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NASA NTRS1992

Several recursive techniques for observer/Kalman filter system identification from data

This paper derives algorithms for identifying autoregressive models, with external input, of multi-input multi-output systems from data using a fast transversal filter or a least-squares lattice filter. The autoregressive models including external inputs are used to identify state-space models and the corresponding observer/Kalman filter gains of the system. The derivation is an extension of scalar autoregressive model approaches, modified to cope with multivariables, external inputs and an extra direct-influence term. Comparisons between the fast transversal filter, the least-squares lattice filter and the classical least-squares method are made in terms of complexity, computational cost and practical applications issues. A numerical example is included to illustrate the approach.

Chen, Chung-Wen↗

NASA NTRS1992

Importance of Geosat orbit and tidal errors in the estimation of large-scale Indian Ocean variations

To improve the estimate accuracy of large-scale meridional sea-level variations, Geosat ERM data on the Indian Ocean for a 26-month period were processed using two different techniques of orbit error reduction. The first technique removes an along-track polynomial of degree 1 over about 5000 km and the second technique removes an along-track once-per-revolution sine wave about 40,000 km. Results obtained show that the polynomial technique produces stronger attenuation of both the tidal error and the large-scale oceanic signal. After filtering, the residual difference between the two methods represents 44 percent of the total variance and 23 percent of the annual variance. The sine-wave method yields a larger estimate of annual and interannual meridional variations.

Perigaud, Claire↗

NASA NTRS1988

Modal identification using single-mode projection filters and comparison with ERA and MLE results

The Single-Mode Projection Filter (SPF) is a newly developed algorithm for eigensystem parameter identification from both analytical results and test data. The SPF is formulated with a single mode only and practical for parallel processing implementation. Explicit formulations of SPF are derived for the multi-input multi-output (MIMO) system by using the orthogonal matrices of the controllability and observability matrices in the general sense. The modal parameters of SPF are initially obtained from an analytical model in modal space. The experimental data are then processed through SPF to update its modal parameters and to minimize a cost function defined by the norm of an error matrix. The updated modal parameters represent the characteristics of the test data. A two-dimensional global minimum optimization algorithm is developed and applied for the filter update by using the interval analysis method. The SPF is developed based on a single-mode subsystem and identifies only one modal frequency and one modal damping within a specified region. For an n-modes structure, n SPF can be implemented for parallel processing to reduce the computational burden. The SPF is applied to analyze the simulated data for the MAST beam structure. The estimated modal parameters are comparable to those from the Eigensystem Realization Algorithm (ERA) and repeated modal frequencies are identified. The modal analysis of the Spacecraft Control Laboratory Experiment (SCOLE) data is also performed by using the ERA and the Maximum Likelihood Estimate (MLE). The result shows that the first five modal frequencies are very close from ERA and MLE. However, there are slight disparities in the damping rates and the computational burdens are quite different among these two algorithms.

Huang, Jen-Kuang↗

NASA NTRS1996

Uncertainty Modeling for Structural Control Analysis and Synthesis

The development of an accurate model of uncertainties for the control of structures that undergo a change in operational environment, based solely on modeling and experimentation in the original environment is studied. The application used throughout this work is the development of an on-orbit uncertainty model based on ground modeling and experimentation. A ground based uncertainty model consisting of mean errors and bounds on critical structural parameters is developed. The uncertainty model is created using multiple data sets to observe all relevant uncertainties in the system. The Discrete Extended Kalman Filter is used as an identification/parameter estimation method for each data set, in addition to providing a covariance matrix which aids in the development of the uncertainty model. Once ground based modal uncertainties have been developed, they are localized to specific degrees of freedom in the form of mass and stiffness uncertainties. Two techniques are presented: a matrix method which develops the mass and stiffness uncertainties in a mathematical manner; and a sensitivity method which assumes a form for the mass and stiffness uncertainties in macroelements and scaling factors. This form allows the derivation of mass and stiffness uncertainties in a more physical manner. The mass and stiffness uncertainties of the ground based system are then mapped onto the on-orbit system, and projected to create an analogous on-orbit uncertainty model in the form of mean errors and bounds on critical parameters. The Middeck Active Control Experiment is introduced as experimental verification for the localization and projection methods developed. In addition, closed loop results from on-orbit operations of the experiment verify the use of the uncertainty model for control analysis and synthesis in space.

Campbell, Mark E.↗

NASA NTRS2001

Wind Tunnel Testing of the NASA-DFRC Flutterometer using a Two DOF Wing Section

Flutter of an aeroelastic structure is potentially destructive aeroelastic instability. This phenomenon has motivated research within the aeroelastic community to develop methods that can accurately predict aeroelastic instabilities. The Flutterometer method used herein, and as developed by NASA DFRC, is based upon the mu method which has been coupled with wavelet filtering processes in estimating aeroelastic models from flight data. The approach leads to a methodology to predict the occurrence of flutter boundaries, and may prove to reliably predict flutter boundaries during flight tests. An analytical model is used as the first estimate of the aeroelastic structural dynamics, and uncertainty operators are introduced into the system to model variations between the theoretical system and the physical system. The modelling uncertainties are then updated from experimental data. Although the model used did not work well with this particular experiment, a sensitivity analysis was additionally performed and improvements suggested.

Strganac, Thomas W.↗

NASA NTRS2001

Fiber Bragg Grating Filter High Temperature Sensors

We present a scaled-down method for determining high temperatures using fiber-based Bragg gratings. Bragg gratings are distributed along the length of the optical fiber, and have high reflectivities whenever the optical wavelength is twice the grating spacing. These spatially distinct Bragg regions (located in the core of a fiber) are sensitive to local temperature changes. Since these fibers are silica-based they are easily affected by localized changes in temperature, which results in changes to both the grating spacing and the wavelength reflectivity. We exploit the shift in wavelength reflectivity to measure the change in the local temperature. Note that the Bragg region (sensing area) is some distance away from where the temperature is being measured. This is done so that we can measure temperatures that are much higher than the damage threshold of the fiber. We do this by affixing the fiber with the Bragg sensor to a material with a well-known coefficient of thermal expansion, and model the heat gradient from the region of interest to the actual sensor. The research described in this paper will culminate in a working device as well as be the second portion of a publication pending submission to Optics Letters.

Lyons, Donald R.↗

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 NTRS2017

Planetary Probe Entry Atmosphere Estimation Using Synthetic Air Data System

This paper develops an atmospheric state estimator based on inertial acceleration and angular rate measurements combined with a vehicle aerodynamic model. The approach uses the navigation state of the vehicle to recast the vehicle aerodynamic model to be a function solely of the atmospheric state. Force and moment measurements are based on vehicle sensed accelerations and angular rates. These measurements are combined with an aerodynamic model and a Kalman–Schmidt filter to estimate the atmospheric conditions. The method is applied to data from the Mars Science Laboratory mission, which landed the Curiosity rover on the surface of Mars in August 2012. The results of the estimation algorithm are compared with results from a flush air data sensing algorithm based on onboard pressure measurements on the vehicle forebody. The comparison indicates that the proposed method provides estimates consistent with the air data measurements, without the use of pressure transducers. Implications for future missions such as the Mars 2020 entry capsule are described.

Karlgaard, Chris↗

NASA NTRSDate not supplied

Testing of the ISS’s Charcoal HEPA Integrated Particle Scrubber (CHIPS) Filters to Ascertain an Efficiency Curve and Most Penetrating Particle Size

High air quality is a top priority in hermetically sealed environments, such as the International Space Station (ISS). Aerosol particulate filtration on the ISS is achieved via 21 HEPA filters. Traditional HEPA filters have an efficiency of 99.97%, but HEPA filters aboard the ISS have been found to achieve 99.99% efficiency. This may be due to the flight-grade HEPA filters chosen or, less likely, that they are combined with activated charcoal filters for siloxane removal in a combination referred to as Charcoal HEPA Integrated Particle Scrubbers (CHIPS) filters. All filters have a Most Penetrating Particle Size (MPPS), but, until now, the MPPS and efficiency curve of the CHIPS filters was not known. The efficiency curve of the CHIPS filters was ascertained at NASA Glenn Research Center through standard salt crystal aerosolization. This method was selected over aerosolized oil droplets due to a desire to preserve the filter’s function for further testing. The CHIPS filters were tested as they are oriented on the ISS (activated charcoal filter followed by a flight-grade HEPA filter), with the HEPA portion alone, and with the activated charcoal portion alone. The results of this experiment are generally useful but can be especially useful for the microbiology and Environmental Control and Life Support Systems (ECLSS). This is due to microbial contamination being a potential concern in the air revitalization systems on the ISS. Knowing the MMPS of the CHIPS filters, in conjunction with levels of airborne microbes, is useful for predicting which and how many microbes are likely to get through the HEPA filters that sit in the beginning of the ISS airstream. It may also be useful to other groups at NASA to know how many particulates are able to pass through high-efficiency filtration, and even for healthcare cleanrooms and other air purification pursuits.

A Nicolas Whitlock↗

NASA NTRS2009

Recovering Aerodynamic Side Loads on Rocket Nozzles using Quasi-Static Strain-Gage Measurements

During over-expanded operation of rocket nozzles, which is defined to be when the exit pressure is greater than internal pressure over some part of the nozzle, the nozzle will experience a transverse forcing function due to the pressure differential across the nozzle wall. Over-expansion occurs during the nozzle start-up and shutdown transient, even in high-altitude engines, because most test facilities cannot completely reproduce the near-vacuum pressures at those altitudes. During this transient, the pressure differential moves axially down the nozzle as it becomes pressurized, but this differential is never perfectly symmetric circumferentially. The character of the forcing function is highly complex and defined by a series of restricted and free shock separations. The subject of this paper is the determination of the magnitude of this loading during sub-scale testing via measurement of the structural dynamic response of the nozzle and its support structure. An initial attempt at back-calculating this load using the inverse of the transfer function was performed, but this attempt was shown to be highly susceptible to numerical error. The final method chosen was to use statically calibrated strain data and to filter out the system fundamental frequency such that the measured response yields close to the correct dynamic loading function. This method was shown to capture 93% of the pressure spectral energy using controlled load shaker testing. This method is one of the only practical ways for the inverse determination of the forcing function for non-stationary excitations, and, to the authors' knowledge, has not been described in the literature to date.

Brown, Andrew↗

NASA NTRS2003

Quadrature Moments Method for the Simulation of Turbulent Reactive Flows

A sub-filter model for reactive flows, namely the DQMOM model, was formulated for Large Eddy Simulation (LES) using the filtered mass density function. Transport equations required to determine the location and size of the delta-peaks were then formulated for a 2-peak decomposition of the FDF. The DQMOM scheme was implemented in an existing structured-grid LES solver. Simulations of scalar shear layer using an experimental configuration showed that the first and second moments of both reactive and inert scalars are in good agreement with a conventional Lagrangian scheme that evolves the same FDF. Comparisons with LES simulations performed using laminar chemistry assumption for the reactive scalar show that the new method provides vast improvements at minimal computational cost. Currently, the DQMOM model is being implemented for use with the progress variable/mixture fraction model of Pierce. Comparisons with experimental results and LES simulations using a single-environment for the progress-variable are planned. Future studies will aim at understanding the effect of increase in environments on predictions.

Raman, Venkatramanan↗

NASA NTRS1975

Chandrasekhar-type algorithms for fast recursive estimation in linear systems with constant parameters

In this recursive method proposed, the gain matrix for the Kalman filter and the convariance of the state vector are computed not via the Riccati equation, but from certain other equations. These differential equations are of Chandrasekhar-type. The 'invariant imbedding' idea resulted in the reduction of the basic boundary value problem of transport theory to an equivalent initial value system, a significant computational advance. Initial value experience showed that there is some computational savings in the method and the loss of positive definiteness of the covariance matrix is less vulnerable.

Choudhury, A. K.↗

NASA NTRSDate not supplied

A Method for Obtaining Surface Flow Vectors and Its Implementation in Interferometric Skin Friction Measurement

A new method was developed to extract surface flow vectors from an oilflow visualization image that has oil streaklines. The method is analogous to the PIV processing where the image is divided into interrogation windows. A representative flow direction is obtained for each interrogation window using image processing with a line-detection algorithm. Repeating the process for the entire image, one can find the surface vector field. The Hough transformation, Radon transformation and Machine Learning were used as line-detection algorithms. The obtained vector field is then postprocessed to filter spurious vectors and to apply smoothing. The method was tested on 2D and 3D models with different flow complexities. The method was able to predict surface flow vectors for all cases tested. The predicted surface flow vectors were used in obtaining the surface skin friction. The successful implementation of the surface flow vectors enables interferometric skin friction measurements on surfaces beneath 3D complex flows. In addition, the surface flow vectors can be superimposed on the oilflow visualization images to better explain the surface flow topology.

Surface flow vectors↗

NASA NTRSDate not supplied

A Method for Obtaining Surface Flow Vectors and Its Implementation in Interferometric Skin Friction Measurement

Surface flow vectors↗

NASA NTRS1972

Control system estimation and design for aerospace vehicles with time delay

The problems of estimation and control of discrete, linear, time-varying systems are considered. Previous solutions to these problems involved either approximate techniques, open-loop control solutions, or results which required excessive computation. The estimation problem is solved by two different methods, both of which yield the identical algorithm for determining the optimal filter. The partitioned results achieve a substantial reduction in computation time and storage requirements over the expanded solution, however. The results reduce to the Kalman filter when no delays are present in the system. The control problem is also solved by two different methods, both of which yield identical algorithms for determining the optimal control gains. The stochastic control is shown to be identical to the deterministic control, thus extending the separation principle to time delay systems. The results obtained reduce to the familiar optimal control solution when no time delays are present in the system.

Allgaier, G. R.↗

NASA NTRS2010

Multi-Stage System for Automatic Target Recognition

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.

Chao, Tien-Hsin↗

NASA NTRS2009

Neural Network Target Identification System for False Alarm Reduction

ATR↗

NASA NTRS1986

Design of robust failure detection filters

An essential aspect of the design of control systems for large, flexible spacecraft is fault tolerance. Because it is anticipated that a large number of sensors and actuators will be required to realize good control over these assemblies, the detection and isolation of component failures cannot be based on direct comparisons among replicated components. Instead, the notion of 'analytic redundancy' must be employed for the FDI function. Unfortunately this makes the FDI function sensitive to modeling errors which are certain to exist in the large space structure problem due to model truncation and parameter uncertainty. This paper addresses the robustness to model error of one method of FDI residual generation - the failure detection filter. Initial designs were found to be extremely sensitive to modeling error. The sources of this sensitivity are analyzed and modifications to the design are suggested. The improved filter is shown to have much better visibility of the failure signatures relative to the background due to modeling error.

San Martin, A. M.↗