Search NASA⌕ Search

SEARCH · Search NASA

Results for “INERTIAL NAVIGATION”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

A real-time algorithm for integrating differential satellite and inertial navigation information during helicopter approach

A real-time, high-rate precision navigation Kalman filter algorithm is developed and analyzed. This Navigation algorithm blends various navigation data collected during terminal area approach of an instrumented helicopter. Navigation data collected include helicopter position and velocity from a global position system in differential mode (DGPS) as well as helicopter velocity and attitude from an inertial navigation system (INS). The goal of the Navigation algorithm is to increase the DGPS accuracy while producing navigational data at the 64 Hertz INS update rate. It is important to note that while the data was post flight processed, the Navigation algorithm was designed for real-time analysis. The design of the Navigation algorithm resulted in a nine-state Kalman filter. The Kalman filter's state matrix contains position, velocity, and velocity bias components. The filter updates positional readings with DGPS position, INS velocity, and velocity bias information. In addition, the filter incorporates a sporadic data rejection scheme. This relatively simple model met and exceeded the ten meter absolute positional requirement. The Navigation algorithm results were compared with truth data derived from a laser tracker. The helicopter flight profile included terminal glideslope angles of 3, 6, and 9 degrees. Two flight segments extracted during each terminal approach were used to evaluate the Navigation algorithm. The first segment recorded small dynamic maneuver in the lateral plane while motion in the vertical plane was recorded by the second segment. The longitudinal, lateral, and vertical averaged positional accuracies for all three glideslope approaches are as follows (mean plus or minus two standard deviations in meters): longitudinal (-0.03 plus or minus 1.41), lateral (-1.29 plus or minus 2.36), and vertical (-0.76 plus or minus 2.05).

Hoang, TY↗
NASA NTRS1994

A Real-Time Algorithm for Integrating Differential Satellite and Inertial Navigation Information During Helicopter Approach

A real-time, high-rate precision navigation Kalman filter algorithm is developed and analyzed. This navigation algorithm blends various navigation data collected during terminal area approach of an instrumented helicopter. Navigation data collected include helicopter position and velocity from a global position system in differential mode (DGPS) as well as helicopter velocity and attitude from an inertial navigation system (INS). The goal of the navigation algorithm is to increase the DGPS accuracy while producing navigational data at the 64 Hertz INS update rate. It is important to note that while the data was post flight processed, the navigation algorithm was designed for real-time analysis. The design of the navigation algorithm resulted in a nine-state Kalman filter. The Kalman filter's state matrix contains position, velocity, and velocity bias components. The filter updates positional readings with DGPS position, INS velocity, and velocity bias information. In addition, the filter incorporates a sporadic data rejection scheme. This relatively simple model met and exceeded the ten meter absolute positional requirement. The navigation algorithm results were compared with truth data derived from a laser tracker. The helicopter flight profile included terminal glideslope angles of 3, 6, and 9 degrees. Two flight segments extracted during each terminal approach were used to evaluate the navigation algorithm. The first segment recorded small dynamic maneuver in the lateral plane while motion in file vertical plane was recorded by the second segment. The longitudinal, lateral, and vertical averaged positional accuracies for all three glideslope approaches are as follows (mean +/- two standard deviations in meters): longitudinal (-0.03 +/- 1.41), lateral (-1.29 +/- 2.36), and vertical (-0.76 +/- 2.05).

Hoang, Ty↗
NASA NTRS1971

Performance evaluation system for inertial navigation equipment

Testing system studies inertial characteristics of gyroscopic devices. System consisting of instrument support package, dynamic test table, torque control electronics, and real-time computer evaluates performance of prototype gyroscopic strapdown units in inertial-grade attitude-reference systems. System is applicable to commercial aircraft.

Mc Kern, R. A.↗
NASA NTRS2013

An Application of UAV Attitude Estimation Using a Low-Cost Inertial Navigation System

Unmanned Aerial Vehicles (UAV) are playing an increasing role in aviation. Various methods exist for the computation of UAV attitude based on low cost microelectromechanical systems (MEMS) and Global Positioning System (GPS) receivers. There has been a recent increase in UAV autonomy as sensors are becoming more compact and onboard processing power has increased significantly. Correct UAV attitude estimation will play a critical role in navigation and separation assurance as UAVs share airspace with civil air traffic. This paper describes attitude estimation derived by post-processing data from a small low cost Inertial Navigation System (INS) recorded during the flight of a subscale commercial off the shelf (COTS) UAV. Two discrete time attitude estimation schemes are presented here in detail. The first is an adaptation of the Kalman Filter to accommodate nonlinear systems, the Extended Kalman Filter (EKF). The EKF returns quaternion estimates of the UAV attitude based on MEMS gyro, magnetometer, accelerometer, and pitot tube inputs. The second scheme is the complementary filter which is a simpler algorithm that splits the sensor frequency spectrum based on noise characteristics. The necessity to correct both filters for gravity measurement errors during turning maneuvers is demonstrated. It is shown that the proposed algorithms may be used to estimate UAV attitude. The effects of vibration on sensor measurements are discussed. Heuristic tuning comments pertaining to sensor filtering and gain selection to achieve acceptable performance during flight are given. Comparisons of attitude estimation performance are made between the EKF and the complementary filter.

Eure, Kenneth W.↗
NASA NTRS1992

Inertial navigation sensor integrated obstacle detection system

A system that incorporates inertial sensor information into optical flow computations to detect obstacles and to provide alternative navigational paths free from obstacles. The system is a maximally passive obstacle detection system that makes selective use of an active sensor. The active detection typically utilizes a laser. Passive sensor suite includes binocular stereo, motion stereo and variable fields-of-view. Optical flow computations involve extraction, derotation and matching of interest points from sequential frames of imagery, for range interpolation of the sensed scene, which in turn provides obstacle information for purposes of safe navigation.

Bhanu, Bir↗
NASA NTRS1982

Guidance and flight control demonstration in a helicopter flight environment using a laser-gyro inertial navigation system

The present flight test program results indicate that an all-digital inertial sensing system can be used in helicopter flight guidance and control, provided that the rotor rotation-induced motions are filtered from body rate and accelerometer signals before they are used in the feedback control system. Attention is given to the problem posed by the different repetition rates used by each of the manufacturers involved in the procurement of such all-digital subsystems. Autopilot designers must accordingly predict the need for filters and install them where called for in the sensor software. Two alternatives to this method are explored. Recent technological developments indicate that strapped down inertial systems will replace vertical and direction gyros as well as body rate accelerometers in future aircraft systems.

Schmidt, S. F.↗
NASA NTRS2011

Feature and Pose Constrained Visual Aided Inertial Navigation for Computationally Constrained Aerial Vehicles

A Feature and Pose Constrained Extended Kalman Filter (FPC-EKF) is developed for highly dynamic computationally constrained micro aerial vehicles. Vehicle localization is achieved using only a low performance inertial measurement unit and a single camera. The FPC-EKF framework augments the vehicle's state with both previous vehicle poses and critical environmental features, including vertical edges. This filter framework efficiently incorporates measurements from hundreds of opportunistic visual features to constrain the motion estimate, while allowing navigating and sustained tracking with respect to a few persistent features. In addition, vertical features in the environment are opportunistically used to provide global attitude references. Accurate pose estimation is demonstrated on a sequence including fast traversing, where visual features enter and exit the field-of-view quickly, as well as hover and ingress maneuvers where drift free navigation is achieved with respect to the environment.

autonomous operation↗
NASA NTRS1991

Inertial navigation sensor integrated motion analysis for obstacle detection

The authors describe a maximally passive system for obstacle detection and avoidance designed for rotorcraft which benefits from the use of inertial data. The sensor options are briefly discussed, and sensor fusion needs are mentioned. Some critical system requirements and their effect on system implementation are discussed. In addition, high-level system implementation issues are presented.

Roberts, Barry↗
NASA NTRS2011

Radar Altimetry and Velocimetry for Inertial Navigation: A Lunar Landing Example

The traditional role that altimetry and velocimetry have played in spacecraft landings is to provide a direct measure of the spacecraft's surface altitude and surface relative velocity; however, their role in determining an inertial position and velocity has seen limited investigation. In this study, inertially sensitive measurement models for altimetry and velocimetry are formulated that include relevant instrument and environment error models. These models are applied and simulated for a realistic lunar landing scenario that is based on recent work for NASA's Altair lander. The preliminary results indicate that inertial landing accuracies of several meters are possible.

navigation systesm↗
NASA NTRS2011

LIDAR-Aided Inertial Navigation with Extended Kalman Filtering for Pinpoint Landing

In support of NASA s Autonomous Landing and Hazard Avoidance Technology (ALHAT) project, an extended Kalman filter routine has been developed for estimating the position, velocity, and attitude of a spacecraft during the landing phase of a planetary mission. The proposed filter combines measurements of acceleration and angular velocity from an inertial measurement unit (IMU) with range and Doppler velocity observations from an onboard light detection and ranging (LIDAR) system. These high-precision LIDAR measurements of distance to the ground and approach velocity will enable both robotic and manned vehicles to land safely and precisely at scientifically interesting sites. The filter has been extensively tested using a lunar landing simulation and shown to improve navigation over flat surfaces or rough terrain. Experimental results from a helicopter flight test performed at NASA Dryden in August 2008 demonstrate that LIDAR can be employed to significantly improve navigation based exclusively on IMU integration.

Busnardo, David M.↗
NASA NTRS1992

Inertial navigation sensor integrated motion analysis for autonomous vehicle navigation

Recent work on INS integrated motion analysis is described. Results were obtained with a maximally passive system of obstacle detection (OD) for ground-based vehicles and rotorcraft. The OD approach involves motion analysis of imagery acquired by a passive sensor in the course of vehicle travel to generate range measurements to world points within the sensor FOV. INS data and scene analysis results are used to enhance interest point selection, the matching of the interest points, and the subsequent motion-based computations, tracking, and OD. The most important lesson learned from the research described here is that the incorporation of inertial data into the motion analysis program greatly improves the analysis and makes the process more robust.

Roberts, Barry↗
NASA NTRS1974

The application of microprocessors to strapdown inertial navigation

The paper describes the nature of strapdown navigators and the computational requirements associated with them. A current system design is then described in which three limited-capability microcomputers perform the tasks previously assigned to a powerful minicomputer. In addition, a technique employing dedicated microprocessors in place of conventional analog electronics in the gyroscope control loops is discussed.

Napjus, G. A.↗
NASA NTRS1976

Preliminary design of a redundant strapped down inertial navigation unit using two-degree-of-freedom tuned-gimbal gyroscopes

This redundant strapdown INS preliminary design study demonstrates the practicality of a skewed sensor system configuration by means of: (1) devising a practical system mechanization utilizing proven strapdown instruments, (2) thoroughly analyzing the skewed sensor redundancy management concept to determine optimum geometry, data processing requirements, and realistic reliability estimates, and (3) implementing the redundant computers into a low-cost, maintainable configuration.

Source record↗