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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.

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At least 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

On the feasibility of real-time prediction of aircraft carrier motion at sea

Landing aircraft on board carriers is a most delicate phase of flight operations at sea. The ability to predict the aircraft carrier's motion over an interval of several seconds within reasonable error bounds may allow an improvement in touchdown dispersion and reduce the value of the ramp clearance due to a smoother aircraft trajectory. Also, improved information to the landing signal officer should decrease the number of waveoffs substantially. This paper indicates and shows quantitatively that, based upon the power density spectrum data for pitch and heave measured for various ships and sea conditions, the motion can be predicted well, for up to 15 s. Moreover, the zero crossover times for both pitch and heave motions can be predicted with impressive accuracy. The predictor was designed on the basis of Kalman's optimum filtering theory (the discrete time case), being compatible with real-time digital computer operation.

Sidar, M. M.↗
NASA NTRS1993

Determination of the Excess Length-of-Day Since 1630

The Kalman Earth Orientation Filter (KEOF) is used at JPL to combine independent observations of the Earth's rotation parameters, producing smoothed, interpolated estimates of polar motion (PM) and UT1-TAI, as well as estimates of their excitation functions such as the length-of-day (lod). Prior to their combination, adjustments to the data sets are made in order to place them within a common reference frame. Recently, a number of lod data sets have been determined at JPL by combining the observations summarized in Table 1. The resulting lod data sets, summarized in Table 2, span different time intervals depending upon the particular subset of observations being combined, with the longest series, spanning 1630-1990, being obtained by combining all of the observations. The determination of these lod data sets and their comparison with series of climate indicators such as the Southern Oscillation Index will be discussed.

Gross, R. S.↗
NASA NTRS2019

Application of Swept-Sine Excitation for Acoustic Impedance Education

The NASA Langley Normal Incidence Tube (NIT) and Grazing Flow Impedance Tube (GFIT) are regularly employed to characterize the frequency response of acoustic liners through the eduction of their specific acoustic impedance. Both test rigs typically use an acoustic source that produces sine wave signals at discrete frequencies (Stepped-Sine) to educe the impedance. The current work details a novel approach using frequency-swept sine waveforms normalized to a constant sound pressure level for excitation. Determination of the sound pressure level and phase from microphone measurements acquired using swept-sine excitation is performed using a modified Vold-Kalman order tracking filter. Four acoustic liners are evaluated in the NIT and GFIT with both stepped-sine and swept-sine sources. Using these two methods, the educed impedance spectra are shown to compare favorably. However, the new (Swept-Sine) approach provides much greater frequency resolution in less time, allowing the acoustic liner properties to be studied in much greater detail.

Howerton, Brian M.↗
NASA NTRS2016

An Inertial Dual-State State Estimator for Precision Planetary Landing with Hazard Detection and Avoidance

The navigation filter architecture successfully deployed on the Morpheus flight vehicle is presented. The filter was developed as a key element of the NASA Autonomous Landing and Hazard Avoidance Technology (ALHAT) project and over the course of 15 free fights was integrated into the Morpheus vehicle, operations, and flight control loop. Flight testing completed by demonstrating autonomous hazard detection and avoidance, integration of an altimeter, surface relative velocity (velocimeter) and hazard relative navigation (HRN) measurements into the onboard dual-state inertial estimator Kalman flter software, and landing within 2 meters of the vertical testbed GPS-based navigation solution at the safe landing site target. Morpheus followed a trajectory that included an ascent phase followed by a partial descent-to-landing, although the proposed filter architecture is applicable to more general planetary precision entry, descent, and landings. The main new contribution is the incorporation of a sophisticated hazard relative navigation sensor-originally intended to locate safe landing sites-into the navigation system and employed as a navigation sensor. The formulation of a dual-state inertial extended Kalman filter was designed to address the precision planetary landing problem when viewed as a rendezvous problem with an intended landing site. For the required precision navigation system that is capable of navigating along a descent-to-landing trajectory to a precise landing, the impact of attitude errors on the translational state estimation are included in a fully integrated navigation structure in which translation state estimation is combined with attitude state estimation. The map tie errors are estimated as part of the process, thereby creating a dual-state filter implementation. Also, the filter is implemented using inertial states rather than states relative to the target. External measurements include altimeter, velocimeter, star camera, terrain relative navigation sensor, and a hazard relative navigation sensor providing information regarding hazards on a map generated on-the-fly.

Bishop, Robert H.↗
NASA NTRS1972

Bounding filter - A simple solution to lack of exact a priori statistics.

Wiener and Kalman-Bucy estimation problems assume that models describing the signal and noise stochastic processes are exactly known. When this modeling information, i.e., the signal and noise spectral densities for Wiener filter and the signal and noise dynamic system and disturbing noise representations for Kalman-Bucy filtering, is inexactly known, then the filter's performance is suboptimal and may even exhibit apparent divergence. In this paper a system is designed whereby the actual estimation error covariance is bounded by the covariance calculated by the estimator. Therefore, the estimator obtains a bound on the actual error covariance which is not available, and also prevents its apparent divergence.

Nahi, N. E.↗
NASA NTRS1982

Methods of sequential estimation for determining initial data in numerical weather prediction

Numerical weather prediction (NWP) is an initial-value problem for a system of nonlinear differential equations, in which initial values are known incompletely and inaccurately. Observational data available at the initial time must therefore be supplemented by data available prior to the initial time, a problem known as meteorological data assimilation. A further complication in NWP is that solutions of the governing equations evolve on two different time scales, a fast one and a slow one, whereas fast scale motions in the atmosphere are not reliably observed. This leads to the so called initialization problem: initial values must be constrained to result in a slowly evolving forecast. The theory of estimation of stochastic dynamic systems provides a natural approach to such problems. For linear stochastic dynamic models, the Kalman-Bucy (KB) sequential filter is the optimal data assimilation method, for linear models, the optimal combined data assimilation-initialization method is a modified version of the KB filter.

Cohn, S. E.↗
NASA NTRS1975

Parameter adaptive control of multivariable systems

Model reference adaptive controllers for multivariable plants are designed using only input and output signals rather than the complete state vector. The design is based on Liapunov's direct method, and the Meyer-Kalman-Yacubovich lemma. State variable filters are employed to avoid differentiating output signals. Augmented error signals are used in deriving stable adaptive control laws which assure that the normally used response error signals approach zero asymptotically.

Monopoli, R. V.↗
NASA NTRS2010

Improving Global Mass Flux Solutions from Gravity Recovery and Climate Experiment (GRACE) Through Forward Modeling and Continuous Time Correlation

We describe Earth's mass flux from April 2003 through November 2008 by deriving a time series of mas cons on a global 2deg x 2deg equal-area grid at 10 day intervals. We estimate the mass flux directly from K band range rate (KBRR) data provided by the Gravity Recovery and Climate Experiment (GRACE) mission. Using regularized least squares, we take into account the underlying process dynamics through continuous space and time-correlated constraints. In addition, we place the mascon approach in the context of other filtering techniques, showing its equivalence to anisotropic, nonsymmetric filtering, least squares collocation, and Kalman smoothing. We produce mascon time series from KBRR data that have and have not been corrected (forward modeled) for hydrological processes and fmd that the former produce superior results in oceanic areas by minimizing signal leakage from strong sources on land. By exploiting the structure of the spatiotemporal constraints, we are able to use a much more efficient (in storage and computation) inversion algorithm based upon the conjugate gradient method. This allows us to apply continuous rather than piecewise continuous time-correlated constraints, which we show via global maps and comparisons with ocean-bottom pressure gauges, to produce time series with reduced random variance and full systematic signal. Finally, we present a preferred global model, a hybrid whose oceanic portions are derived using forward modeling of hydrology but whose land portions are not, and thus represent a pure GRACE-derived signal.

Sabaka, T. J.↗
NASA NTRS2004

New Predictive Filters for Compensating the Transport Delay on a Flight Simulator

The problems of transport delay in a flight simulator, such as its sources and effects, are reviewed. Then their effects on a pilot-in-the-loop control system are investigated with simulations. Three current prominent delay compensators the lead/lag filter, McFarland filter, and the Sobiski/Cardullo filter were analyzed and compared. This paper introduces two novel delay compensation techniques an adaptive predictor using the Kalman estimator and a state space predictive filter using a reference aerodynamic model. Applications of these two new compensators on recorded data from the NASA Langley Research Center Visual Motion Simulator show that they achieve better compensation over the current ones.

Guo, Liwen↗
NASA NTRSDate not supplied

On A Higher Order Method for Anonymous Feature Processing

Some feature-driven navigation sources, such as cameras or lidars, often require measurement-to-feature associations between the collected data and an onboard feature catalog to be performed upstream of the filter. Standard navigation practice suggests the use of Kalman updates with measurements that have first passed residual editing tests, but this is often insufficient to prevent updates based upon incorrectly associated data, leading to filter degradation and divergence. Recent work has developed the anonymous feature processing (AFP) technique that eliminates reliance upon explicit feature associations outside of the filter entirely while maintaining desirable estimation performance. This paper continues by exploring the approximation employed by AFP, and a higher order approximation is presented to further improve the estimation performance of the AFP update.

James S Mccabe↗
NASA NTRS2019

An Improved Far-Field Small Unmanned Aerial System Optical Detection Algorithm

Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation for autonomous small Unmanned Aerial System (sUAS) operations. This work presents an aircraft detect and track pipeline that fuses image differencing and morphological filtering detections for inputs to a Kalman-based object tracking pipeline. The pipeline is evaluated using two types of flight encounters: 1) motorcopter sUAS vs. fixed-wing sUAS 2) motorcopter sUAS vs general aviation plane.

Chester Dolph↗
NASA NTRS1993

Stochastic Parameters in Lunar Laser Ranging

This oral presentation describes the techniques, algorithms, and results of incorporatng a Kalman-smoothing technique into the square-root information filter when estimating deterministic and stochastic parameters relating to Earth, the Moon, and the lunar and planetary ephemerides.

lunar laser ranging ephemerides stochastic paramet↗
DOE OSTI2023

Non-asymptotic analysis of ensemble Kalman updates: effective dimension and localization

Many modern algorithms for inverse problems and data assimilation rely on ensemble Kalman updates to blend prior predictions with observed data. Ensemble Kalman methods often perform well with a small ensemble size, which is essential in applications where generating each particle is costly. This paper develops a non-asymptotic analysis of ensemble Kalman updates, which rigorously explains why a small ensemble size suffices if the prior covariance has moderate effective dimension due to fast spectrum decay or approximate sparsity. Here, we present our theory in a unified framework, comparing everal implementations of ensemble Kalman updates that use perturbed observations, square root filtering and localization. As part of our analysis, we develop new dimension-free covariance estimation bounds for approximately sparse matrices that may be of independent interest.

Mathematics↗
NASA NTRS1991

Restoration of distorted depth maps calculated from stereo sequences

A model-based Kalman estimator is developed for spatial-temporal filtering of noise and other degradations in velocity and depth maps derived from image sequences or cinema. As an illustration of the proposed procedures, edge information from image sequences of rigid objects is used in the processing of the velocity maps by selecting from a series of models for directional adaptive filtering. Adaptive filtering then allows for noise reduction while preserving sharpness in the velocity maps. Results from several synthetic and real image sequences are given.

Damour, Kevin↗
NASA NTRS1990

Computation of the factorized error covariance of the difference between correlated estimators

A state estimation problem where some of the measurements may be common to two or more data sets is considered. Two approaches for computing the error covariance of the difference between filtered estimates (for each data set) are discussed. The first algorithm is based on postprocessing of the Kalman gain profiles of two correlated estimators. It uses UD factors of the covariance of the relative error. The second algorithm uses a square root information filter applied to relative error analysis. In the absence of process noise, the square root information filter is computationally more efficient and more flexible than the Kalman gain (covariance update) method. Both the algorithms (covariance and information matrix based) are applied to a Venus orbiter simulation, and their performances are compared.

Wolff, Peter J.↗
NASA NTRS1982

Terminal area automatic navigation, guidance, and control research using the Microwave Landing System (MLS). Part 2: RNAV/MLS transition problems for aircraft

The problems in navigation and guidance encountered by aircraft in the initial transition period in changing from distance measuring equipment, VORTAC, and barometric instruments to the more precise microwave landing system data type navaids in the terminal area are investigated. The effects of the resulting discontinuities on the estimates of position and velocity for both optimal (Kalman type navigation schemes) and fixed gain (complementary type) navigation filters, and the effects of the errors in cross track, track angle, and altitude on the guidance equation and control commands during the critical landing phase are discussed. A method is presented to remove the discontinuities from the navigation loop and to reconstruct an RNAV path designed to land the aircraft with minimal turns and altitude changes.

Pines, S.↗
NASA NTRS2011

Kalman Orbit Optimized Loop Tracking

Under certain conditions of low signal power and/or high noise, there is insufficient signal to noise ratio (SNR) to close tracking loops with individual signals on orbiting Global Navigation Satellite System (GNSS) receivers. In addition, the processing power available from flight computers is not great enough to implement a conventional ultra-tight coupling tracking loop. This work provides a method to track GNSS signals at very low SNR without the penalty of requiring very high processor throughput to calculate the loop parameters. The Kalman Orbit-Optimized Loop (KOOL) tracking approach constitutes a filter with a dynamic model and using the aggregate of information from all tracked GNSS signals to close the tracking loop for each signal. For applications where there is not a good dynamic model, such as very low orbits where atmospheric drag models may not be adequate to achieve the required accuracy, aiding from an IMU (inertial measurement unit) or other sensor will be added. The KOOL approach is based on research JPL has done to allow signal recovery from weak and scintillating signals observed during the use of GPS signals for limb sounding of the Earth s atmosphere. That approach uses the onboard PVT (position, velocity, time) solution to generate predictions for the range, range rate, and acceleration of the low-SNR signal. The low- SNR signal data are captured by a directed open loop. KOOL builds on the previous open loop tracking by including feedback and observable generation from the weak-signal channels so that the MSR receiver will continue to track and provide PVT, range, and Doppler data, even when all channels have low SNR.

Young, Lawrence E.↗
NASA NTRSDate not supplied

Hybrid Flush and Synthetic Air Data Filter for Entry Vehicle Atmospheric State Estimation

A hybrid flush/synthetic air data sensing filter for entry vehicle atmosphere estimation is developed. The approach makes use of a Kalman-Schmidt and Rauch-Tung-Streibel smoother. The filter/smoother blends information from pressure sensors distributed on the heatshield with pseudo-measurements of the vehicle aerodynamic forces and moments computed from mass properties and inertial measurement unit data, and prior estimates of the atmosphere. The filter produces estimates of the atmospheric conditions along the entry trajectory, and systematic error estimates to reconcile differences between the pressure and aerodynamic data sources. The filter is applied to data acquired during the Mars 2020 entry, descent, and landing at Jezero crater on February 18th, 2021. The final paper will also include results from the Mars Science Laboratory entry, descent, and landing.

Chris D. Karlgaard↗