Results for “IMU” — Page 4

NASA NTRS1974

Autonomous orbital navigation using Kepler's equation

A simple method of determining the six elements of elliptic satellite orbits has been developed for use aboard manned and unmanned spacecraft orbiting the earth, moon, or any planet. The system requires the use of a horizon sensor or other device for determining the local vertical, a precision clock or timing device, and Apollo-type navigation equipment including an inertial measurement unit (IMU), a digital computer, and a coupling data unit. The three elements defining the in-plane motion are obtained from simultaneous measurements of central angle traversed around the planet and elapsed flight time using a linearization of Kepler's equation about a reference orbit. It is shown how Kalman filter theory may also be used to determine the in-plane orbital elements. The three elements defining the orbit orientation are obtained from position angles in celestial coordinates derived from the IMU with the spacecraft vertically oriented after alignment of the IMU to a known inertial coordinate frame.

Boltz, F. W.↗

NASA NTRS1975

Redundancy management of multiple KT-70 inertial measurement units applicable to the space shuttle

Results of an investigation of velocity failure detection and isolation for 3 inertial measuring units (IMU) and 2 inertial measuring units (IMU) configurations are presented. The failure detection and isolation algorithm performance was highly successful and most types of velocity errors were detected and isolated. The failure detection and isolation algorithm also included attitude FDI but was not evaluated because of the lack of time and low resolution in the gimbal angle synchro outputs. The shuttle KT-70 IMUs will have dual-speed resolvers and high resolution gimbal angle readouts. It was demonstrated by these tests that a single computer utilizing a serial data bus can successfully control a redundant 3-IMU system and perform FDI.

Cook, L. J.↗

NASA NTRS1983

Navigation of the Space Shuttle

Navigational systems and operations for the Space Shuttle are described. All navigational instrumentation is controlled from within the pressurized main cabin. Measurements of the state vector and the attitude are made with an inertial measurement unit (IMU), which uses data initialized at the moment of take-off. Orbital location is calculated in approximations using the initial propulsion conditions, models of the gravity field, and aerodynamic drag forces. Updates are periodically received from ground tracking stations. IMU continues attitude information, and additional references are made with an automated startracker device. Information can also be gathered by optical alignment, and future systems will include radar tracking in an approach mode. Deorbit is accompanied by IMU altitude measurements as well as calculations of altitude based on drag measurements. Barometric measurements begin at about 80,000 ft altitude. Signals are received from TACAN beginning at 145,000 ft, and the microwave scanning beam landing system is started at 20,000 ft. Various radionavigation systems are also employed in all flight phases.

Edwards, A., Jr.↗

NASA NTRS1991

Testing of the high accuracy inertial navigation system in the Shuttle Avionics Integration Lab

The description, results, and interpretation is presented of comparison testing between the High Accuracy Inertial Navigation System (HAINS) and KT-70 Inertial Measurement Unit (IMU). The objective was to show the HAINS can replace the KT-70 IMU in the space shuttle Orbiter, both singularly and totally. This testing was performed in the Guidance, Navigation, and Control Test Station (GTS) of the Shuttle Avionics Integration Lab (SAIL). A variety of differences between the two instruments are explained. Four, 5 day test sessions were conducted varying the number and slot position of the HAINS and KT-70 IMUs. The various steps in the calibration and alignment procedure are explained. Results and their interpretation are presented. The HAINS displayed a high level of performance accuracy previously unseen with the KT-70 IMU. The most significant improvement of the performance came in the Tuned Inertial/Extended Launch Hold tests. The HAINS exceeded the 4 hr specification requirement. The results obtained from the SAIL tests were generally well beyond the requirements of the procurement specification.

Strachan, Russell L.↗

NASA NTRS2002

Tightly Coupled Inertial Navigation System/Global Positioning System (TCMIG)

Many NASA applications planned for execution later this decade are seeking high performance, miniaturized, low power Inertial Management Units (IMU). Much research has gone into Micro-Electro-Mechanical System (MEMS) over the past decade as a solution to these needs. While MEMS devices have proven to provide high accuracy acceleration measurements, they have not yet proven to have the accuracy required by many NASA missions in rotational measurements. Therefore, a new solution has been formulated integrating the best of all IMU technologies to address these mid-term needs in the form of a Tightly Coupled Micro Inertial Navigation System (INS)/Global Positioning System (GPS) (TCMIG). The TCMIG consists of an INS and a GPS tightly coupled by a Kalman filter executing on an embedded Field Programmable Gate Array (FPGA) processor. The INS consists of a highly integrated Interferometric Fiber Optic Gyroscope (IFOG) and a MEMS accelerometer. The IFOG utilizes a tightly wound fiber coil to reduce volume and the high level of integration and advanced optical components to reduce power. The MEMS accelerometer utilizes a newly developed deep etch process to increase the proof mass and yield a highly accurate accelerometer. The GPS receiver consists of a low power miniaturized version of the Blackjack receiver. Such an IMU configuration is ideal to meet the mid-term needs of the NASA Science Enterprises and the new launch vehicles being developed for the Space Launch Initiative (SLI).

Watson, Michael D.↗

NASA NTRS2006

Practical Application of Model-based Programming and State-based Architecture to Space Missions

A viewgraph presentation to develop models from systems engineers that accomplish mission objectives and manage the health of the system is shown. The topics include: 1) Overview; 2) Motivation; 3) Objective/Vision; 4) Approach; 5) Background: The Mission Data System; 6) Background: State-based Control Architecture System; 7) Background: State Analysis; 8) Overview of State Analysis; 9) Background: MDS Software Frameworks; 10) Background: Model-based Programming; 10) Background: Titan Model-based Executive; 11) Model-based Execution Architecture; 12) Compatibility Analysis of MDS and Titan Architectures; 13) Integrating Model-based Programming and Execution into the Architecture; 14) State Analysis and Modeling; 15) IMU Subsystem State Effects Diagram; 16) Titan Subsystem Model: IMU Health; 17) Integrating Model-based Programming and Execution into the Software IMU; 18) Testing Program; 19) Computationally Tractable State Estimation & Fault Diagnosis; 20) Diagnostic Algorithm Performance; 21) Integration and Test Issues; 22) Demonstrated Benefits; and 23) Next Steps

Mission Data System (MDS)↗

NASA NTRS2013

Orion MPCV Touchdown Detection Threshold Development and Testing

A robust method of detecting Orion Multi ]Purpose Crew Vehicle (MPCV) splashdown is necessary to ensure crew and hardware safety during descent and after touchdown. The proposed method uses a triple redundant system to inhibit Reaction Control System (RCS) thruster firings, detach parachute risers from the vehicle, and transition to the post ]landing segment of the Flight Software (FSW). The vehicle crew is the prime input for touchdown detection, followed by an autonomous FSW algorithm, and finally a strictly time based backup timer. RCS thrusters must be inhibited before submersion in water to protect against possible damage due to firing these jets under water. In addition, neglecting to declare touchdown will not allow the vehicle to transition to post ]landing activities such as activating the Crew Module Up ]righting System (CMUS), resulting in possible loss of communication and difficult recovery. A previous AIAA paper gAssessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module h concluded that a strictly Inertial Measurement Unit (IMU) based detection method using an acceleration spike algorithm had the highest safety margins and shortest detection times of other methods considered. That study utilized finite element simulations of vehicle splashdown, generated by LS ]DYNA, which were expanded to a larger set of results using a Kriging surface fit. The study also used the Decelerator Systems Simulation (DSS) to generate flight dynamics during vehicle descent under parachutes. Proto ]type IMU and FSW MATLAB models provided the basis for initial algorithm development and testing. This paper documents an in ]depth trade study, using the same dynamics data and MATLAB simulations as the earlier work, to further develop the acceleration detection method. By studying the combined effects of data rate, filtering on the rotational acceleration correction, data persistence limits and values of acceleration thresholds, an optimal configuration was determined. The lever arm calculation, which removes the centripetal acceleration caused by vehicle rotation, requires that the vehicle angular acceleration be derived from vehicle body rates, necessitating the addition of a 2nd order filter to smooth the data. It was determined that using 200 Hz data directly from the vehicle IMU outperforms the 40 Hz FSW data rate. Data persistence counter values and acceleration thresholds were balanced in order to meet desired safety and performance. The algorithm proved to exhibit ample safety margin against early detection while under parachutes, and adequate performance upon vehicle splashdown. Fall times from algorithm initiation were also studied, and a backup timer length was chosen to provide a large safety margin, yet still trigger detection before CMUS inflation. This timer serves as a backup to the primary acceleration detection method. Additionally, these parameters were tested for safety on actual flight test data, demonstrating expected safety margins.

Daum, Jared↗

NASA NTRS2013

GPS/INS Sensor Fusion Using GPS Wind up Model

A method of stabilizing an inertial navigation system (INS), includes the steps of: receiving data from an inertial navigation system; and receiving a finite number of carrier phase observables using at least one GPS receiver from a plurality of GPS satellites; calculating a phase wind up correction; correcting at least one of the finite number of carrier phase observables using the phase wind up correction; and calculating a corrected IMU attitude or velocity or position using the corrected at least one of the finite number of carrier phase observables; and performing a step selected from the steps consisting of recording, reporting, or providing the corrected IMU attitude or velocity or position to another process that uses the corrected IMU attitude or velocity or position. A GPS stabilized inertial navigation system apparatus is also described.

Williamson, Walton R.↗

NASA NTRSDate not supplied

Kinematic Sensors Evaluation for Spaceflight Exercise Data Collections

INTRODUCTION: On the International Space Station (ISS), exercise feedback from astronauts is very important to diagnose and mitigate any form-related injuries and ensure efficacious exercise prescriptions and systems. Going forward, exploration exercise efforts seek to gain further quantitative data of human and system performance. Currently, methods of collecting in-flight exercise data on the ISS are limited to marker-based motion capture (MoCap) where astronauts must wear reflective markers over their clothes and specialized cameras are used. The main objective of this work was to investigate the following alternative tracking options: markerless video-based MoCap and inertial measurement units (IMUs). These were compared against traditional marker-based MoCap to evaluate kinematic accuracy and inform feasible methods for future exercise data collections on the ISS, especially in support of future Vibration Isolation and Stabilization (VIS) system development. METHODS: Three test subjects performed a variety of flight-like resistance and aerobic exercises using the Miniature Exercise Device (MED-2), Concept-2 rowing ergometer, barbell mockup, bench (e.g., for bench press, hip thruster, and cycling), and a custom structure for dips. These were intended also to represent exercises which could be performed on the multi-modality European Enhanced Exploration Exercise Device (E4D) [1]. The marker-based MoCap data, collected through a 16-camera OptiTrack MoCap system, was regarded as the gold standard to compare the data against. Passive markers were affixed to each subject according to a modified full body Plug-in Gait marker set [2] with 46 total markers. The markerless MoCap data was collected using two GoPro Hero7 cameras and one GoPro Hero11 camera. For the IMU data, a full body set of 17 Xsens DOTs were placed on the subject: 10 upper body and 7 lower body IMUs. Biomechanical modeling and evaluation was conducted through OpenSim [3] (MoCap), OpenSense [4] (IMU), OpenCap [5] (markerless), ENABLE [6] (markerless), and other modeling software. Secondary objectives included comparing the volume of equipment, reducing mass and crew set-up time. RESULTS AND DISCUSSION: While there were issues with initial processing for the IMUs and markerless MoCap, the results aided in the understanding of each sensor, developing end-to-end processes, and identifying future needs. Some observed concerns with the markerless MoCap approaches included being cognizant of a cluttered background, number of people in field of view, camera number and placement. Some challenges with the IMUs included possible sliding, early deactivation possibly due to exercise pose, and large quantity sensor synchronization. Overall, the markerless MoCap option may be the preferred method of data collection and processing as it provides a solution for certain IMU shortcomings and may be least in equipment volume, upmass, and crew setup time. CONCLUSIONS: While this work was mainly focused on ISS data collection, these sensor data along with continued evaluation and development efforts will help to establish best methods for exercise data collection on Gateway, for other Artemis missions, and beyond. Details on the latest end-to-end processing of the data and results will be presented, along with lessons learned and recommended sensor selection and methods.

S. Faragalla↗

NASA NTRSDate not supplied

Trajectory Reconstruction of the Low-Earth Orbit Flight Test of an Inflatable Decelerator

The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) project conducted a flight test of a 6m inflatable aeroshell. The LOFTID test article was a secondary payload on an Atlas V launcher that carried the Joint Polar Satellite System-2 (JPSS-2) as its primary payload. The vehicle launched on November 10th, 2022. After reaching orbit, the LOFTID test article inflated the aeroshell, separated from the upper stage on an entry trajectory, and entered the atmosphere to splash down in the Pacific Ocean under parachutes. The test concept of operations is shown in Figure 1. The test article was instrumented with a variety of sensors to be used for post-flight evaluation of vehicle performance. Data from one of the key sensors for trajectory reconstruction, the Inertial Measurement Unit (IMU), was not captured in the data recorder due to a malfunction. Data from the nose cone mounted Flush Air Data Sensing (FADS) system were successfully acquired. The layout of the FADS sensors and the measured pressures during atmospheric entry are shown in Figure 2. The FADS data were combined with a Newtonian flow pressure model [1, 2] to produce estimates of the atmospheric relative trajectory. A Mach number anchoring technique given in [2] was used to stabilize estimates in high speed flight conditions. Since no IMU data were available, a trajectory simulation was used to provide the Mach number time history. The resulting estimates of the atmospheric-relative trajectory are shown in Figures 3. Given the loss of the IMU data, alternate methods for trajectory reconstruction are being explored. One approach under investigation is the use of the on-board video recorder data to be analyzed to reconstruct attitude motion. This approach is currently under investigation and will be reported on in the final paper. The Newtonian flow pressure model for the FADS analysis will also be updated with a CFD-based pressure model.

Christopher D Karlgaard↗

NASA NTRSDate not supplied

Trajectory Reconstruction of the Low-Earth Orbit Flight Test of an Inflatable Decelerator

The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) project conducted a flight test of a 6m inflatable aeroshell. The LOFTID test article was a secondary payload on an Atlas V launcher that carried the Joint Polar Satellite System-2 (JPSS-2) as its primary payload. The vehicle launched on November 10th, 2022. After reaching orbit, the LOFTID test article inflated the aeroshell, separated from the upper stage on an entry trajectory, and entered the atmosphere to splash down in the Pacific Ocean under parachutes. The test concept of operations is shown in Figure 1. The test article was instrumented with a variety of sensors to be used for post-flight evaluation of vehicle performance. Data from one of the key sensors for trajectory reconstruction, the Inertial Measurement Unit (IMU), was not captured in the data recorder due to a malfunction. Data from the nose cone mounted Flush Air Data Sensing (FADS) system were successfully acquired. The layout of the FADS sensors and the measured pressures during atmospheric entry are shown in Figure 2. The FADS data were combined with a Newtonian flow pressure model [1, 2] to produce estimates of the atmospheric relative trajectory. A Mach number anchoring technique given in [2] was used to stabilize estimates in high speed flight conditions. Since no IMU data were available, a trajectory simulation was used to provide the Mach number time history. The resulting estimates of the atmospheric-relative trajectory are shown in Figures 3. Given the loss of the IMU data, alternate methods for trajectory reconstruction are being explored. One approach under investigation is the use of the on-board video recorder data to be analyzed to reconstruct attitude motion. This approach is currently under investigation and will be reported on in the final paper. The Newtonian flow pressure model for the FADS analysis will also be updated with a CFD-based pressure model.

Christopher D. Karlgaard↗

NASA NTRS2024

Kinematic Sensors Evaluation for Spaceflight Exercise Data Collections

INTRODUCTION: On the International Space Station (ISS), exercise feedback from astronauts is very important to diagnose and mitigate any form-related injuries and ensure efficacious exercise prescriptions and systems. Going forward, exploration exercise efforts seek to gain further quantitative data of human and system performance. Currently, methods of collecting in-flight exercise data on the ISS are limited to marker-based motion capture (MoCap) where astronauts must wear reflective markers over their clothes and specialized cameras are used. The main objective of this work was to investigate the following alternative tracking options: markerless video-based MoCap and inertial measurement units (IMUs). These were compared against traditional marker-based MoCap to evaluate kinematic accuracy and inform feasible methods for future exercise data collections on the ISS, especially in support of future Vibration Isolation and Stabilization (VIS) system development. METHODS: Three test subjects performed a variety of flight-like resistance and aerobic exercises using the Miniature Exercise Device (MED-2), Concept-2 rowing ergometer, barbell mockup, bench (e.g., for bench press, hip thruster, and cycling), and a custom structure for dips. These were intended also to represent exercises which could be performed on the multi-modality European Enhanced Exploration Exercise Device (E4D) [1]. The marker-based MoCap data, collected through a 16-camera OptiTrack MoCap system, was regarded as the gold standard to compare the data against. Passive markers were affixed to each subject according to a modified full body Plug-in Gait marker set [2] with 46 total markers. The markerless MoCap data was collected using two GoPro Hero7 cameras and one GoPro Hero11 camera. For the IMU data, a full body set of 17 Xsens DOTs were placed on the subject: 10 upper body and 7 lower body IMUs. Biomechanical modeling and evaluation was conducted through OpenSim [3] (MoCap), OpenSense [4] (IMU), OpenCap [5] (markerless), ENABLE [6] (markerless), and other modeling software. Secondary objectives included comparing the volume of equipment, reducing mass and crew set-up time. RESULTS AND DISCUSSION: While there were issues with initial processing for the IMUs and markerless MoCap, the results aided in the understanding of each sensor, developing end-to-end processes, and identifying future needs. Some observed concerns with the markerless MoCap approaches included being cognizant of a cluttered background, number of people in field of view, camera number and placement. Some challenges with the IMUs included possible sliding, early deactivation possibly due to exercise pose, and large quantity sensor synchronization. Overall, the markerless MoCap option may be the preferred method of data collection and processing as it provides a solution for certain IMU shortcomings and may be least in equipment volume, upmass, and crew setup time. CONCLUSIONS: While this work was mainly focused on ISS data collection, these sensor data along with continued evaluation and development efforts will help to establish best methods for exercise data collection on Gateway, for other Artemis missions, and beyond. Details on the latest end-to-end processing of the data and results will be presented, along with lessons learned and recommended sensor selection and methods.

S Faragalla↗

NASA NTRS1973

Failure detection and isolation of redundant inertial systems for Space Shuttle

A failure detection and isolation technique is presented for application to 'off-the-shelf' type four-gimbaled inertial measurement units (IMUs) such as the KT-70 or Carousel IV. This study concentrates initially upon actual four-gimbaled IMU performance requirements for current Space Shuttle booster and orbiter mission phases. When this information is obtained for a simplex system, the remaining area of study necessary to attain redundant IMU capabilities is the establishment of mission-dependent performance failure detection thresholds. These thresholds, then, permit a careful evaluation of the capabilities of various 'off-the-shelf' four gimbaled IMUs to satisfy the Shuttle mission performance requirements with various failure detection and isolation methods.

Brown, H. E.↗

NASA NTRS1975

Dispersion analysis and linear error analysis capabilities of the space vehicle dynamics simulation program

Previous error analyses conducted by the Guidance and Dynamics Branch of NASA have used the Guidance Analysis Program (GAP) as the trajectory simulation tool. Plans are made to conduct all future error analyses using the Space Vehicle Dynamics Simulation (SVDS) program. A study was conducted to compare the inertial measurement unit (IMU) error simulations of the two programs. Results of the GAP/SVDS comparison are presented and problem areas encountered while attempting to simulate IMU errors, vehicle performance uncertainties and environmental uncertainties using SVDS are defined. An evaluation of the SVDS linear error analysis capability is also included.

Snow, L. S.↗

NASA NTRS1976

Redundant Strapdown Laser Gyro Navigation System

For the last several years, NASA has pursued the development of low-cost high-reliability inertial navigation systems that would satisfy a broad spectrum of future space and avionics missions. Two specific programs have culminated in the construction of a Redundant Strapdown Laser Gyro Navigation System. These two programs were for development of a space ultrareliable modular computer (SUMC) and a redundant laser gyro inertial measurement unit (IMU). The SUMC is a digital computer that employs state-of-the-art large-scale integrated circuits configured in a functional modular breakdown. The redundant laser gyro IMU is a six-pack strapdown sensor package in a dodecahedron configuration which uses six laser gyros to provide incremental angular positions and six accelerometers for linear velocity outputs. The sensor arrangement allows automatic accommodation of two failures; a third failure can be tolerated provided it can be determined. The navigation system also includes redundant power supplies, built-in test-equipment (BITE) circuits for failure detection, and software which provides for navigation, redundancy management, and automatic calibration and alignment.

Mcpherson, B. W.↗

NASA NTRS1980

Space shuttle navigation analysis. Volume 2: Baseline system navigation

Studies related to the baseline navigation system for the orbiter are presented. The baseline navigation system studies include a covariance analysis of the Inertial Measurement Unit calibration and alignment procedures, postflight IMU error recovery for the approach and landing phases, on-orbit calibration of IMU instrument biases, and a covariance analysis of entry and prelaunch navigation system performance.

Jones, H. L.↗

NASA NTRS1981

GPS aiding of ocean current determination

The navigational accuracy of an oceangoing vessel using conventional GPS p-code data is examined. The GPS signal is transmitted over two carrier frequencies in the L-band at 1575.42 and 1227.6 MHz. Achievable navigational uncertainties of differenced positional estimates are presented as a function of the parameters of the problem, with particular attention given to the effect of sea-state, user equivalent range error, uncompensated antenna motion, varying delay intervals, and reduced data rate examined in the unaided mode. The unmodeled errors resulting from satellite ephemeris uncertainties are shown to be negligible for the GPS-NDS (Navigation Development) satellites. Requirements are met in relatively calm seas, but accuracy degradation by a factor of at least 2 must be anticipated in heavier sea states. The aided mode of operation is examined, and it is shown that requirements can be met by using an inertial measurement unit (IMU) to aid the GPS receiver operation. Since the use of an IMU would mean higher costs, direct Doppler from the GPS satellites is presented as a viable alternative.

Mohan, S. N.↗

NASA NTRS1981

Inertial measurement unit pre-processors and post-flight STS-1 comparisons

The flight results show that the relative tri-redundant Inertial Measurement Unit IMU performance throughout the entire entry flight was within the expected accuracy. Comparisons are presented which show differences in the accumulated sensed velocity changes as measured by the tri-redundant IMUs (in Mean Equator and Equinox of 1950.0), differences in the equivalent inertial Euler angles as measured with respect to the M50 system, and finally, preliminary instrument calibrations determined relative to the ensemble average measurement set. Also, differences in the derived body axes rates and accelerations are presented. Because of the excellent performance of the IMUs during the STS-1 entry, the selection as to which particular IMU would best serve as the dynamic data source for entry reconstruction is arbitrary.

Findlay, J. T.↗