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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Observability analysis of inertial navigation systems during in-flight alignment

In this work the observability of an Inertial Navigation System (INS) during In-Flight Alignment (IFA) is investigated. A systematic approach to the observability analysis of a piece-wise constant system is presented and its use is justified mathematically. As a tool for the investigation a Stepped Observability Matrix (SOM) is constructed and a corresponding Stepped Space is defined. The INS error model is transformed into the Stepped Space during a specially chosen IFA trajectory and a full observability is carried out. The results of the mathematical analysis are checked against the results of covariance simulation and a full agreement is found between the two. Finally, the practical implications of this analysis are outlined.

Bar-Itzhack, I. Y.↗
NASA NTRS2013

Automatic Calibration of an Airborne Imaging System to an Inertial Navigation Unit

This software automatically calibrates a camera or an imaging array to an inertial navigation system (INS) that is rigidly mounted to the array or imager. In effect, it recovers the coordinate frame transformation between the reference frame of the imager and the reference frame of the INS. This innovation can automatically derive the camera-to-INS alignment using image data only. The assumption is that the camera fixates on an area while the aircraft flies on orbit. The system then, fully automatically, solves for the camera orientation in the INS frame. No manual intervention or ground tie point data is required.

Ansar, Adnan I.↗
NASA NTRS1971

Inertial navigation system using three TDF gyroscopic sensors not jointly mounted on a stable platform

An inertial navigation system is described and analyzed based on two two-degree-of-freedom Schuler-gyropendulums and one two-degree-of-freedom azimuth gyro. The three sensors, each base motion isolated about its two input axes, are mounted on a common base, strapped down to the vehicle. The up and down pointing spin vectors of the two properly tuned gyropendulums track the vertical and indicate physically their velocity with respect to inertial space. The spin vector of the azimuth gyro is pointing northerly parallel to the earth axis. The system can be made self-aligning on a stationary base. If external measurements for the north direction and the vertical are available, initial disturbance torques can be measured and easily biased out. The error analysis shows that the system is practicable with today's technology.

Stieler, B.↗
NASA NTRS1980

Accuracy of aircraft velocities from inertial navigation systems for application to airborne wind measurements

An experimental assessment was made of two commercially available inertial navigation systems (INS) with regard to their velocity measuring capability for use in wind, shear, and long-wavelength atmospheric turbulence research. The assessment was based on 52 sets of postflight measurements of velocity (error) during a "Schuler cycle" (84 minutes) while the INS was still operating but the airplane was motionless. Four INS units of one type and two units of another were tested over a period of 2 years after routine research flights similar to air-linetype operations of from 1 to 6 hours duration. The maximum postflight errors found for the 52 cases had a root mean square value of 2.82 m/sec with little or no correlation of error magnitude with flight duration. Using an INS for monitoring ground speed during landway in a predicted high wind shear situation could lead to landing speeds which are dangerously high or low.

Rhyne, R. H.↗
NASA NTRS2016

Coupled Inertial Navigation and Flush Air Data Sensing Algorithm for Atmosphere Estimation

This paper describes an algorithm for atmospheric state estimation based on a coupling between inertial navigation and flush air data-sensing pressure measurements. The navigation state is used in the atmospheric estimation algorithm along with the pressure measurements and a model of the surface pressure distribution to estimate the atmosphere using a nonlinear weighted least-squares algorithm. The approach uses a high-fidelity model of atmosphere stored in table-lookup form, along with simplified models propagated along the trajectory within the algorithm to aid the solution. Thus, the method is a reduced-order Kalman filter in which the inertial states are taken from the navigation solution and atmospheric states are estimated in the filter. The algorithm is applied to data from the Mars Science Laboratory entry, descent, and landing from August 2012. Reasonable estimates of the atmosphere are produced by the algorithm. The observability of winds along the trajectory are examined using an index based on the observability Gramian and the pressure measurement sensitivity matrix. The results indicate that bank reversals are responsible for adding information content. The algorithm is applied to the design of the pressure measurement system for the Mars 2020 mission. A linear covariance analysis is performed to assess estimator performance. The results indicate that the new estimator produces more precise estimates of atmospheric states than existing algorithms.

Karlgaard, Christopher D.↗
NASA NTRS1973

Ames-aided inertial navigation work - The first two years of progress

A description of work whose broad objective is to attain improved aircraft navigation performance through exploitation of the concept of combining navigation data from several sources in an optimum manner is presented. The system developed as a result of the work, called RAINPAL (Recursive Aided Inertial Navigation for Precision Approach and Landing) is designed to combine precision radio range measurements with data from on-board inertial sensors to achieve precision navigation for approach and landing. The paper describes RAINPAL and the rationale of its design, and also serves as a sort of planning document, including a progress report, a summary of objectives past and present, and an exposition of reasons for doing the work.

Smith, G. L.↗
NASA NTRS2015

Coupled Inertial Navigation and Flush Air Data Sensing Algorithm for Atmosphere Estimation

This paper describes an algorithm for atmospheric state estimation that is based on a coupling between inertial navigation and flush air data sensing pressure measurements. In this approach, the full navigation state is used in the atmospheric estimation algorithm along with the pressure measurements and a model of the surface pressure distribution to directly estimate atmospheric winds and density using a nonlinear weighted least-squares algorithm. The approach uses a high fidelity model of atmosphere stored in table-look-up form, along with simplified models of that are propagated along the trajectory within the algorithm to provide prior estimates and covariances to aid the air data state solution. Thus, the method is essentially a reduced-order Kalman filter in which the inertial states are taken from the navigation solution and atmospheric states are estimated in the filter. The algorithm is applied to data from the Mars Science Laboratory entry, descent, and landing from August 2012. Reasonable estimates of the atmosphere and winds are produced by the algorithm. The observability of winds along the trajectory are examined using an index based on the discrete-time observability Gramian and the pressure measurement sensitivity matrix. The results indicate that bank reversals are responsible for adding information content to the system. The algorithm is then applied to the design of the pressure measurement system for the Mars 2020 mission. The pressure port layout is optimized to maximize the observability of atmospheric states along the trajectory. Linear covariance analysis is performed to assess estimator performance for a given pressure measurement uncertainty. The results indicate that the new tightly-coupled estimator can produce enhanced estimates of atmospheric states when compared with existing algorithms.

Karlgaard, Christopher D.↗
NASA NTRS1977

Use of an inertial navigation system for accurate track recovery and coastal oceanographic measurements

A data acquisition system using a Litton LTN-51 inertial navigation unit (INU) was tested and used for aircraft track recovery and for location and tracking from the air of targets at sea. The characteristic position drift of the INU is compensated for by sighting landmarks of accurately known position at discrete time intervals using a visual sighting system in the transparent nose of the Beechcraft 18 aircraft used. For an aircraft altitude of about 300 m, theoretical and experimental tests indicate that calculated aircraft and/or target positions obtained from the interpolated INU drift curve will be accurate to within 10 m for landmarks spaced approximately every 15 minutes in time. For applications in coastal oceanography, such as surface current mapping by tracking artificial targets, the system allows a broad area to be covered without use of high altitude photography and its attendant needs for large targets and clear weather.

Oliver, B. M.↗
NASA NTRS1971

Inertial navigation systems analysis.

This volume offers the avionic systems engineer a fundamental exposition of the mechanization and error analysis of inertial navigation systems. While the material is applicable to spacecraft and undersea navigation, emphasis is placed upon terrestrial applications on or slightly above the earth's surface. As a result, practical considerations are geared toward those aircraft navigation systems of particular current interest. Extensive use is made of perturbation techniques to develop linearized system equations, whose solutions closely approximate those obtained by nonlinear differential equations. A unified error analysis technique is developed that is applicable to virtually all system configurations. The technique provides a greatly simplified method for comparing the performance of competing system configurations.

Britting, K. R.↗
NASA NTRS1973

Flight results from a study of aided inertial navigation applied to landing operations

An evaluation is presented of the approach and landing performance of a Kalman filter aided inertial navigation system using flight data obtained from a series of approaches and landings of the CV-340 aircraft at an instrumented test area. A description of the flight test is given, in which data recorded included: (1) accelerometer signals from the platform of an INS; (2) three ranges from the Ames-Cubic Precision Ranging System; and (3) radar and barometric altimeter signals. The method of system evaluation employed was postflight processing of the recorded data using a Kalman filter which was designed for use on the XDS920 computer onboard the CV-340 aircraft. Results shown include comparisons between the trajectories as estimated by the Kalman filter aided system and as determined from cinetheodolite data. Data start initialization of the Kalman filter, operation at a practical data rate, postflight modeling of sensor errors and operation under the adverse condition of bad data are illustrated.

Mcgee, L. A.↗
NASA NTRS1982

Performance of a Strapdown Ring Laser Gyro Tetrad Inertial Navigation System in a helicopter flight environment

Results are presented for the first flight demonstration test of a strapdown laser inertial navigation system in a helicopter environment. It is noted that acceptable navigation accuracies were obtained with system alignment times as short as 5 min or less, and that the use of all four available sensors in the navigation algorithm did not significantly improve performance over that achievable by means of only three orthogonal sensors. Failure detection by means of the parity residual from four axes proved successful for failure levels far below the noise threshold of flight control sensing requirements. Summing the gyro parity residual for a period of 13-20 min can yield step-shifted laser gyro sensor bias error detection comparable to the gyro random shift specification.

Carestia, R. A.↗
NASA NTRS2015

A Short Tutorial on Inertial Navigation System and Global Positioning System Integration

The purpose of this document is to describe a simple method of integrating Inertial Navigation System (INS) information with Global Positioning System (GPS) information for an improved estimate of vehicle attitude and position. A simple two dimensional (2D) case is considered. The attitude estimates are derived from sensor data and used in the estimation of vehicle position and velocity through dead reckoning within the INS. The INS estimates are updated with GPS estimates using a Kalman filter. This tutorial is intended for the novice user with a focus on bringing the reader from raw sensor measurements to an integrated position and attitude estimate. An application is given using a remotely controlled ground vehicle operating in assumed 2D environment. The theory is developed first followed by an illustrative example.

Smalling, Kyle M.↗
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 NTRS2018

6DOF Testing of the SLS Inertial Navigation Unit

The Navigation System on the NASA Space Launch System (SLS) Block 1 vehicle performs initial alignment of the Inertial Navigation System (INS) navigation frame through gyrocompass alignment (GCA). In lieu of direct testing of GCA accuracy in support of requirement verification, the SLS Navigation Team proposed and conducted an engineering test to, among other things, validate the GCA performance and overall behavior of the SLS INS model through comparison with test data. This paper will detail dynamic hardware testing of the SLS INS, conducted by the SLS Navigation Team at Marshall Space Flight Center's 6DOF Table Facility, in support of GCA performance characterization and INS model validation. A 6-DOF motion platform was used to produce 6DOF pad twist and sway dynamics while a simulated SLS flight computer communicated with the INS. Tests conducted include an evaluation of GCA algorithm robustness to increasingly dynamic pad environments, an examination of GCA algorithm stability and accuracy over long durations, and a long-duration static test to gather enough data for Allan Variance analysis. Test setup, execution, and data analysis will be discussed, including analysis performed in support of SLS INS model validation.

Geohagan, Kevin W.↗