Search NASA⌕ Search

SEARCH · Search NASA

Results for “GUIDANCE SENSOR”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 379 records · Page 21

Interfacing and Verifying ALHAT Safe Precision Landing Systems with the Morpheus Vehicle

The NASA Autonomous precision Landing and Hazard Avoidance Technology (ALHAT) project developed a suite of prototype sensors to enable autonomous and safe precision landing of robotic or crewed vehicles under any terrain lighting conditions. Development of the ALHAT sensor suite was a cross-NASA effort, culminating in integration and testing on-board a variety of terrestrial vehicles toward infusion into future spaceflight applications. Terrestrial tests were conducted on specialized test gantries, moving trucks, helicopter flights, and a flight test onboard the NASA Morpheus free-flying, rocket-propulsive flight-test vehicle. To accomplish these tests, a tedious integration process was developed and followed, which included both command and telemetry interfacing, as well as sensor alignment and calibration verification to ensure valid test data to analyze ALHAT and Guidance, Navigation and Control (GNC) performance. This was especially true for the flight test campaign of ALHAT onboard Morpheus. For interfacing of ALHAT sensors to the Morpheus flight system, an adaptable command and telemetry architecture was developed to allow for the evolution of per-sensor Interface Control Design/Documents (ICDs). Additionally, individual-sensor and on-vehicle verification testing was developed to ensure functional operation of the ALHAT sensors onboard the vehicle, as well as precision-measurement validity for each ALHAT sensor when integrated within the Morpheus GNC system. This paper provides some insight into the interface development and the integrated-systems verification that were a part of the build-up toward success of the ALHAT and Morpheus flight test campaigns in 2014. These campaigns provided valuable performance data that is refining the path toward spaceflight infusion of the ALHAT sensor suite.

Carson, John M., III↗

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗

Time bases, discretes, and interrupts

The effort performed to assure proper flight program handling of time bases, discretes, and interrupts was discussed. The following time bases are used as key mission events: guidance reference release, liftoff, S-1B low level sensors dry, S-1B outboard engines cutoff, S-IVB cutoff, and S-IVB de-orbit DCS command. The five discrete outputs in the discrete output register were verified for proper setting. Each discrete input is honored in the proper time frame by forcing each discrete in the following intervals: before the discrete is enabled, after the discrete has been detected, and after the discrete has been disabled. As an assurance that an interrupt is honored only in the proper time frames, each interrupt was forced during the following intervals: prior to the specified enable time, after the interrupt has been honored, after the interrupt has been disabled.

Source record↗

Fault tolerant navigation in a Microwave Landing System environment

This paper describes the failure detection and isolation performance of a sensor fault tolerant system for the NASA Terminal Configured Vehicle (TCV) research aircraft in a Microwave Landing System (MLS) environment. The objective of the fault tolerant system is to detect failures in navigation-aid instruments and on-board sensors and to provide reliable estimates for the aircraft states in the possible presence of these sensor malfunctions. Analytic redundancy, which exists between the various sensor outputs due to the aircraft point mass equations of motion, is used to identify sensor failures. State estimates are used by an automatic guidance and control system to land the aircraft along a prescribed path.

Caglayan, A. K.↗

Characterizing performance of ultra-sensitive accelerometers

An overview is given of methodology and test results pertaining to the characterization of ultra sensitive accelerometers. Two issues are of primary concern. The terminology ultra sensitive accelerometer is used to imply instruments whose noise floors and resolution are at the state of the art. Hence, the typical approach of verifying an instrument's performance by measuring it with a yet higher quality instrument (or standard) is not practical. Secondly, it is difficult to find or create an environment with sufficiently low background acceleration. The typical laboratory acceleration levels will be at several orders of magnitude above the noise floor of the most sensitive accelerometers. Furthermore, this background must be treated as unknown since the best instrument available is the one to be tested. A test methodology was developed in which two or more like instruments are subjected to the same but unknown background acceleration. Appropriately selected spectral analysis techniques were used to separate the sensors' output spectra into coherent components and incoherent components. The coherent part corresponds to the background acceleration being measured by the sensors being tested. The incoherent part is attributed to sensor noise and data acquisition and processing noise. The method works well for estimating noise floors that are 40 to 50 dB below the motion applied to the test accelerometers. The accelerometers being tested are intended for use as feedback sensors in a system to actively stabilize an inertial guidance component test platform.

Sebesta, Henry↗

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↗

Autonomous docking ground demonstration (category 3)

The NASA Johnson Space Center (JSC) is involved in the development of an autonomous docking ground demonstration. The demonstration combines the technologies, expertise and facilities of the JSC Tracking and Communications Division (EE), Structures and Mechanics Division (ES), and the Navigation, Guidance and Control Division (EG) and their supporting contractors. The autonomous docking ground demonstration is an evaluation of the capabilities of the laser sensor system to support the docking phase (12ft to contact) when operated in conjunction with the Guidance, Navigation and Control Software. The docking mechanism being used was developed for the Apollo Soyuz Test Program. This demonstration will be conducted using the Six-Degrees of Freedom (6-DOF) Dynamic Test System (DTS). The DTS environment simulates the Space Station Freedom as the stationary or target vehicle and the Orbiter as the active or chase vehicle. For this demonstration the laser sensor will be mounted on the target vehicle and the retroreflectors on the chase vehicle. This arrangement was used to prevent potential damage to the laser. The sensor system. GN&C and 6-DOF DTS will be operated closed-loop. Initial condition to simulate vehicle misalignments, translational and rotational, will be introduced within the constraints of the systems involved. Detailed description of each of the demonstration components (e.g., Sensor System, GN&C, 6-DOF DTS and supporting computer configuration) including their capabilities and limitations will be discussed. A demonstration architecture drawing and photographs of the test configuration will be presented.

Lamkin, Steve L.↗

A Trajectory Generation Approach for Payload Directed Flight

Presently, flight systems designed to perform payload-centric maneuvers require preconstructed procedures and special hand-tuned guidance modes. To enable intelligent maneuvering via strong coupling between the goals of payload-directed flight and the autopilot functions, there exists a need to rethink traditional autopilot design and function. Research into payload directed flight examines sensor and payload-centric autopilot modes, architectures, and algorithms that provide layers of intelligent guidance, navigation and control for flight vehicles to achieve mission goals related to the payload sensors, taking into account various constraints such as the performance limitations of the aircraft, target tracking and estimation, obstacle avoidance, and constraint satisfaction. Payload directed flight requires a methodology for accurate trajectory planning that lets the system anticipate expected return from a suite of onboard sensors. This paper presents an extension to the existing techniques used in the literature to quickly and accurately plan flight trajectories that predict and optimize the expected return of onboard payload sensors.

Ippolito, Corey A.↗

Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) Imagery of Sensors

Collection of imagery of SPLICE hardware and software visualization. Descent Landing Computer ETU Open Frame Chassis (OFC). ETU pictures of the GSFC Hazard Detection Lidar during assembly. Prototype picture of the delivered Psionic Navigation Doppler Lidar, including testing with a three-dimensional mobile target. Visualization of a lunar lander simulation using the Dual Quaternion Guidance, demonstrating pointing of a mounted hazard lidar, and then diverting to a selected safe site.

Guidance Navigation and Control↗

Laser-Based Relative Navigation and Guidance for Space Shuttle Proximity Operations

American manned spacecraft have used visual piloting techniques in the terminal phase of randezvous during the Gemini, Apollo, Skylab, and Space Shuttle programs. In the last several years, space- shuttle astronauts have used the Rendezvous and Proximity Operations Program (RPOP), running; on a laptop computer, as a guidance and navigation aid during proximity operations. By processing measurements to the target satellite taken by a laser sensor, RPOP provides the shuttle crew with a more accurate relative position and velocity than from any other source. The inclusion of guidance algorithms allows RPOP to determine delta-velocities to fly very efficient, repeatable trajectories. This paper will focus on the guidance and navigation algorithms in RPOP, as well as results from simulation and flight. Although developed for shuttle proximity operations, the RPOP algorithms have potential applicability to an automated vehicle.

Laser sensors↗

Applications of Payload Directed Flight

Next generation aviation flight control concepts require autonomous and intelligent control system architectures that close control loops directly around payload sensors in manner more integrated and cohesive that in traditional autopilot designs. Research into payload directed flight control at NASA Ames Research Center is investigating new and novel architectures that can satisfy the requirements for next generation control and automation concepts for aviation. Tighter integration between sensor and machine requires definition of specific sensor-directed control modes to tie the sensor data directly into a vehicle control structures throughout the entire control architecture, from low-level stability- and control loops, to higher level mission planning and scheduling reasoning systems. Payload directed flight systems can thus provide guidance, navigation, and control for vehicle platforms hosting a suite of onboard payload sensors. This paper outlines related research into the field of payload directed flight; and outlines requirements and operating concepts for payload directed flight systems based on identified needs from the scientific literature.'

Ippolito, Corey↗

The Generic Resolution Advisor and Conflict Evaluator (GRACE) for Detect-And-Avoid (DAA) Systems

The paper describes the Generic Resolution Advisor and Conflict Evaluator (GRACE), a novel alerting and guidance algorithm that combines flexibility, robustness, and computational efficiency. GRACE is "generic" in that it makes no assumptions regarding temporal or spatial scales, aircraft performance, or its sensor and communication systems. Accordingly, GRACE is well suited to research applications where alerting and guidance is a central feature and requirements are fluid involving a wide range of aviation technologies. GRACE has been used at NASA in a number of real-time and fast-time experiments supporting evolving requirements of DAA research, including parametric studies, NAS-wide simulations, human-in-the-loop experiments, and live flight tests.

Unmanned Aircraft↗

Space Shuttle development update

The development efforts, since the STS-4 flight, in the Space Shuttle (SS) program are presented. The SS improvements introduced in the last two years include lower-weight loads, communication through the Tracking and Data Relay Satellite, expanded extravehicular activity capability, a maneuvering backpack and the manipulator foot restraint, the improvements in thermal projection system, the 'optional terminal area management targeting' guidance software, a rendezvous system with radar and star tracker sensors, and improved on-orbit living conditions. The flight demonstrations include advanced launch techniques (e.g., night launch and direct insertion to orbit); the on-orbit demonstrations; and added entry and launching capabilities. The entry aerodynamic analysis and entry flight control fine tuning are described. Reusability, improved ascent performance, intact abort and landing flexibility, rollout control, and 'smart speedbrakes' are among the many improvements planned for the future.

Brand, V.↗

STS-103 Mission Highlights Resource Tape (1 of 2)

The STS-103 flight crew, Commander Curtis L. Brown, Pilot Scott J. Kelly, Mission Specialists Steven L. Smith, C. Michael Foale, John M. Grunsfeld, Claude Nicollier, and Jean-Francois Clervoy, are seen performing pre-launch activities such as crew suit-up, and ride out to the launch pad for a night launch. Also, included are various panoramic views of the shuttle on the pad. The crew is readied in the White Room for their mission. After the closing of the hatch and arm retraction, launch activities are shown including countdown, engine ignition, launch, and the separation of the Solid Rocket Boosters. Once on-orbit the primary objective is to capture and service the Hubble Space Telescope. Included are various live shots of the payload bay showing the flight support system, the orbiter replacement unit carrier and the forward fixture that house the new Fine Guidance System (FGS). Smith and Grunsfeld replaces and changes the sensor units during the first space walk of this mission. The second space walk by Nicollier and Foale includes the changing of the computer and installation of the FGS. This is tape 1 of 2, tape 2 has a report number of NONP-NASA-VT-2000036031.

Source record↗

Ares-I Bending Filter Design using a Constrained Optimization Approach

The Ares-I launch vehicle represents a challenging flex-body structural environment for control system design. Software filtering of the inertial sensor output is required to ensure adequate stable response to guidance commands while minimizing trajectory deviations. This paper presents a design methodology employing numerical optimization to develop the Ares-I bending filters. The design objectives include attitude tracking accuracy and robust stability with respect to rigid body dynamics, propellant slosh, and flex. Under the assumption that the Ares-I time-varying dynamics and control system can be frozen over a short period of time, the bending filters are designed to stabilize all the selected frozen-time launch control systems in the presence of parameter uncertainty. To ensure adequate response to guidance command, step response specifications are introduced as constraints in the optimization problem. Imposing these constrains minimizes performance degradation caused by the addition of the bending filters. The first stage bending filter design achieves stability by adding lag to the first structural frequency to phase stabilize the first flex mode while gain stabilizing the higher modes. The upper stage bending filter design gain stabilizes all the flex bending modes. The bending filter designs provided here have been demonstrated to provide stable first and second stage control systems in both Draper Ares Stability Analysis Tool (ASAT) and the MSFC MAVERIC 6DOF nonlinear time domain simulation.

Hall, Charles↗

Autonomous Precision Landing and Hazard Avoidance Technology (ALHAT) Project Status as of May 2010

This paper includes the current status of NASA s Autonomous precision Landing and Hazard Avoidance Technology (ALHAT) Project. The ALHAT team has completed several flight tests and two major design analysis cycles. These tests and analyses examine terrain relative navigation sensors, hazard detection and avoidance sensors and algorithms, and hazard relative navigation algorithms, and the guidance and navigation system using these ALHAT functions. The next flight test is scheduled for July 2010. The paper contains results from completed flight tests and analysis cycles. ALHAT system status, upcoming tests and analyses is also addressed. The current ALHAT plans as of May 2010 are discussed. Application of the ALHAT system to landing on bodies other than the Moon is included

Striepe, Scott A.↗

Validation of Linear Covariance Techniques for Mars Entry, Descent, and Landing Guidance and Navigation Performance Analysis

Current Monte Carlo-based uncertainty analysis methods may require significant computational resources to evaluate the performance of a closed-loop guidance, navigation, and control system. An attractive alternative, particularly during the preliminary and conceptual design phase, is to use linear covariance analysis, which can provide the same statistical information as Monte Carlo methods at a fraction of the computational load. Linear covariance has already been demonstrated in various spaceflight regimes, but only recently has it been applied to atmospheric flight. In this study, a 6-degree-of-freedom formulation of both a linear covariance and Monte Carlo analysis tools are utilized for a Mars entry, descent, and landing scenario which capture both atmospheric and powered flight phases to support precision landing. Comparison of the performance results shows close agreement between the linear covariance and traditional Monte Carlo methods when incorporating an assortment of guidance algorithms and processing a variety of inertial and relative sensor measurements onboard the lander's navigation filter.

James W. Williams↗

Cross-Calibration of GNC and OLA LIDAR Systems Onboard OSIRIS-REx

The Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission carried two distinct light detection and ranging (LIDAR) systems: A scanning LIDAR called OSIRIS-REx Laser Altimeter (OLA) as part of the science payload, and a flash LIDAR system has part of the Guidance, Navigation, & Control (GNC) subsystem to serve as a navigation sensor during TAG. This presents a unique opportunity to compare the performance of the two LIDAR systems in close proximity to a small asteroid body. During the Orbital B mission phase, between June to August 2019, the OSIRIS-REx spacecraft orbited Bennu in a near-circular terminator orbit during which the altitude above the surface varied between 645 m to 740 m. Over five-week period observations were recorded with the OLA instrument that were subsequently used to construct a global digital terrain map (DTM) with a resolution of 5 cm and accuracy of ±20 cm. This model provides an excellent reference for assessing the performance of the GNC LIDAR system. Two different GNC LIDAR checkout activities were also conducted during the Orbital B phase: A limb-crossing check-out featured a series of slews to collect GNC LIDAR data across varying ranges and phase angles and operate the automatic gain control modes of the device; An OLA-GNC LIDAR cross calibration was designed to collect data from both the OLA and GNC LIDAR devices with overlapping footprints while the spacecraft was pointed nadir. This paper compares the on-orbit performance observed during the cross-calibration activity. The OLA-based global DTM and point clouds are used to evaluate the GNC LIDAR not available during previous analysis. The GNC LIDAR measurements were found to be well within accuracy and precision specified for the instrument, but much noisier than the measurements from OLA within this operating regime.

Jason M Leonard↗