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NASA Near Earth Network (NEN) Support for Lunar and L1/L2 CubeSats

The NASA Near Earth Network (NEN) consists of globally distributed tracking stations, including NASA, commercial, and partner ground stations, that are strategically located to maximize the coverage provided to a variety of orbital and suborbital missions, including those in LEO, GEO, HEO, lunar and L1/L2 orbits. The NENs future mission set includes and will continue to include CubeSat missions. The majority of the CubeSat missions destined to fly on EM-1, launching in late 2018, many in a lunar orbit, will communicate with ground based stations via X-band and will utilize the NASA Jet Propulsion Laboratory (JPL) developed IRIS radio. The NEN recognizes the important role CubeSats are beginning to play in carrying out NASAs mission and is therefore investigating the modifications needed to provide IRIS radio compatibility. With modification, the NEN could potentially expand support to the EM-1 lunar CubeSats.The NEN could begin providing significant coverage to lunar CubeSat missions utilizing three to four of the NENs mid-latitude sites. This coverage would supplement coverage provided by the JPL Deep Space Network (DSN). The NEN, with smaller apertures than DSN, provides the benefit of a larger beamwidth that could be beneficial in the event of uncertain ephemeris data. In order to realize these benefits the NEN would need to upgrade stations targeted based on coverage ability and current configuration/ease of upgrade, to ensure compatibility with the IRIS radio. In addition, the NEN is working with CubeSat radio developers to ensure NEN compatibility with alternative CubeSat radios for Lunar and L1/L2 CubeSats. The NEN has provided NEN compatibility requirements to several radio developers who are developing radios that offer lower cost and, in some cases, more capabilities with fewer constraints. The NEN is ready to begin supporting CubeSat missions. The NEN is considering network upgrades to broaden the types of CubeSat missions that can be supported and is supporting both the CubeSat community and radio developers to ensure future CubeSat missions have multiple options when choosing a network for their communications support.

Schaire, Scott↗

The Mars Reconnaissance Orbiter Mission Operations: Architecture and Approach

The Mars Reconnaissance Orbiter (MRO) was launched on August 12, 2005 by an Atlas V launch vehicle from Cape Canaveral Air Force Station. MRO will carry a rich set of science instruments to Mars and provide global, regional survey, and targeted observations. In addition, a set of engineering instruments providing optical navigation, Ka band telecommunication and UHF relay services to future Mars missions are part of the MRO payload. During the mission, the MRO operations teams are presented with two major challenges - unprecedented high data rate and data volumes, and complex science planning and resource sharing. MRO has the capability to communicate with earth at a maximum of six Megabits per second (> 50 times any previous Mars missions). With the current Deep Space Network (DSN) contact schedule of 19 eight-hour tracks per week, the baseline mission plan is for MRO to return 34 Terabits of raw science data during the two year primary science phase. Each of the science instruments has its unique requirements for global mapping, regional survey, and targeted observations. Some instruments prefer nadir-only observations, while others require off-nadir observations (especially for stereo viewing). The requirements from these Mars viewing instruments presented a significant challenge for the operations team to design the complex science planning and resource sharing/allocation process. This paper describes what MRO project is implementing to solve these challenges.

Mars Reconnaissance Orbiter (MRO)↗

Autonomy Architectures for a Constellation of Spacecraft

Until the past few years, missions typically involved fairly large expensive spacecraft. Such missions have primarily favored using older proven technologies over more recently developed ones, and humans controlled spacecraft by manually generating detailed command sequences with low-level tools and then transmitting the sequences for subsequent execution on a spacecraft controller. This approach toward controlling a spacecraft has worked spectacularly on previous missions, but it has limitations deriving from communications restrictions - scheduling time to communicate with a particular spacecraft involves competing with other projects due to the limited number of deep space network antennae. This implies that a spacecraft can spend a long time just waiting whenever a command sequence fails. This is one reason why the New Millennium program has an objective to migrate parts of mission control tasks onboard a spacecraft to reduce wait time by making spacecraft more robust. The migrated software is called a "remote agent" and has 4 components: a mission manager to generate the high level goals, a planner/scheduler to turn goals into activities while reasoning about future expected situations, an executive/diagnostics engine to initiate and maintain activities while interpreting sensed events by reasoning about past and present situations, and a conventional real-time subsystem to interface with the spacecraft to implement an activity's primitive actions. In addition to needing remote planning and execution for isolated spacecraft, a trend toward multiple-spacecraft missions points to the need for remote distributed planning and execution. The past few years have seen missions with growing numbers of probes. Pathfinder has its rover (Sojourner), Cassini has its lander (Huygens), and the New Millenium Deep Space 3 (DS3) proposal involves a constellation of 3 spacecraft for interferometric mapping. This trend is expected to continue to progressively larger fleets. For example, one mission proposed to succeed DS3 would have 18 spacecraft flying in formation in order to detect earth-sized planets orbiting other stars. A proposed magnetospheric constellation would involve 5 to 500 spacecraft in Earth orbit to measure global phenomena within the magnetosphere. This work describes and compares three autonomy architectures for a system that continuously plans to control a fleet of spacecraft using collective mission goals instead of goals or command sequences for each spacecraft. A fleet of self-commanding spacecraft would autonomously coordinate itself to satisfy high level science and engineering goals in a changing partially-understood environment making feasible the operation of tens or even a hundred spacecraft (such as for interferometry or plasma physics missions). The easiest way to adapt autonomous spacecraft research to controlling constellations involves treating the constellation as a single spacecraft. Here one spacecraft directly controls the others as if they were connected. The controlling "master" spacecraft performs all autonomy reasoning, and the slaves only have real-time subsystems to execute the master's commands and transmit local telemetry/observations. The executive/diagnostics module starts actions and the master's real-time subsystem controls the action either locally or remotely through a slave. While the master/slave approach benefits from conceptual simplicity, it relies on an assumption that the master spacecraft's executive can continuously monitor the slaves' real-time subsystems, and this relies on high-bandwidth highly-reliable communications. Since unintended results occur fairly rarely, one way to relax the bandwidth requirements involves only monitoring unexpected events in spacecraft. Unfortunately, this disables the ability to monitor for unexpected events between spacecraft and leads to a host of coordination problems among the slaves. Also, failures in the communications system can result in losing slaves. The other two architectures improve robustness while reducing communications by progressively distributing more of the other three remote agent components across the constellation. In a teamwork architecture, all spacecraft have executives and real-time subsystems - only the leader has the planner/scheduler and mission manager. Finally, distributing all remote agent components leads to a peer-to-peer approach toward constellation control.

Barrett, Anthony↗

DSN Data Visualization Suite

The DSN Data Visualization Suite is a set of computer programs and reusable Application Programming Interfaces (APIs) that assist in the visualization and analysis of Deep Space Network (DSN) spacecraft-tracking data, which can include predicted and actual values of downlink frequencies, uplink frequencies, and antenna-pointing angles in various formats that can include tables of values and polynomial coefficients. The data can also include lists of antenna-pointing events, lists of antenna- limit events, and schedules of tracking activities. To date, analysis and correlation of these intricately related data before and after tracking have been difficult and time-consuming. The DSN Data Visualization Suite enables operators to quickly diagnose tracking-data problems before, during, and after tracking. The Suite provides interpolation on demand and plotting of DSN tracking data, correlation of all data on a given temporal point, and display of data with color coding configurable by users. The suite thereby enables rapid analysis of the data prior to transmission of the data to DSN control centers. At the control centers, the same suite enables operators to validate the data before committing the data to DSN subsystems. This software is also Web-enabled to afford its capabilities to international space agencies.

Bui, Bach X.↗

NASA Near Earth Network (NEN) Support for Lunar and L1/L2 CubeSats

The NASA Near Earth Network (NEN) consists of globally distributed tracking stations, including NASA, commercial, and partner ground stations, that are strategically located to maximize the coverage provided to a variety of orbital and suborbital missions, including those in LEO, GEO, HEO, lunar and L1/L2 orbits. The NENs future mission set includes and will continue to include CubeSat missions. The first NEN supported CubeSat mission will be the Cubesat Proximity Operations Demonstration (CPOD) launching into low earth orbit (LEO) in early 2017. The majority of the CubeSat missions destined to fly on EM-1, launching in late 2018, many in a lunar orbit, will communicate with ground based stations via X-band and will utilize the NASA Jet Propulsion Laboratory (JPL) developed IRIS radio. The NEN recognizes the important role CubeSats are beginning to play in carrying out NASAs mission and is therefore investigating the modifications needed to provide IRIS radio compatibility. With modification, the NEN could potentially expand support to the EM-1 lunar CubeSats. The NEN could begin providing significant coverage to lunar CubeSat missions utilizing three to four of the NENs mid-latitude sites. This coverage would supplement coverage provided by the JPL Deep Space Network (DSN). The NEN, with smaller apertures than DSN, provides the benefit of a larger beamwidth that could be beneficial in the event of uncertain ephemeris data. In order to realize these benefits the NEN would need to upgrade stations targeted based on coverage ability and current configurationease of upgrade, to ensure compatibility with the IRIS radio.In addition, the NEN is working with CubeSat radio developers to ensure NEN compatibility with alternative CubeSat radios for Lunar and L1/L2 CubeSats. The NEN has provided NEN compatibility requirements to several radio developers who are developing radios that offer lower cost and, in some cases, more capabilities with fewer constraints. The NEN is ready to begin supporting CubeSat missions. The NEN is considering network upgrades to broaden the types of CubeSat missions that can be supported and is supporting both the CubeSat community and radio developers to ensure future CubeSat missions have multiple options when choosing a network for their communications support.

Global Tracking Network Ground Station↗

GPS and Galileo Developments on Board the International Space Station With the Space Communications and Navigation (SCaN) Testbed

The Space Communications and Navigation (SCaN) is a facility developed by NASA and hosted on board the International Space Station (ISS) on an external truss since 2013.It has the objective of testing navigation and communication experimentations with a Software Defined Radio (SDR) approach, which permits software updates for testing new experimentations.NASA has developed the Space Telecommunications Radio System (STRS) architecture standard for SDRs used in space and ground-based platforms to provide commonality among radio developments to provide enhanced capability. The hardware is equipped with both L band front-end radios and the NASA space network communicates with it using S-band, Ku-band and Ka-band links.In May 2016 Qascom started GARISS (GPS and Galileo Receiver for the ISS), an activity of experimentation in collaboration with ESA and NASA that has the objective to develop and validate the acquisition and processing of combined GPS and Galileo signals on board the ISS SCaN testbed. This paper has the objective to present the mission, and provide preliminary details about the challenges in the design, development and verification of the waveform that will be installed on equipment with limited resources. GARISS is also the first attempt to develop a waveform for the ISS as part of an international collaboration between US and Europe. Although the final mission objective is to target dual frequency processing, initial operations will foresee a single frequency processing. Initial results and trade-off between the two options, as well as the final decision will be presented and discussed. The limited resources on board the SCaN with respect to the challenging requirements to acquire and track contemporaneously two satellite navigation systems, with different modulations and data structure, led to the need to assess the possibility of aiding from ground through the S-band. This option would allow assistance to the space receiver in order to provide knowledge of GNSS orbits and reduce the processing on board. Trade off and various options for telemetry and uplink data are presented and discussed. Finally, integration and validation of the waveform are one of the major challenges of GARISS: The Experiment Development System (EDS) and the the Ground Integration Unit (GIU) for VV will be used prior to conducting the experiment on the ISS. The EDS can be used in lab environment and allows prototyping and verification activities with the simulator, but does not include all hardware components. The GIU on the other side is the flight model which replicates the flying equipment, but has limited flexibility for testing.As conclusion, the project is now approaching the Preliminary Design Review (PDR) and indeed only preliminary results are available. This paper is an opportunity to present the GARISS mission as part of an International cooperation between ESA, NASA and Qascom. The preliminary results include GPS and Galileo processing from space signals, the challenges and trade off decisions, the high level STRS architecture and foreseen experimentation campaign. Detailed results from the test campaigns are expected in 2017.

space navigation↗

Mars Cube One (MarCO) Shifting the Paradigm in Relay Deep Space Operations

A very significant challenge in the planetary mission design and operations is communications with ground control teams during critical events and highly risky maneuvers. These include entry, descent, and landing (EDL) and orbit insertion, which should not be carried out in the blind. Although vast planetary distances and long round-trip light times disallow real-time intervention from controllers, acquiring the relevant event performance parameters in near real-time can be imperative for determining the corrective actions needed immediately following or, in the case of significant anomalies, aid in the diagnostic analysis. During several previous Mars missions landing events, and the Huygens probe landing on Titan, the communications strategy relied on proximity links to planetary orbiters, which then relayed the data to the Deep Space Network (DSN). In addition, attempts were successfully made in parallel to receive the signal carrier directly at Earth often using large radio telescopes when the wavelength was outside the DSN’s reception bands. This Direct-to-Earth (DTE) back-up method was only possible due to special techniques utilizing the DSN’s open-loop Radio Science Receivers. In every case, it was very challenging since the link budget of a landing vehicles were designed for proximity orbiter relays and not for distances across the solar system. A new method is introduced since not all future missions can rely on the presence of pre-existing orbiters at their planetary targets to relay their critical data ad, furthermore, most missions would not likely have the resources to implement a reliable DTE link at acceptable data rates, bypassing a need for a relay asset. With the advent CubeSat form-factor spacecraft, one or more, for added reliability, CubeSats can be launched with the primary mission, travel to the target, and be positioned to view the critical event, such as EDL, and carry out real-time relay of the data to the DSN at higher rates. CubeSats have flown in the Earth environment but never flown or been operated in deep space or planetary environment so careful design as well as flight experience are needed. The relay function requires the development of radio and antenna systems to meet challenging specifications. After initial technical demonstration of the concept and operational experience, the cost can decrease as systems become more standardized with increased reliability. This paper describes the invention of the “carry your own relay” concept and the formulation of the mission likely to be the first planetary CubeSat mission called Mars Cube One (MarCO). It also describes the operational concept of relay small spacecraft and their role reducing mission risk as well as overall mission cost.

Asmar, Sami↗

Near Earth Network (NEN) CubeSat Communications

The NASA Near Earth Network (NEN) consists of globally distributed tracking stations, including NASA, commercial, and partner ground stations, that are strategically located to maximize the coverage provided to a variety of orbital and suborbital missions, including those in LEO (Low Earth Orbit), GEO (Geosynchronous Earth Orbit), HEO (Highly Elliptical Orbit), lunar and L1-L2 orbits. The NEN's future mission set includes and will continue to include CubeSat missions. The first NEN-supported CubeSat mission will be the Cubesat Proximity Operations Demonstration (CPOD) launching into LEO in 2017. The majority of the CubeSat missions destined to fly on EM-1, launching in late 2018, many in a lunar orbit, will communicate with ground-based stations via X-band and will utilize the NASA Jet Propulsion Laboratory (JPL)-developed IRIS (Satellite Communication for Air Traffic Management) radio. The NEN recognizes the important role CubeSats are beginning to play in carrying out NASAs mission and is therefore investigating the modifications needed to provide IRIS radio compatibility. With modification, the NEN could potentially expand support to the EM-1 (Exploration Mission-1) lunar CubeSats. The NEN could begin providing significant coverage to lunar CubeSat missions utilizing three to four of the NEN's mid-latitude sites. This coverage would supplement coverage provided by the JPL Deep Space Network (DSN). The NEN, with smaller apertures than DSN, provides the benefit of a larger beamwidth that could be beneficial in the event of uncertain ephemeris data. In order to realize these benefits the NEN would need to upgrade stations targeted based on coverage ability and current configuration ease of upgrade, to ensure compatibility with the IRIS radio. In addition, the NEN is working with CubeSat radio developers to ensure NEN compatibility with alternative CubeSat radios for Lunar and L1-L2 CubeSats. The NEN has provided NEN compatibility requirements to several radio developers who are developing radios that offer lower cost and, in some cases, more capabilities with fewer constraints. The NEN is ready to begin supporting CubeSat missions. The NEN is considering network upgrades to broaden the types of CubeSat missions that can be supported and is supporting both the CubeSat community and radio developers to ensure future CubeSat missions have multiple options when choosing a network for their communications support.

CubeSat↗

Track-Level-Compensation Look-Up Table Improves Antenna Pointing Precision

This article presents the improvement of the beam-waveguide antenna pointing accuracy due to the implementation of the track-level-compensation look-up table. It presents the development of the table, from the measurements of the inclinometer tilts to the processing of the measurement data and the determination of the threeaxis alidade rotations. The table consists of three axis rotations of the alidade as a function of the azimuth position. The article also presents the equations to determine the elevation and cross-elevation errors of the antenna as a function of the alidade rotations and the antenna azimuth and elevation positions. The table performance was verified using radio beam pointing data. The pointing error decreased from 4.5 mdeg to 1.4 mdeg in elevation and from 14.5 mdeg to 3.1 mdeg in cross-elevation. I. Introduction The Deep Space Station 25 (DSS 25) antenna shown in Fig. 1 is one of NASA s Deep Space Network beam-waveguide (BWG) antennas. At 34 GHz (Ka-band) operation, it is necessary to be able to track with a pointing accuracy of 2-mdeg root-mean-square (rms). Repeatable pointing errors of several millidegrees of magnitude have been observed during the BWG antenna calibration measurements. The systematic errors of order 4 and lower are eliminated using the antenna pointing model. However, repeatable pointing errors of higher order are out of reach of the model. The most prominent high-order systematic errors are the ones caused by the uneven azimuth track. The track is shown in Fig. 2. Manufacturing and installation tolerances, as well as gaps between the segments of the track, are the sources of the pointing errors that reach over 14-mdeg peak-to-peak magnitude, as reported in [1,2]. This article presents a continuation of the investigations and measurements of the pointing errors caused by the azimuth-track-level unevenness that were presented in [1] and [2], and it presents the implementation results. Track-level-compensation (TLC) look-up tables were created for the DSS 25, DSS 26, DSS 34, and DSS 55 antennas. To date, the most complete and detailed results were obtained for the DSS 25 and DSS 55 antennas. In this article, for brevity of presentation, we present the DSS 25 antenna results only. 1 Communications Ground Systems Section. The research described in this publication was carried out by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

Gawronski, W.↗

BioSentinel Deep Space CubeSat Mission

The BioSentinel mission was recently launched aboard the SLS launch vehicle (LV) as part of the Artemis-1 campaign. This 6U CubeSat carries yeast cells to analyze the effects of radiation at large distances from Earth, becoming the first biological payload in Deep Space. Prelaunch activities included mission design updates, orbit determination rehearsals and the development of a tracking schedule in coordination with the Artemis-1 payload office and the Deep Space Network (DSN). An important influence on the trajectories of Artemis I secondaries was the uncertainty associated with deployment from the Interim Cryogenic Propulsion System (ICPS), the upper stage of the SLS LV. The ICPS was rotating at a rate of 1 rpm; there was also uncertainty in the spin axis attitude, which translated into an unknown clock angle of deployment. The variability in this angle and magnitude of deployment implied the existence of a non-negligible risk of a lunar impact, which was evaluated for various potential launch dates. On November 16 th 2022 BioSentinel successfully deployed from ICPS and the navigation team started to receive tracking data from the DSN and ESA antennas. Soon after deployment, the spacecraft was tumbling and entered safe mode. The mission team recovered the spacecraft and after four tracking passes, we solved for a first ephemeris that was sent to the DSN for better tracking of the spacecraft. After propagating this first ephemeris solution, we determined that we avoided impact with a margin of a few hundred km from the lunar surface. More tracking data over the next few days allowed for a more refined orbit solution predicting a periselene altitude of 406 km and a lunar eclipse lasting 36.5 minutes. Therefore, BioSentinel operators avoided any correction maneuvers on the trajectory and successfully tracked and guide the spacecraft. The spacecraft performed a nominal lunar flyby which provided the pertinent energy to achieve a final Earth-trailing heliocentric orbit. Over the course of two weeks, the mission operators corroborated that the subsystems were functioning as expected after the lunar eclipse and the large ΔV incurred. Science operations started once the mission achieved the nominal orbit in Deep Space. This paper discusses in detail the BioSentinel flight performance, as well as the challenges and lessons learned prior to and during this CubeSat mission.

Andres Dono Perez↗

Navigation Performance of the BioSentinel Deep Space CubeSat Mission

The BioSentinel mission was recently launched aboard the SLS launch vehicle (LV) as part of the Artemis- 1 campaign. The BioSentinel navigation team successfully tracked and guided the spacecraft through a lunar gravity assist to its destination Earth-trailing heliocentric orbit. This 6U CubeSat carries live yeast cells to analyze the effects of radiation at large distances from Earth, becoming the first biological payload in Deep Space. Prelaunch activities included mission design updates, orbit determination rehearsals and the development of a tracking schedule in coordination with the Artemis-1 payload office and the Deep Space Network (DSN). An important influence on the trajectories of Artemis I secondaries was the uncertainty associated with deployment from the Interim Cryogenic Propulsion System (ICPS), the upper stage of the SLS LV. The ICPS was rotating at a rate of 1 rpm; there was also an uncertainty in the spin axis attitude, which translated into an unknown clock angle of deployment. The variability in this angle and magnitude of deployment implied the existence of a non-negligible risk of a lunar impact, which was evaluated for various potential launch dates. We present the results of Monte Carlo analyses and compute the pertinent maneuvers to avoid it. In addition, we present a comparison with the actual deployment once the mission launched by reconstructing our trajectory with tracking data. On November 16th 2022 BioSentinel successfully deployed from ICPS and the navigation team started to receive 2-way Doppler and Sequential Ranging data from the DSN. We processed early data to try to obtain a first ephemeris using Initial Orbit Determination (IOD) methods such as the least squares. Soon after deployment, the spacecraft was tumbling and entered safe mode, creating a period where the tracking data were sparse. The mission team recovered the spacecraft and after four tracking passes, we solved for a first ephemeris that was sent to the DSN for better tracking of the spacecraft. After propagating this first ephemeris solution, we determined that we avoided impact with a margin of a few hundred km from the lunar surface. More tracking data over the next few days (from DSN as well as ESA antennas) allowed for a more refined orbit solution predicting a periselene altitude of 406 km and a lunar eclipse lasting 36.5 minutes. Therefore, BioSentinel operators aborted any correction maneuvers. This periselene altitude also gave us the necessary energy to achieve a heliocentric orbit. The next challenge was due to the necessary adjustments in our orbit determination method due to the large energy boost resulting from the lunar flyby. After a series of tracking passes we were able to get a nominal solution that resulted into a stable trajectory. This paper discusses in detail the navigation performance using the X-band IRIS transponder, as well as the challenges and lessons learned prior to and during this deep space, CubeSat mission.

Andres Dono Perez↗

Recommendations Emerging from an Analysis of NASA's Deep Space Communications Capacity

During 2016-2017, NASA’s Space Communications and Navigation (SCaN) Office chartered a study of Deep Space Network (DSN) communications capacity relative to projected future-mission demand over the next 30 years. In this paper, we briefly describe the methodology used to analyze capacity vs. demand over such a broad timeframe, summarize key findings emerging from the analysis, and discuss the associated recommendations. Performing the analysis entailed: identifying key factors shaping the anticipated future mission set, identifying several alternative future mission set scenarios consistent with these factors, and then analyzing each mission set scenario in terms of required antenna capacity, downlink and uplink capabilities, and spectrum as a function of time. On the basis of these aggregate requirements, DSN loading simulations were then conducted that examined how well each of the postulated mission sets could load up onto the the DSN’s “in-plan” architecture. To the extent that capacity shortfalls emerged during these baseline simulations, architectural solutions to the shortfalls were then postulated and tested via additional simulations. In general, the trend analyses and baseline loading simulations indicated a significant progression in challenges over the next three decades. In the current decade, the DSN appeared to be operating very close to capacity. The first human exploration mission and its secondary payload launch opportunities for cubesats traveling beyond GEO contributed to this loading. As a consequence, the main challenge appeared to be managing peak assetcontention periods. In the next decade, the DSN continued to operate close to capacity but also began transitioning to more frequent human mission support. Upgrading for, and operating, a human-rated system while continuing to meet robotic mission customer requirements emerged as the key challenge. In the 2030’s and beyond, simulations suggested a need for fundamentally new capability and capacity. The high data rates and long link distances characteristic of human Mars exploration drove requirements far beyond what is currently “in plan.” The key challenge then became determining the most cost-effective combination of RF and optical assets for communicating with the postulated human Mars assets while still providing for the needs of all the other missions across the solar system. Various link budget, visibility, and loading analyses ultimately suggested that the human Mars exploration demands of the 2030’s could best be addressed with two cross-linked RF-optical areostationary relays (or an areostationary relay and deep space habitat) providing a dual “trunk link” to an array of 2-to-3 additional 34m beam waveguide antennas and an ~8.5m optical antenna at each DSN Complex. The dual “trunk link” would enable the same amount of total data return to Earth as a single trunk link at twice the data rate, but with only half the required array size on the ground, assuming use of Multiple Spacecraft Per Antenna (MSPA) techniques. MSPA techniques, including a Multiple Uplink Per Antenna (MUPA) technique currently under investigation, also showed promise for reducing asset contention in the decades prior to human Mars exploration.

Lee, Carlyn-Ann↗

Enabling Affordable Communications for the Burgeoning Deep Space Cubesat Fleet

The low costs of development and launch, coupled with new propulsive technologies, have made CubeSats increasingly popular for use in science investigations beyond geosynchronous orbit. As this deep space CubeSat fleet grows in size, the challenge of trying to provide affordable communications for it grows commensurately. The mass, power, and volume constraints inherent to CubeSats limit the antenna size and transmit power that they can use to close the deep space link. As a consequence, CubeSats need to rely more heavily on ground antennas that are characterized by large aperture, low noise temperatures, and relatively high-power transmitters. Such antennas are not in great abundance, nor are they inexpensive to build. For this reason, NASA’s Deep Space Network has been advocating a three-pronged approach to meeting anticipated CubeSat demand: development of simultaneous, shared-beam multi-spacecraft communications capabilities, development of large-antenna cross-support arrangements with other agencies and universities, and development of less uplink-intensive navigation techniques. This paper focuses on the pursuit of simultaneous, shared-beam multi-spacecraft communications capabilities. While the Multiple Spacecraft per Antenna (MSPA) technique has existed for over a decade, it has generally been limited to supporting downlink for just two in-beam spacecraft at a time. This limitation has largely been a function of the number and cost of available receivers. A relatively new technique that potentially overcomes this limitation is Opportunistic MSPA (OMSPA). Instead of relying on additional receivers, OMSPA makes use of a digital recorder at each ground station that is capable of capturing the intermediate frequency (IF) signals from every spacecraft in the antenna beam within the frequency bands of interest. When CubeSat projects see one or more opportunities for their CubeSat(s) to intercept the traditionally scheduled antenna beam of a “host” spacecraft, they can arrange for the CubeSat(s) to transmit open loop during those opportunities. Via a secure Internet site, the CubeSat mission operators can then retrieve the time- and frequency-relevant portions of the digital recording for subsequent demodulation and decoding, or subscribe to a service that does it for them. This “opportunistic” use of a host spacecraft’s ground antenna beam potentially enables CubeSat projects to make use of large ground antennas for downlink without having to compete with bigger, better-funded missions for antenna time in the formal scheduling process. In so doing, it also potentially enables CubeSat projects to avoid the aperture fees associated with formally scheduled downlink time - fees that factor into the “bottom-line” of competitively-bid NASA missions and that actually get charged to non-NASA missions. Taking advantage of these potential OMSPA benefits, however, will require CubeSat projects to pursue mission designs that ensure at least periodic in-beam operations relative to a “host” spacecraft. In the case of a constellation of CubeSats with inter-spacecraft distances that do not extend outside of the beam-width of the desired ground antenna at the given range, one CubeSat can serve as the “host” and have a formally scheduled downlink while the rest of the CubeSats can downlink essentially for “free” via OMSPA. Deep space CubeSats, of course, will need uplink in addition to downlink. Beyond commanding, this need is driven by the use of two-way ranging and Doppler for navigation. While OMSPA may not directly facilitate uplink, it does have the potential to free up antennas for those spacecraft that periodically require formally scheduled links for commanding and two-way radio metrics. NASA is also exploring the physical feasibility of an in-beam, simultaneous multi-spacecraft uplink technique. As with OMSPA, if successful, it will require little new equipment, further enabling affordable deep space CubeSat communications.

Abraham, Douglas S.↗

X-Ray Detection and Processing Models for Spacecraft Navigation and Timing

The current primary method of deepspace navigation is the NASA Deep Space Network (DSN). High-performance navigation is achieved using Delta Differential One-Way Range techniques that utilize simultaneous observations from multiple DSN sites, and incorporate observations of quasars near the line-of-sight to a spacecraft in order to improve the range and angle measurement accuracies. Over the past four decades, x-ray astronomers have identified a number of xray pulsars with pulsed emissions having stabilities comparable to atomic clocks. The x-ray pulsar-based navigation and time determination (XNAV) system uses phase measurements from these sources to establish autonomously the position of the detector, and thus the spacecraft, relative to a known reference frame, much as the Global Positioning System (GPS) uses phase measurements from radio signals from several satellites to establish the position of the user relative to an Earth-centered fixed frame of reference. While a GPS receiver uses an antenna to detect the radio signals, XNAV uses a detector array to capture the individual xray photons from the x-ray pulsars. The navigation solution relies on detailed xray source models, signal processing, navigation and timing algorithms, and analytical tools that form the basis of an autonomous XNAV system. Through previous XNAV development efforts, some techniques have been established to utilize a pulsar pulse time-of-arrival (TOA) measurement to correct a position estimate. One well-studied approach, based upon Kalman filter methods, optimally adjusts a dynamic orbit propagation solution based upon the offset in measured and predicted pulse TOA. In this delta position estimator scheme, previously estimated values of spacecraft position and velocity are utilized from an onboard orbit propagator. Using these estimated values, the detected arrival times at the spacecraft of pulses from a pulsar are compared to the predicted arrival times defined by the pulsar s pulse timing model. A discrepancy provides an estimate of the spacecraft position offset, since an error in position will relate to the measured time offset of a pulse along the line of sight to the pulsar. XNAV researchers have been developing additional enhanced approaches to process the photon TOAs to arrive at an estimate of spacecraft position, including those using maximum-likelihood estimation, digital phase locked loops, and "single photon processing" schemes that utilize all available time data associated with each photon. Using pulsars from separate, non-coplanar locations provides range and range-rate measurements in each pulsar s direction. Combining these different pulsar measurements solves for offsets in position and velocity in three dimensions, and provides accurate overall navigation for deep space vehicles.

Sheikh, Suneel↗

State-of-the-Art: Small Spacecraft Technology

When the first edition of NASA’s Small Spacecraft Technology State-of-the-art report was published in 2013, 247 CubeSats and 105 other non-CubeSat small spacecraft under 50 kilograms (kg) had been launched worldwide, representing less than 2% of launched mass into orbit over multiple years. In 2013 alone, around 60% of the total spacecraft launched had a mass under 600 kg, and of those under 600 kg, 83% were under 200 kg and 37% were nanosatellites (1). Of the total 1,849 spacecraft launched in 2021, 94% were small spacecraft with an overall mass under 600 kg, and of those under 600 kg, 40% were under 200 kg, and 11% were nanosatellites (1). Since 2013, the fight heritage for small spacecraft has increased by over 30% and has become the primary source to space access for commercial, government, private, and academic institutions. The total number of spacecraft launched in the past 10 years is 5,681 and 45% of those had a mass. As with all previous editions of this report, the 2022 edition captures and distills a wealth of new information available on small spacecraft systems from NASA and other publicly available sources. This report is limited to publicly available information and cannot reflect major advances in development that are not publicly disclosed. We encourage any opportunity to publish mission outcomes and technology development milestones (e.g., via conference papers, press releases, company website) so they can be reflected in this report. Overall, this report is a survey of small spacecraft technologies sourced from open literature; it does not endeavor to be an original source, and only considers literature in the public domain to identify and classify devices. Commonly used sources for data include manufacturer datasheets, press releases, conference papers, journal papers, public filings with government agencies, news articles, presentations, the compendium of databases accessed via NASA’s Small Spacecraft Systems Virtual Institute (S3VI) Information Search, and engagement with companies. Data not appropriate for public dissemination, such as proprietary, export controlled, or otherwise restricted data, are not considered. As a result, this report includes many dedicated hours of desk research performed by subject matter experts reviewing resources noted above. Content in this 2022 edition is based on data available by October 2022. This report should not be considered as a comprehensive overview of all the technologies but a great reference for the current state-of-the-art SmallSat technologies. The organizational approach for each chapter is relatively consistent with previous editions and includes an introduction of the technology, current development status of the technology’s procurable systems, and summary tables of technologies surveyed. The content in each chapter is uniquely organized to present a mini-stand-alone report on spacecraft subsystems. As in previous years, chapters include information from previous editions but are updated with new and maturating technologies and reference missions. Tables in each section provide a convenient summary of the technologies discussed, with explanations and references in the body text. The authors have attempted to isolate trends in the small spacecraft industry to point out which technologies have been adopted after successful demonstration missions. Lastly, the authors tried to use the terms “SmallSat,” “microsatellite,” “nanosatellite,” and “CubeSat” in a consistent manner, even as these terms are often used interchangeably in the space industry. Every subsystem chapter contains updated information to reflect the growth in the small spacecraft market. Significant changes are included in several chapters. The “Complete Spacecraft Platforms” chapter now includes information on the two main market options, hosted payload services and dedicated buses. The “Power” chapter provides information on the development of solid-state batteries with significantly higher energy than the current state-of-theart lithium-ion batteries. A large effort was made to update the “Communications” chapter to appropriately capture the recent technology maturation of optical communications for SmallSats. The “Ground Data Systems and Mission Operations” chapter was updated to reflect the recent establishment of the Near Space Network and influx of SmallSat Optical Ground Stations. The “Guidance, Navigation and Control” chapter was updated to include Lidar sensor technology. The “Deorbit Systems” chapter includes a discussion of recently proposed changes by the Federal Communications Commission (FCC) to limit a spacecraft’s lifetime to no longer than 5 years after end-of-mission. The “Identification and Tracking” Chapter includes updated information on the progress of SmallSat tracking. Finally, this report now encompasses technology funded by NASA’s Small Spacecraft Technology (SST) program’s SmallSat Technology Partnerships (STP) initiative which is described further in this Introduction. The reader can find the included SST technology in the “On the Horizon” section of the “Thermal Systems”, “Communications”, and “Guidance, Navigation, and Control” chapters. A central element of this report is to list state-of-the-art technologies by NASA standard Technology Readiness Level (TRL) as defined by the 2020 NASA Engineering Handbook, found in NASA NPR 7123.1C NASA Systems Engineering Processes and Requirements. The authors have endeavored to independently verify the TRL value of each technology by reviewing and citing published test results or publicly available data to the best of their ability. Where test results and data disagree with vendors’ own advertised TRL, the authors have attempted to engage the vendors to discuss the discrepancy. Readers are strongly encouraged to follow the references cited in the literature describing the full performance range and capabilities of each technology. Readers of this report should reach out to individual companies to further clarify information. It is important to note that this report takes a broad system-level view. To attain a high TRL, the subsystem must be in a flight-ready configuration with all supporting infrastructure—such as mounting points, power conversion, and control algorithms—in an integrated unit. An accurate TRL assessment requires a high degree of technical knowledge on a subject device, and an in-depth understanding of the mission (including interfaces and environment) on which the device was flown. There is variability in TRL values depending on design factors for a specific technology. For example, differences in TRL assessment based on the operating environment may result from the thermal environment, mechanical loads, mission duration, or radiation exposure. If a technology has flown on a mission without success, or without providing valid confirmation to the operator, such claimed “flight heritage” was discounted. The authors believe TRLs are most accurately determined when assessed within the context of a program’s unique requirements. While the overall capability of small spacecraft has matured since the 2021 edition of this report, technologies are still being developed to make deep space SmallSat missions more routine and more cost effective. Future editions of this report may include content dedicated to the rapidly growing fields of assembly, integration, and testing services, and mission modeling and simulation–all of which are now extensively represented at small spacecraft conferences. Many of these subsystems and services are still in their infancy, but as they evolve and reliable conventions and standards emerge, the next iteration of this report may also evolve to include additional chapters.

Bruce Yost↗

James Webb Space Telescope Navigation Optimization Challenges

The James Webb Space Telescope (JWST) is a NASA flagship mission launched on December 25, 2021. The early orbit phase was highlighted by three midcourse correction burns that were performed to maneuver the vehicle into a libration orbit at the second Sun-Earth-Moon (SEM) libration point (L2). During the coast to L2, the vehicle’s primary observatory and sunshield were deployed. Once the tennis-court-sized sunshield was unfurled and tensioned into place, the large area exposed to solar radiation pressure (SRP) dictated that the SRP force model would need to account for the vehicle’s geometry, reflective properties, and orientation relative to the Sun. Following the insertion of JWST into its L2 libration orbit on January 24, 2022, the commissioning phase for the vehicle’s observatory commenced along with the first cycle of routine station-keeping maneuvers. The NASA Goddard Space Flight Center’s Flight Dynamics Facility (FDF) provides navigation services for the JWST mission. The FDF serves as the prime or backup navigation operations center for more than 30 active missions spanning a wide array of flight regimes performing orbit determination, maneuver planning, trajectory optimization, and tracking data evaluation. Definitive orbit determination for JWST is performed by the FDF using an Extended Kalman Filter (EKF) to estimate the vehicle’s trajectory and reflective properties using Deep Space Network (DSN) TRK-2-34 tracking data and spacecraft attitude telemetry. For the purposes of orbit prediction and station-keeping maneuver targeting, the sensitivity of the SRP force to the vehicle’s orientation requires that predictive attitude information must be incorporated to generate accurate orbit predictions and maneuver plans. Predictive attitude plans are provided to the FDF by the JWST Spacecraft Operations Center (SOC). Short-term (28 day) orbit predictions are propagated using a Short Range Attitude Plan (SRAP) to model the vehicle’s future attitude states for up to a week. SRAP attitude data is highly reliable, as it reflects finalized planned attitude states that the vehicle will be commanded to attain during the ensuing week. A Long Range Attitude Plan (LRAP) can be implemented to model predicted attitude states beyond one week, but these predictions are tentative and subject to revision due to changes in science operations plans. Instead, a conservative approach of applying a Sun-Pointing Neutral (SPN) attitude configuration is utilized for long-term (2 year) orbit predictions where reliable planned attitude data is not available. SPN attitude mode aligns the net SRP force along the JWST-to-Sun vector where it becomes independent of the vehicle’s angle of rotation about this vector, defined as the Sun yaw. Given the dynamic instability of the libration orbit, station-keeping thrust must be applied in either the sunward or anti-sunward direction to balance the resulting orbit. The attitude constraints of the vehicle also impose limits on the available pointing directions for station-keeping thrusters. The thrusters cannot be aligned with the optimal pointing direction for sunward maneuvers, rendering sunward maneuvers to be significantly less fuel efficient than anti-sunward maneuvers. Consequently, station-keeping maneuvers must be targeted to balance the orbit while ensuring that the next maneuver will also be performed in the anti-sunward direction to optimize propellant usage. When targeting a station-keeping maneuver, the attitude and SRP modeling configuration which is applied to the predicted post-maneuver trajectory will dictate the direction of the subsequent station-keeping maneuver, assuming nominal propulsion system performance. If the predicted post-maneuver attitude states result in an SRP model which under-predicts the cumulative SRP impact on the post-maneuver orbit, a targeted anti-sunward maneuver will be larger than necessary and the next maneuver will need to be executed sunward in order to compensate. In contrast, an anti-sunward maneuver which is targeted using an SRP model which over-predicts the cumulative SRP impact will achieve station-keeping while ensuring that the next maneuver will likewise be performed anti-sunward. For this reason, station-keeping maneuvers are targeted while applying the SPN attitude mode, as this mode entails the largest possible SRP area cross-section and therefore a larger modeled cumulative SRP impact. This paper documents the NASA Goddard Space Flight Center’s FDF support for JWST on-orbit operations and the analysis projects undertaken utilizing the experiences and data accumulated throughout the first full year of routine science operations. The results of these analysis efforts have been used to implement improvements to orbit prediction accuracy and maneuver efficiency which have the potential to prolong the lifespan of JWST to continue to conduct ground-breaking infrared astronomy for decades to come.

Flight Dynamics↗

Thomas Leps Internship Abstract

An optical navigation system is being flown as the backup system to the primary Deep Space Network telemetry for navigation and guidance purposes on Orion. This is required to ensure Orion can recover from a loss of communication, which would simultaneously cause a loss of DSN telemetry. Images taken of the Moon and Earth are used to give range and position information to the navigation computer for trajectory calculations and maneuver execution. To get telemetry data from these images, the size and location of the moon need to be calculated with high accuracy and precision. The reentry envelope for the Orion EM-1 mission requires the centroid and radius of the moon images to be determined within 1/3 of a pixel 3 sigma. In order to ensure this accuracy and precision can be attained, I was tasked with building precise dot grid images for camera calibration as well as building a hardware in the loop test stand for flight software and hardware proofing. To calibrate the Op-Nav camera a dot grid is imaged with the camera, the error between the image dot location and the actual dot location can be used to build a distortion map of the camera and lens system so that images can be fixed to display truth locations. To build the dot grid images I used the Electro Optics Lab optical bench Bright Object Simulator System, and gimbal. The gimbal was slewed to a series of elevations and azimuths. An image of the collimated single point light source was then taken at each position. After a series of 99 images were taken at different locations the single light spots were extracted from each image and added to a composite image containing all 99 points. During the development of these grids it was noticed that an intermittent error in the artificial "star" locations occurred. Prior to the summer this error was attributed to the gimbal having glitches in it's pointing direction and was going to be replaced, however after further examining the issue I determined it to be a software issue. I have since narrowed the likely source of the error down to a Software Development Kit released by the camera supplier PixeLink. I have since developed a workaround in order to build star grids for calibration until the software bug can be isolated and fixed. I was also tasked with building a Hardware in the Loop test stand in order to test the full Op-Nav system. A 4k screen displays simulated Lunar and Terrestrial images from a possible Orion trajectory. These images are then projected through a collimator and then captured with an Op-Nav camera controlled by an Intel NUC computer running flight software. The flight software then analyzes the images to determine attitude and position, this data is then reconstructed into a trajectory and matched to the simulated trajectory in order to determine the accuracy of the attitude and position estimates. In order for the system to work it needs to be precisely and accurately aligned. I developed an alignment procedure that allows the screen, collimator and camera to be squared, centered and collinear with each other within a micron spatially and 5 arcseconds in rotation. I also designed a rigid mount for the screen that was machined on site in Building 10 by another intern. While I was working in the EOL we received a $500k Orion startracker for alignment procedure testing. Due to my prior experience in electronics development, as an ancillary duty, I was tasked with building the cables required to operate and power the startracker. If any errors are made building these cables the startracker would be destroyed, I was honored that the director of the lab entrusted such a critical component with me. This internship has cemented my view on public space exploration. I always preferred public sector to privatization because, as a scientist, the most interesting aspects of space for me are not necessarily the most profitable. I was concerned that the public sector was faltering however, and that in order to improve human space exploration I would be forced into private sector. I now know that, at least at JSC, human spaceflight is still progressing, and exciting work is still being done. I am now actively seeking employment at JSC after I complete my Ph.D and have met with my branch chiefs and mentor to discuss transitioning to a grad Co-op position.

Leps, Thomas↗

A model to compare performance of space and ground network support of low-Earth orbiters

This article compares the downlink performance in a gross average sense between space and ground network support of low-Earth orbiters. The purpose is to assess what the demand for DSN support of future small, low-cost missions might be, if data storage for spacecraft becomes reliable enough and small enough to support the storage requirements needed to enable support only a fraction of the time. It is shown that the link advantage of the DSN over space reception in an average sense is enormous for low-Earth orbiters. The much shorter distances needed to communicate with the ground network more than make up for the speedup in data rate needed to compensate for the short contact times with the DSN that low-Earth orbiters have. The result is that more and more requests for DSN-only support of low-Earth orbiters can be expected.

Posner, E. C.↗