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NASA NTRS2019

Global Navigation Satellite Systems and How They Work

Products that rely on Global Navigation Satellite Systems (GNSS) have become an essential part of daily life for millions of people around the world. In addition to enabling navigation, these constellations of satellites and the signals they transmit provide a global, precise timing source, used in everything from electrical power grid phasing to synchronization of financial networks. This colloquium introduces the concept of radio navigation, describes the features of GNSS signals that make navigation possible, and explains how these signals are processed by GNSS receivers. The resulting measurements and error sources, such as atmospheric effects and multipath, are discussed. Special consideration is given to the challenges of using GNSS in space, and the innovations that make it possible. A survey of space applications and recent flight experiences is provided. Active areas of research are discussed, including the use of GNSS for missions to the Moon.

Ashman, Ben↗
NASA NTRS2019

Sensor Configuration Trade Study for Navigation in near Rectilinear Halo Orbits

Gateway is a NASA program planned to support a human space explorationand prove new technologies for deep space exploration. One of the Gatewayrequirements is to operate in the absence of communications with the Deep SpaceNetwork (DSN) for a period of at least 3 weeks. In this paper three types ofonboard sensors (a camera for optical navigation, a GPS receiver, and X-ray navigation),are considered to enhance its autonomy and reduce the reliance on DSN.A trade study is conducted to explore alternatives on how to achieve autonomy andhow to reduce DSN dependency while satisfying navigation performance requirements.Using linear covariance analysis, the performance of a navigation systemusing DSN and/or the other sensors is shown.

Gateway↗
NASA NTRS2017

A Minimal State Augmentation Algorithm for Vision-Based Navigation without Using Mapped Landmarks

This paper describes MAVeN (Minimal State Augmentation Algorithm for Vision-Based Navigation), which is a new algorithm for vision-based navigation that has only 21 states, yet is able to track features in successive camera images and use them to propagate estimates of the spacecraft position and velocity. The filter dimension drops to 12 if attitude information is already available. The low filter dimension makes MAVeN a very reliable and practical algorithm for real-time flight implementation. The main idea is to project observed features onto a rough shape model of the ground surface, which are then used by the filter as pseudo-landmarks. The shape model is assumed to be known beforehand, as would be obtained from prior surveillance of the landing site from orbit. MAVeN does not require pre-mapped landmarks, so it is able to navigate terrain that has not been previously observed up close. This property is especially important for close proximity operations in small body missions where ground surface features are being seen for the first time at close range. MAVeN is also able to hover motionless above the ground without position error growth, which is unusual for this class of vision-based navigation algorithms.

San Martin, A. Miguel↗
NASA NTRS2020

An Introduction to Global Navigation Satellite Systems

Products that rely on Global Navigation Satellite Systems (GNSS) have become an essential part of daily life for millions of people around the world. In addition to enabling navigation, these constellations of satellites and the signals they transmit provide a global, precise timing source, used in everything from electrical power grid phasing to synchronization of financial networks. This lecture introduces the concept of radio navigation, describes the features of GNSS signals that make navigation possible, and explains how these signals are processed by GNSS receivers. The resulting measurements and error sources, such as atmospheric effects and multipath, are discussed. Special consideration is given to the challenges of using GNSS in space, and the innovations that make it possible. A survey of space applications and recent flight experiences is provided. Active areas of research are discussed, including the use of GNSS for missions to the Moon.

Ashman, Ben↗
NASA NTRS2008

Overview of terrain relative navigation approaches for Precise Lunar Landing

The driving precision landing requirement for the Autonomous Landing and Hazard Avoidance Technology project is to autonomously land within 100m of a predetermined location on the lunar surface. Traditional lunar landing approaches based on inertial sensing do not have the navigational precision to meet this requirement. The purpose of Terrain Relative Navigation (TRN) is to augment inertial navigation by providing position or bearing measurements relative to known surface landmarks. From these measurements, the navigational precision can be reduced to a level that meets the 100m requirement. There are three different TRN functions: global position estimation, local position estimation and velocity estimation. These functions can be achieved with active range sensing or passive imaging. This paper gives a survey of many TRN approaches and then presents some high fidelity simulation results for contour matching and area correlation approaches to TRN using active sensors. Since TRN requires an a-priori reference map, the paper concludes by describing past and future lunar imaging and digital elevation map data sets available for this purpose.

Montgomery, James F.↗
NASA NTRS2017

Deep-Space Navigation Using Optical Communications Systems

Optical communication links using lasers can potentially deliver data rates much higher than those possible using radio frequencies. If optical communications equipment is going to be carried by future deep-space missions, this equipment, with some adaptations, could also be used to perform tracking for trajectory determination. A number of experiments have been performed in Earth orbit and in lunar orbit using optical data links, while other missions have demonstrated optical links over interplanetary distances. Laser ranging using corner cube retroreflectors is a well-established technique that has been used for orbit determination of Earth orbiting spacecraft, for geodesy, and for lunar research, achieving centimeter-level precisions, but it is not a practical method for deep-space distances. There are two main optical tracking types that are being considered for deep-space navigation. The first is optical astrometry of spacecraft: a telescope on the ground images the laser beam coming from a spacecraft against the star background, determining its plane-of-sky position as seen from the observatory. This type will greatly benefit from the release of the high-accuracy star catalog produced by ESA’s Gaia mission, allowing for the generation of plane-of-sky measurements with an accuracy similar to that obtained today using VLBI tracking techniques. The second is optical ranging using active optical systems at both ends of the link, requiring a more careful design of the spacecraft optical communications system. One of the advantages of using optical frequencies is that they are not affected by charged particles in the signal path the way that radio frequencies are, eliminating solar plasma and ionospheric effects from the light-time calculation and the corresponding noise. On the other hand, clouds would preclude any type of optical communication, and daytime light scattering precludes astrometric measurements. This paper presents our analysis so far of the performance that could be achieved using optical data types in a number of deep-space scenarios. One of the questions that we are trying to answer is whether spacecraft equipped with optical communications terminals would also need to carry radio-frequency equipment for navigational purposes. We also want to understand how accurately we will be able to navigate spacecraft in different mission types and phases, and what would be the constraints, advantages, and disadvantages of using optical communications systems for deep-space navigation.

Karimi, Reza↗
NASA NTRS2017

Optical Navigation During Cassini's Solstice Mission

After nearly twenty years in flight, Cassini’s mission at Saturn will conclude as it purposely dives into Saturn’s atmosphere on September 15, 2017. Primarily to avoid moons potentially harboring conditions for life and with propellant very low, the intentional plunge into the atmosphere was set in motion years ago. We take this opportunity to give an overview of the optical navigation and its roles throughout the mission. The paper describes the navigation process and the evolution of optical navigation over the past thirteen years. The last equatorial phase of the Cassini mission was particularly challenging for the OD team as the Saturn system was not being estimated anymore, and it had been a few years since the last icy moon flybys. Science pictures of Enceladus one month prior to the Enceladus encounters confirmed the moon’s position to be in good agreement with the Saturn system dynamical modeling used. This reduced Enceladus’s absolute uncertainty by a factor of three, less than 1 km, and gave confidence the navigation team could achieve acceptable flybys and meet science objectives.

Tarzi, Zahi↗
NASA NTRS2017

Using Optical Communications Links for Deep-Space Navigation

Abstract—Optical communications systems that are being developed for deep-space missions could also be used to perform deep-space navigation. A two-way optical communications system could be modified to support ranging, and the laser signal emitted by a spacecraft could be tracked against background stars to perform plane-of-sky observables. We have been analyzing a number of deep-space navigation scenarios to understand the advantages and disadvantages of using optical communication systems for navigation. Our objectives were to evaluate the performance achievable with optical systems, and to study under what circumstances these systems could be sufficient to navigate the mission, reducing the requirements for radio systems to perhaps just to be a backup for emergency communications.

Karimi, Reza R.↗
NASA NTRS2020

Navigation Design and Operations of Maven Aerobraking

This paper describes the operational design and execution of the MAVEN aerobraking phase at Mars from a Navigation Team perspective. MAVEN was designed to perform atmospheric science in a ~150x6200 km altitude elliptical orbit. After the primary science mission, it was decided that MAVEN should circularize its orbit, as much as feasible from a spacecraft and mission standpoint, to better support relay operations with the landers. As a result, MAVEN performed aerobraking in the first half of 2019 to reduce its orbit to ~150x4500 km altitude. Although MAVEN did not decrease its altitude as low as previous aerobraking missions, it had several unique challenges. Science observations continued to be taken during aerobraking, requiring dramatically better Navigation accuracies than typical for such phases. Furthermore, continuous DSN coverage with 2-way Doppler data was not available. So, with 40% less Doppler data, Navigation had to meet prediction accuracies which were an order of magnitude smaller than in previous aerobraking operations. Spacecraft accelerometer data was included in Navigation analyses in order to meet these requirements.

Jakosky, Bruce↗
NASA NTRS2020

A ROS-based Simulator for Testing the Enhanced Autonomous Navigation of the Mars 2020 Rover

In order to achieve the ambitious objectives of the Mars 2020 (M2020) mission, in particular the ability to autonomously traverse more challenging terrains more efficiently, new surface mobility software was developed for Enhanced Navigation (ENav). That decision was made early in the project, before most of the new surface flight software (FSW) existed, which created a need for a separate framework where the new navigation algorithms could be quickly prototyped and tested, before more realistic FSW-based testbeds became available. The JPL robotics team chose the Robot Operating System [1] (ROS) as the environment in which to test the new ENav algorithms. This made it possible to write the algorithms in the C language required by the FSW, so they could be directly ported over to the flight module later on, while leveraging all the C++ libraries and tools provided by ROS for simulation and testing. The ENav algorithms were developed as a separate C library, and stubs were used to replace any FSW-specific code, such as Event Reporting (EVRs) and data products (DPs). A ROS simulator was developed to generate a rich set of varied 3D terrains representative of the candidate Mars landing sites and simulate the physics of the rover motion, the point cloud perceived by the rover’s stereo vision system, and the new thinking-while-driving (TWD) navigation logic which directs the rover to drive autonomously to user-specified waypoints. To simulate the rover motion and perception, a ROS node was developed that uses a software library called HyperDrive Sim (HDSim), which is a wrapper for the Rover Sequencing and Visualization Program [2] (RSVP). That library provides roverterrain settling, realistic slip modelling, and camera rendering capability based on the rover’s NavCam machine vision models. To simulate the navigation logic, a ROS node was created that initializes and runs the ENav algorithms in a way that mimics the FSW execution, while also providing the capability to load and replay data products, including re-running the recorded inputs through the ENav algorithms for testing. An engineering Graphical User Interface (GUI) was also developed to visualize various elements, such as the rover pose during the drive, the simulated and perceived terrain, the selected local and global paths to the goal, the evaluated candidate paths and the reasons why they were rejected, the keep-in and keep-out zones (KIOZs), etc. Finally, an advanced Monte Carlo (MC) framework that can run many simulations in parallel on the Cloud and automatically generate reports that capture the key ENav performance metrics was developed to evaluate the system in a statisticallymeaningful way. This paper provides an overview of the ROSbased simulator used for testing the M2020 ENav algorithms.

Toupet, Olivier↗
NASA NTRS2020

Autonomous Optical-only Navigation for Deep Space Missions

Navigation of spacecraft for interplanetary missions is typically performed on the ground using a two-way radio link to obtain the necessary tracking data. Due to the limited number of antenna capable of tracking these spacecraft, it would be advantageous to have a navigation capability that is entirely self-contained onboard a spacecraft. A camera mounted on a spacecraft is theoretically capable of enabling self-navigating spacecraft, and has been demonstrated in limited circumstances in past missions. Fundamentally, the technique involves using various natural or artificial targets as observational beacons to determine the observers position in space. In this paper, the technique of optical-only navigation is described, including discussions of what types of observations are used, and results of analysis showing the accuracies achievable for various mission types across the Solar System is discussed.

Bhaskaran, Shyam↗
NASA NTRSDate not supplied

KNaCK-SLAM: Kinematic Navigation and Cartography Knapsack Velocity-aided LiDAR Inertial Simultaneous Localization and Mapping (SLAM)

As manned missions return to the Moon and continue on to Mars in the near future, surface navigation and mapping in extremely low solar illumination and unstructured environments without navigation aids like Global Navigation Satellite Systems (GNSS) becomes more important than even. This work explores the use of LiDAR-based Simultaneous Localization and Mapping (SLAM) to solve those problems. A LiDAR-based SLAM system be deployed as a self-contained instrument independent of external sensor inputs, and can operate in unlit environments where Vision-based SLAM system are inoperable. Furthermore, the advent of chip-scale frequency modulated continuous wave (FMCW) LiDAR technology provides Doppler-velocity information for each sensed point in the scene, which can be used to further constrain localization error in the SLAM front-end. Here we discuss the development of SLAM algorithm that makes use of the unique velocity and range sensing capabilities of FMCW-LiDAR based sensors for rover and kinematic (i.e. person-mounted) mobile navigation and terrain mapping applications for surface exploration and scientific investigations.

Kyle Miller↗
NASA NTRSDate not supplied

Operational Performance of Limb-Based Navigation from Osiris-Rex at Bennu

During approach to an unvisited body, particularly small primitive bodies, much time is spent characterizing the target and learning how to navigate with respect to it. The primary means of navigating with respect to these bodies typically involves some form of optical navigation (OpNav), where observables are extracted from images of the target and fed to a navigation filter to refine the relative position and velocity between the spacecraft and the target. We demonstrate the performance of a recently developed, limb-based OpNav technique for the approach time period by applying it to flight data from the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) spacecraft’s approach to asteroid Bennu.

Andrew J. Liounis↗
NASA NTRS2021

Autonomy for Deep Space Communications and Navigation

In more than 50 years of deep space exploration, the number and complexity of the world’s space missions have continued to increase. This has placed increasing demands on deep space communication and navigation. However, these demands have been met in spite of an essentially level overall budget. One of the reasons for this this has been the continual application of autonomy in various forms. The NASA Deep Space Network’s implementation of “Fol-low the Sun Operations” is a recent example – but certainly not the only one. During this same period, the end-to-end information system has been increasingly automated through the application of packet telemetry and virtual channels. Fully autonomous spacecraft navigation has been demonstrated, and more limited autonomy has been applied to the operational navigation process. This paper will show how autonomy and automation capabilities have been infused into operational deep space mission communication and navigation in both flight and ground systems as appropriate. It will further discuss ongoing work aimed at increasing the level of autonomy in the future, leverag-ing recent research and application of autonomy in everyday life.

Chang, Susan↗
NASA NTRSDate not supplied

Application and Use of Lunar Node-Derived Beacons for Lunar Surface Navigation

To maximize the scientific return and safety of operations on the lunar surface, multiple civil organizations are investing in Position, Navigation, and Timing infrastructure. This approach mimics the deployment of Global Navigation Satellite Systems around the Earth and aims to enable a similar robust capability around the moon. With these satellites, it will be possible to maintain high- fidelity knowledge of positioning and timing both on the surface and in orbit. For initial deployments, this capability is focused on high need areas, such as the Lunar South Pole, the target of the currently in-planning Artemis surface missions to high accuracy. Similar to GNSS systems, this capability will be implemented over time to build up to a level of global access for real-time navigation. For early missions, this means a limited capability in terms of coverage. To provide increased performance, ground augmentation can be leveraged. This not only provides an additional reference signal but helps to supply timing to enable a high accuracy real-time position solutions. This paper discusses the path towards evolving the Lunar Node 1 platform to augment these early constellation deployments. Analysis of notional performance with and without surface aids is provided, as well as discussion and paths towards deployment and operation. Given the analysis results, the benefit of surface navigation aids to both provide additional surface-based signals to fill in coverage gaps helps to provide additional robustness and an early-on capability.

Evan J. Anzalone↗
NASA NTRS2020

Hardware Verification and Validation for a Navigation Sensor Software Model in Support of Flight Vehicle Performance Analysis

… or, “It’s in the details, how to make complicated software perform like complicated hardware.” In attempts to minimize development time and quickly build an operational vehicle, NASA’s Space Launch System (SLS) has had to be intentional about integrated testing. Constraints on budget and schedule have required balance between testing needs and the desire for an integrated flight vehicle as soon as possible. To provide key insights early in design and analysis cycles, a large amount of effort has shifted into maturing and validating models at the component level with integrated testing as a means to validate their integration. In terms of SLS Navigation, this, and the model-based design approach have pushed explicit requirements for sensor models to be validated against flight hardware to high precision. This paper covers the approach taken to verify and validate the models for the two key navigation sensors on the SLS vehicle, the Redundant Inertial Navigation Sensor and the Rate Gyro Assembly. These models are used in performance evaluation, fault detection, and operations development extensively. Using a mix of data from hardware vendor documentation and testing reports, limited in-house testing, and integration activities, these models were able to be validated against flight hardware at multiple levels, from the internal software design to statistical behavior at the raw sensor and integrated box levels. The high level of insight into the hardware elements is instrumental to support flight certification activities and building confidence in SLS Navigation capability. Focused testing enabled additional insight and validation that proved invaluable and the resulting insights were used to focus and mature models. Additionally, of having validated performance-based hardware models enables a wide breadth of activities including detailed fault detection studies and integration into future vehicle frameworks, such as an upper stage and provide a valuable asset to continued SLS analysis and design.

Evan J Anzalone↗
NASA NTRS2020

Hardware Verification and Validation for a Navigation Sensor Software Model in Support of Flight Vehicle Performance Analysis

… or, “It’s in the details, how to make complicated software perform like complicated hardware.” In attempts to minimize development time and quickly build an operational vehicle, NASA’s Space Launch System (SLS) has had to be intentional about integrated testing. Constraints on budget and schedule have required balance between testing needs and the desire for an integrated flight vehicle as soon as possible. To provide key insights early in design and analysis cycles, a large amount of effort has shifted into maturing and validating models at the component level with integrated testing as a means to validate their integration. In terms of SLS Navigation, this, and the model-based design approach have pushed explicit requirements for sensor models to be validated against flight hardware to high precision. This paper covers the approach taken to verify and validate the models for the two key navigation sensors on the SLS vehicle, the Redundant Inertial Navigation Sensor and the Rate Gyro Assembly. These models are used in performance evaluation, fault detection, and operations development extensively. Using a mix of data from hardware vendor documentation and testing reports, limited in-house testing, and integration activities, these models were able to be validated against flight hardware at multiple levels, from the internal software design to statistical behavior at the raw sensor and integrated box levels. The high level of insight into the hardware elements is instrumental to support flight certification activities and building confidence in SLS Navigation capability. Focused testing enabled additional insight and validation that proved invaluable and the resulting insights were used to focus and mature models. Additionally, of having validated performance-based hardware models enables a wide breadth of activities including detailed fault detection studies and integration into future vehicle frameworks, such as an upper stage and provide a valuable asset to continued SLS analysis and design.

Thomas Park↗
NASA NTRSDate not supplied

Lucy Optical Navigation Performance During The (152830) Dinkinesh Encounter

The Lucy Jupiter-Trojan asteroid mission launched in November 2021. Its original mission concept included six small-body encounters over its 12-year primary mission. In the fall of 2022, an additional target of opportunity encounter was proposed to be executed in the fall of 2023. The encounter with (152830) Dinkinesh (previously 1999 VD57) presented myriad imaging, navigation, engineering, and planning challenges, as well as a chance to exercise and further refine the Optical NavigationSystem concept of operations, interfaces, and tools. Dinkinesh would be the smallest and dimmest target Lucy would encounter, with a higher uncertainty in these physical parameters than for other targets. While the Op Nav system and instruments carried a high amount of heritage from the New Horizons and OSIRIS-REx missions, this would be the first use of these systems on Lucy for navigation purposes. Despite these additional challenges, the Lucy Dinkinesh encounter was a resounding success throughout which the navigational system exceeded requirements.1Optical Navigation was successfully performed and fed into the orbit determination and trajectory maneuver activities up to the final knowledge update. The Dinkinesh encounter also proved to be greatly scientifically interesting, if not additionally challenging, as the Dinkinesh system was discovered to be a binary system through imaging during closest approach, and the secondary body was itself found to be a contact binary. This added complexity notwithstanding, the OpNav and OD teams were able to re-construct the close-approach trajectory of Dinkinesh in cooperation and concert with the Lucy Science Team’s shape modelling efforts.

Erik Lessac-Chenen↗