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Limits on the precision of small accelerometers for inertial measurement units are enumerated and discussed. Scaling laws and errors which affect the precision are discussed in terms of tradeoffs between size, sensitivity, and cost.
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.
A methodology is developed and applied for quantitatively analyzing the reliability of a dual, fail-operational redundant strapdown inertial measurement unit (RSDIMU). A Markov evaluation model is defined in terms of the operational states of the RSDIMU to predict system reliability. A 27 state model is defined based upon a candidate redundancy management system which can detect and isolate a spectrum of failure magnitudes. The results of parametric studies are presented which show the effect on reliability of the gyro failure rate, both the gyro and accelerometer failure rates together, false alarms, probability of failure detection, probability of failure isolation, and probability of damage effects and mission time. A technique is developed and evaluated for generating dynamic thresholds for detecting and isolating failures of the dual, separated IMU. Special emphasis is given to the detection of multiple, nonconcurrent failures. Digital simulation time histories are presented which show the thresholds obtained and their effectiveness in detecting and isolating sensor failures.
The design and operation of a Strapdown Navigation Analysis Program (SNAP) developed to perform covariance analysis on spacecraft inertial-measurement-unit (IMU) navigation errors are described and demonstrated. Consideration is given to the IMU modeling subroutine (with user-specified sensor characteristics), the data input procedures, state updates and the simulation of instrument failures, the determination of the nominal trajectory, the mapping-matrix and Monte Carlo covariance-matrix propagation methods, and aided-navigation simulation. Numerical results are presented in tables for sample applications involving (1) the Galileo/IUS spacecraft from its deployment from the Space Shuttle to a point 10 to the 8th ft from the center of the earth and (2) the TDRS-C/IUS spacecraft from Space Shuttle liftoff to a point about 2 h before IUS deployment. SNAP is shown to give reliable results for both cases, with good general agreement between the mapping-matrix and Monte Carlo predictions.
Aerobraking is an efficient technique for orbit adjustment of planetary spacecraft, such as Magellan (Venus), Mars Global Surveyor, and Mars Odyssey. Determination of the vehicle state during the aerobraking phase has conventionally been performed using only radiometric tracking data prior to and following the atmospheric drag pass. This approach is sufficiently accurate and timely to meet current mission operational requirements; however, it is expensive in terms of ground support and leads to delayed results because ofthe need for post-drag pass data. This research presents a new approach to estimation of the vehicle state during the atmospheric pass that sequentially incorporates observations from an Inertial Measurement Unit (IMU) and models of the vehicle and environment. The approach, called Inertial Measurements for Aerobraking Navigation (IMAN), is shown to perform at a level comparable to the conventional methods in terms of navigation accuracy and superior to them in terms of availability of the results immediately after completion ofthe pass. Furthermore, the research shows that IMAN can be used to reliably predict subsequent periapsis times and locations over all aerobraking regimes. IMAN also yields accurate peak dynamic pressure and heating rates, critical for a successful corridor control strategy, comparable to navigation team reconstructed values. This research also provides the first instance of the utilization of the Unscented Kalman Filter for the purpose of estimating an actual spacecraft trajectory arc about another planet.
Spacesuit navigation is one component of NASA s efforts to return humans to the Moon. Studies performed at the NASA Glenn Research Center (GRC) considered various navigation technologies and filtering approaches to enable navigation on the lunar surface. As part of this effort, microelectromechanical systems (MEMS) inertial measurement units (IMUs) were studied to determine if they could supplement a radiometric infrastructure. MEMS IMUs were included in the Lunar Extra-Vehicular Activity Crewmember Location Determination System (LECLDS) testbed during NASA s annual Desert Research and Technology Studies (D-RATS) event in 2009 and 2010. The testbed included one IMU in 2009 and three IMUs in 2010, along with a custom circuit board interfacing between the navigation processor and each IMU. The board was revised for the 2010 test, and this paper documents the design details of this latest revision of the interface circuit board and firmware.
The Aerodynamic Coefficient Identification Package (ACIP) is an instrument consisting of body mounted linear accelerometers, rate gyros, and angular accelerometers for measuring the Space Shuttle vehicular dynamics. The high rate recorded data are utilized for postflight aerodynamic coefficient extraction studies. Although consistent with pre-mission accuracies specified by the manufacturer, the ACIP data were found to contain detectable levels of systematic error, primarily bias, as well as scale factor, static misalignment, and temperature dependent errors. This paper summarizes the technique whereby the systematic ACIP error sources were detected, identified, and calibrated with the use of recorded dynamic data from the low rate, highly accurate Inertial Measurement Units.
A vector-based failure detection and isolation technique for a skewed array of two degree-of-freedom inertial sensors is developed. Failure detection is based on comparison of parity equations with a threshold, and isolation is based on comparison of logic variables which are keyed to pass/fail results of the parity test. A multi-level approach to failure detection is used to ensure adequate coverage for the flight control, display, and navigation avionics functions. Sensor error models are introduced to expose the susceptibility of the parity equations to sensor errors and physical separation effects. The algorithm is evaluated in a simulation of a commercial transport operating in a range of light to severe turbulence environments. A bias-jump failure level of 0.2 deg/hr was detected and isolated properly in the light and moderate turbulence environments, but not detected in the extreme turbulence environment. An accelerometer bias-jump failure level of 1.5 milli-g was detected over all turbulence environments. For both types of inertial sensor, hard-over, and null type failures were detected in all environments without incident. The algorithm functioned without false alarm or isolation over all turbulence environments for the runs tested.
Ground and flight tests were conducted to investigate the problems associated with using a strapdown inertial flight data system. The objectives of this investigation were to develop a three axis inertial attitude reference system, to evaluate a self-alignment technique, and to examine the problem of time-sharing a general purpose computer for the several tasks required of it. The performance of the strapdown platform/computer system that was developed was sufficiently accurate for the tasks attempted. For flights on the order of 45 minutes duration, attitude angle errors of + or -.035 radian (+ or -2 deg) in all axes were observed. Laboratory tests of the self-alignment technique gave accuracies of + or -.00075 radian in pitch and roll axes and + or -0.0045 radian in the yaw axis. Self-alignment flight results were inconsistent, since a stable solution was not obtained on windy days because of aircraft rocking motions.
The objective of this study was to define and develop techniques for failure detection and isolation (FDI) algorithms for a dual fail/operational redundant strapdown inertial navigation system are defined and developed. The FDI techniques chosen include provisions for hard and soft failure detection in the context of flight control and navigation. Analyses were done to determine error detection and switching levels for the inertial navigation system, which is intended for a conventional takeoff or landing (CTOL) operating environment. In addition, investigations of false alarms and missed alarms were included for the FDI techniques developed, along with the analyses of filters to be used in conjunction with FDI processing. Two specific FDI algorithms were compared: the generalized likelihood test and the edge vector test. A deterministic digital computer simulation was used to compare and evaluate the algorithms and FDI systems.
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Constraints are applied to a general quaternion which describes the skewing between platforms of the Space Shuttle IMU. Once a skewing is derived, the use of the failure magnitude to threshold ratio makes possible predictions of the identification sensitivities for various failure modes. This in turn simplifies analyses and identifies portions of the flight envelope where second failure coverage is lacking. The square root of 6 and square root of 2 skewings have been baselined for use during nominal entry; the realignment software will be used on orbit to reskew the IMUs to the optimal configuration.
For any interplanetary mission, there are certain types of data that are used as a means of determining both the position and velocity of a spacecraft. The data types currently in use are Doppler, Range, Optical and VLBI. All of them are radiometric with the exception of the Optical data type. NASA's Deep Space Network is employed for the purpose of transmitting and receiving data to and from the spacecraft repsectively. For this exchange of information to take place, both the DSN and spacecrft high gain antennae must be pointed towards each other.
For any interplanetary mission, there are certain types of data that used as a means of determining both the position and velocity of a spacecraft. NASA's Deep Space Network (DSN) is employed for the purpose of transmitting and receiving data to and from the spacecraft, respectively.
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In one embodiment, a system for providing fault-tolerant inertial measurement data includes a sensor for measuring an inertial parameter and a processor. The sensor has less accuracy than a typical inertial measurement unit (IMU). The processor detects whether a difference exists between a first data stream received from a first inertial measurement unit and a second data stream received from a second inertial measurement unit. Upon detecting a difference, the processor determines whether at least one of the first or second inertial measurement units has failed by comparing each of the first and second data streams to the inertial parameter.
The general test plan for a redundant sensor strapdown inertial measuring unit evaluation program is presented. The inertial unit contains six gyros and three orthogonal accelerometers. The software incorporates failure detection and correction logic and a land vehicle navigation program. The principal objective of the test is a demonstration of the practicability, reliability, and performance of the inertial measuring unit with failure detection and correction in operational environments.