in the sample for microchip-based electrophoretic (MCE) chiral separation and detection to seek and quantify key organic bio-signatures [5]; it will also deliver sample to a microfluidic version of WCL (mWCL) to measure soluble ions and redox-active species.
This presentation lays out the data processing products that exist and are planned for the Matsu cloud for Earth Observing 1. The presentation focuses on a new feature called co-registration of Earth Observing 1 with Landsat Global Land Survey chips.
Remotely piloted aircraft (RPA) have the potential to revolutionize local to regional data collection for geophysicists as platform and payload size decrease while aircraft capabilities increase. In particular, data from RPAs combine high-resolution imagery available from low flight elevations with comprehensive areal coverage, unattainable from ground investigations and difficult to acquire from manned aircraft due to budgetary and logistical costs. Low flight elevations are particularly important for detecting signals that decay exponentially with distance, such as electromagnetic fields. Onboard data processing coupled with high-bandwidth telemetry open up opportunities for real-time and near real-time data processing, producing more efficient flight plans through the use of payload-directed flight, machine learning and autonomous systems. Such applications not only strive to enhance data collection, but also enable novel sensing modalities and temporal resolution. NASAs Airborne Science Program has been refining the capabilities and applications of RPA in support of satellite calibration and data product validation for several decades. In this paper, we describe current platforms, payloads, and onboard data systems available to the research community. Case studies include Fluid Lensing for littoral zone 3D mapping, structure from motion for terrestrial 3D multispectral imaging, and airborne magnetometry on medium and small RPAs.
This presentation provides information on the development of a small Unmanned Aerial System(UAS) with a low power, high performance Intelligent Payload Module (IPM) and a hyperspectral imager to enable intelligent gathering of science grade vegetation data over agricultural fields at about 150 ft. The IPM performs real time data processing over the image data and then enables the navigation system to move the UAS to locations where measurements are optimal for science. This is important because the small UAS typically has about 30 minutes of battery power and therefore over large agricultural fields, resource utilization efficiency is important. The key innovation is the shrinking of the IPM and the cross communication with the navigation software to allow the data processing to interact with desired way points while using Field Programmable Gate Arrays to enable high performance on large data volumes produced by the hyperspectral imager.
This version of the SpaceCube will be a full-fledged, onboard space processing system capable of 2500+ MIPS, and featuring a number of plug-andplay gigabit and standard interfaces, all in a condensed 3x3x3 form factor [less than 10 watts and less than 3 lb (approximately equal to 1.4 kg)]. The main processing engine is the Xilinx SIRF radiation- hardened-by-design Virtex-5 FX-130T field-programmable gate array (FPGA). Even as the SpaceCube 2.0 version (currently under test) is being targeted as the platform of choice for a number of the upcoming Earth Science Decadal Survey missions, GSFC has been contacted by customers who wish to see a system that incorporates key features of the version 2.0 architecture in an even smaller form factor. In order to fulfill that need, the SpaceCube Mini is being designed, and will be a very compact and low-power system. A similar flight system with this combination of small size, low power, low cost, adaptability, and extremely high processing power does not otherwise exist, and the SpaceCube Mini will be of tremendous benefit to GSFC and its partners. The SpaceCube Mini will utilize space-grade components. The primary processing engine of the Mini is the Xilinx Virtex-5 SIRF FX-130T radiation-hardened-by-design FPGA for critical flight applications in high-radiation environments. The Mini can also be equipped with a commercial Xilinx Virtex-5 FPGA with integrated PowerPCs for a low-cost, high-power computing platform for use in the relatively radiation- benign LEOs (low-Earth orbits). In either case, this version of the Space-Cube will weigh less than 3 pounds (.1.4 kg), conform to the CubeSat form-factor (10x10x10 cm), and will be low power (less than 10 watts for typical applications). The SpaceCube Mini will have a radiation-hardened Aeroflex FPGA for configuring and scrubbing the Xilinx FPGA by utilizing the onboard FLASH memory to store the configuration files. The FLASH memory will also be used for storing algorithm and application code for the PowerPCs and the Xilinx FPGA. In addition, it will feature highspeed DDR SDRAM (double data rate synchronous dynamic random-access memory) to store the instructions and data of active applications. This version will also feature SATA-II and Gigabit Ethernet interfaces. Furthermore, there will also be general-purpose, multi-gigabit interfaces. In addition, the system will have dozens of transceivers that can support LVDS (low-voltage differential signaling), RS-422, or SpaceWire. The SpaceCube Mini includes an I/O card that can be customized to meet the needs of each mission. This version of the SpaceCube will be designed so that multiple Minis can be networked together using SpaceWire, Ethernet, or even a custom protocol. Scalability can be provided by networking multiple SpaceCube Minis together. Rigid-Flex technology is being targeted for the construction of the SpaceCube Mini, which will make the extremely compact and low-weight design feasible. The SpaceCube Mini is designed to fit in the compact CubeSat form factor, thus allowing deployment in a new class of missions that the previous SpaceCube versions were not suited for. At the time of this reporting, engineering units should be available in the summer 2012.
An effort related to that described in the preceding article focuses on developing a spaceborne processing platform for fast and accurate onboard classification of image data, a critical part of modern satellite image processing. The approach again has been to exploit the versatility of recently developed hybrid Virtex-4FX field-programmable gate array (FPGA) to run diverse science applications on embedded processors while taking advantage of the reconfigurable hardware resources of the FPGAs. In this case, the FPGA serves as a coprocessor that implements legacy C-language support-vector-machine (SVM) image-classification algorithms to detect and identify natural phenomena such as flooding, volcanic eruptions, and sea-ice break-up. The FPGA provides hardware acceleration for increased onboard processing capability than previously demonstrated in software. The original C-language program demonstrated on an imaging instrument aboard the Earth Observing-1 (EO-1) satellite implements a linear-kernel SVM algorithm for classifying parts of the images as snow, water, ice, land, or cloud or unclassified. Current onboard processors, such as on EO-1, have limited computing power, extremely limited active storage capability and are no longer considered state-of-the-art. Using commercially available software that translates C-language programs into hardware description language (HDL) files, the legacy C-language program, and two newly formulated programs for a more capable expanded-linear-kernel and a more accurate polynomial-kernel SVM algorithm, have been implemented in the Virtex-4FX FPGA. In tests, the FPGA implementations have exhibited significant speedups over conventional software implementations running on general-purpose hardware.
NASA program providing unmanned photographic spacecraft into lunar orbit, processing image onboard and then transmitting it as signals to Earth