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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Parallel plate capacitor TiN KID array development for the Balloon Experiment for Galactic Infrared Science

The Balloon Experiment for Galactic Infrared Science (BEGINS) will map dust spectral energy distributions (SEDs) between 25 and 250 microns near high-mass star regions, characterizing the radiation fields and dust properties of a variety of stellar environments. To accomplish these goals, BEGINS will be outfitted with roughly 1,800 titanium nitride (TiN), superconducting kinetic inductance detectors (KIDs). A parallel plate capacitor KID design is used to achieve high pixel density arrays. A cryogenic, silicon-based, metal-mesh linear variable filter will define detector band passes across the KID array. Optical coupling to the filters and telescope will be accomplished using silicon microlenses. Here we present laboratory characterization of prototype BEGINS detector arrays, describe future array development plans, and describe the testbed used to characterize the arrays.

Nicholas F. Cothard↗
NASA NTRS2021

Infrared Focal Plane Assemblies for an All-Sky Spectral Survey

This work presents three large-format 2048 x 2048 pixel arrays integrated into passively cooled assemblies able to image in the short and medium-wave infrared range. Continuous spectral selectivity is achieved by means of linear variable filters mounted above the detector arrays.

Bock, James↗
NASA NTRS1983

The effects of the Asselin time filter on numerical solutions to the linearized shallow-water wave equations

In the present investigation, a one-dimensional linearized analysis is used to determine the effect of Asselin's (1972) time filter on both the computational stability and phase error of numerical solutions for the shallow water wave equations, in cases with diffusion but without rotation. An attempt has been made to establish the approximate optimal values of the filtering parameter nu for each of the 'lagged', Dufort-Frankel, and Crank-Nicholson diffusion schemes, suppressing the computational wave mode without materially altering the physical wave mode. It is determined that in the presence of diffusion, the optimum filter length depends on whether waves are undergoing significant propagation. When moderate propagation is present, with or without diffusion, the Asselin filter has little effect on the spatial phase lag of the physical mode for the leapfrog advection scheme of the three diffusion schemes considered.

Schlesinger, R. E.↗
NASA NTRS1983

An allpass filter design for luminance and chrominance separation of NTSC signals

Abstract- Digital techniques for separation of luminance and chrominance components from composite NTSC broadcast television signals are explored. First, comb filters are introduced. Secondly, filters based on linear regression are developed. Finally, a third filtering form, the allpass filter is considered. Techniques are compared based on mean squared error criteria and subjective testing. It is demonstrated that the allpass filtering technique is superior to both the comb filtering as well as the regression filtering.

Border, D. A.↗
NASA NTRS2006

Nonlinear consider covariance analysis using a sigma-point filter formulation

The research reported here extends the mathematical formulation of nonlinear, sigma-point estimators to enable consider covariance analysis for dynamical systems. This paper presents a novel sigma-point consider filter algorithm, for consider-parameterized nonlinear estimation, following the unscented Kalman filter (UKF) variation on the sigma-point filter formulation, which requires no partial derivatives of dynamics models or measurement models with respect to the parameter list. It is shown that, consistent with the attributes of sigma-point estimators, a consider-parameterized sigma-point estimator can be developed entirely without requiring the derivation of any partial-derivative matrices related to the dynamical system, the measurements, or the considered parameters, which appears to be an advantage over the formulation of a linear-theory sequential consider estimator. It is also demonstrated that a consider covariance analysis performed with this 'partial-derivative-free' formulation yields equivalent results to the linear-theory consider filter, for purely linear problems.

covariance↗
NASA NTRS1975

Failure detection by pilots during automatic landing: Models and experiments

A model of the pilot as a monitor of instrument failures during automatic landing is proposed. The failure detection model consists of two stages: a linear estimator (Kalman Filter) and a decision mechanism which is based on sequential analysis. The filter equations are derived from a simplified version of the linearized dynamics of the airplane and the control loop. The perceptual observation noise is modelled to include the effects of the partition of attention among the several instruments. The final result is a simple model consisting of a high pass filter to produce the observation residuals, and a decision function which is a pure integration of the residuals minus a bias term. The dynamics of a Boeing 707 were used to simulate the fully coupled final approach in a fixed base simulator which also included failures in the airspeed, glideslope, and localizer indicators. Subjects monitored the approaches and detected the failures; their performance was compared with the predictions of the model with good agreement between the experimental data and the model.

Gai, E. G.↗
NASA NTRS1977

Failure detection by pilots during automatic landing - Models and experiments

A model is proposed to describe the pilot as a monitor of automatic landing systems. The failures treated are equivalent to the addition of a dynamic change in the mean of the observation process. The failure detection model of the pilot consists of two stages: a linear estimator (Kalman filter) and a decision mechanism based on sequential analysis. The filter equations are derived from a simplified version of the linearized dynamics of the airplane and the control loop. The perceptual observation noise is modified to include the effects of allocation of attention among the several instruments. The final result is a simple model consisting of a high-pass filter to produce the observation residuals and a decision function which is a pure integration of the residuals minus a bias term. The dynamics of a Boeing 707 were used to simulate the fully coupled final approach in a fixed-base simulator. Observers monitored the approaches and detected the failures; their performance was compared with the predictions of the model.

Gai, E. G.↗
NASA NTRS2012

Nonlinear Motion Cueing Algorithm: Filtering at Pilot Station and Development of the Nonlinear Optimal Filters for Pitch and Roll

Telban and Cardullo have developed and successfully implemented the non-linear optimal motion cueing algorithm at the Visual Motion Simulator (VMS) at the NASA Langley Research Center in 2005. The latest version of the non-linear algorithm performed filtering of motion cues in all degrees-of-freedom except for pitch and roll. This manuscript describes the development and implementation of the non-linear optimal motion cueing algorithm for the pitch and roll degrees of freedom. Presented results indicate improved cues in the specified channels as compared to the original design. To further advance motion cueing in general, this manuscript describes modifications to the existing algorithm, which allow for filtering at the location of the pilot's head as opposed to the centroid of the motion platform. The rational for such modification to the cueing algorithms is that the location of the pilot's vestibular system must be taken into account as opposed to the off-set of the centroid of the cockpit relative to the center of rotation alone. Results provided in this report suggest improved performance of the motion cueing algorithm.

Zaychik, Kirill B.↗
NASA NTRS1988

A system for remote measurements of the wind stress over the ocean

The DISSTRESS system for remote measurements of the surface wind stress over the ocean from ships and buoys is described. It is fully digital, utilizing the inertial dissipation technique. Parallel processing allows anemometer data to be filtered in natural frequency space; that is, the filter cutoffs shift linearly with the mean wind speed of the data to be filtered. The construction of the digital Butterworth bandpass filters is presented in detail. The performance of the system is evaluated by analyzing the results from 28 days of operation during the Frontal Air-Sea Interaction Experiment. The mean wind speed is checked, the anemometer response function is established, and drag coefficients are compared to previous studies. The capability of the system is demonstrated by continuous time series of the friction velocity computed every 20 min. The conclusion is that the surface wind stress can be measured more reliably and accurately (20 percent) with this system than from anemometer wind speeds and a bulk formula.

Large, William G.↗
NASA NTRS1984

The application of LQR synthesis techniques to the turboshaft engine control problem

A power turbine governor was designed for a recent-technology turboshaft engine coupled to a modern, articulated rotor system using Linear Quadratic Regulator (LQR) and Kalman Filter (KF) techniques. A linear, state-space model of the engine and rotor system was derived for six engine power settings from flight idle to maximum continuous. An integrator was appended to the fuel flow input to reduce the steady-state governor error to zero. Feedback gains were calculated for the system states at each power setting using the LQR technique. The main rotor tip speed state is not measurable, so a Kalman Filter of the rotor was used to estimate this state. The crossover of the system was increased to 10 rad/s compared to 2 rad/sec for a current governor. Initial computer simulations with a nonlinear engine model indicate a significant decrease in power turbine speed variation with the LQR governor compared to a conventional governor.

Pfeil, W. H.↗
NASA NTRS1985

The application of LQR synthesis techniques to the turboshaft engine control problem

A power turbine governor was designed for a recent-technology turboshaft engine coupled to a modern, articulated rotor system using Linear Quadratic Regulator (LQR) and Kalman Filter (KF) techniques. A linear, state-space model of the engine and rotor system was derived for six engine power settings from flight idle to maximum continuous. An integrator was appended to the fuel flow input to reduce the steady-state governor error to zero. Feedback gains were calculated for the system states at each power setting using the LQR technique. The main rotor tip speed state is not measurable, so a Kalman Filter of the rotor was used to estimate this state. The crossover of the system was increased to 10 rad/s compared to 2 rad/sec for a current governor. Initial computer simulations with a nonlinear engine model indicate a significant decrease in power turbine speed variation with the LQR governor compared to a conventional governor.

Pfeil, W. H.↗
NASA NTRS2012

Adaptive Control of Linear Modal Systems Using Residual Mode Filters and a Simple Disturbance Estimator

Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter.

Balas, Mark↗
NASA NTRSDate not supplied

Sequential Filtering in the Presence of Uniform Measurement Errors

This paper presents a sequential filtering strategy using observations corrupted with uniform measurement noise. While the Kalman filter remains the best linear estimator of the state, other filtering techniques provide minimum variance optimal estimates, a trait only enjoyed by the Kalman filter when the underlying noises are, in fact, Gaussian. This work develops a new approximate optimal estimator for uniform measurement noises. The resulting recursion requires just slightly more computational time to complete a measurement update than the Kalman filter, which generally cannot be claimed by other optimal strategies such as the particle or Gaussian mixture filters.

James S. McCabe↗
NASA NTRSDate not supplied

Sequential Filtering in the Presence of Uniform Measurement Errors

This paper presents a sequential filtering strategy using observations corrupted with uniform measurement noise. While the Kalman filter remains the best linear estimator of the state, other filtering techniques provide minimum variance optimal estimates, a trait only enjoyed by the Kalman filter when the underlying noises are, in fact, Gaussian. This work develops a new approximate optimal estimator for uniform measurement noises. The resulting recursion requires just slightly more computational time to complete a measurement update than the Kalman filter, which generally cannot be claimed by other optimal strategies such as the particle or Gaussian mixture filters.

James S McCabe↗
NASA NTRS2005

Generic Kalman Filter Software

The Generic Kalman Filter (GKF) software provides a standard basis for the development of application-specific Kalman-filter programs. Historically, Kalman filters have been implemented by customized programs that must be written, coded, and debugged anew for each unique application, then tested and tuned with simulated or actual measurement data. Total development times for typical Kalman-filter application programs have ranged from months to weeks. The GKF software can simplify the development process and reduce the development time by eliminating the need to re-create the fundamental implementation of the Kalman filter for each new application. The GKF software is written in the ANSI C programming language. It contains a generic Kalman-filter-development directory that, in turn, contains a code for a generic Kalman filter function; more specifically, it contains a generically designed and generically coded implementation of linear, linearized, and extended Kalman filtering algorithms, including algorithms for state- and covariance-update and -propagation functions. The mathematical theory that underlies the algorithms is well known and has been reported extensively in the open technical literature. Also contained in the directory are a header file that defines generic Kalman-filter data structures and prototype functions and template versions of application-specific subfunction and calling navigation/estimation routine code and headers. Once the user has provided a calling routine and the required application-specific subfunctions, the application-specific Kalman-filter software can be compiled and executed immediately. During execution, the generic Kalman-filter function is called from a higher-level navigation or estimation routine that preprocesses measurement data and post-processes output data. The generic Kalman-filter function uses the aforementioned data structures and five implementation- specific subfunctions, which have been developed by the user on the basis of the aforementioned templates. The GKF software can be used to develop many different types of unfactorized Kalman filters. A developer can choose to implement either a linearized or an extended Kalman filter algorithm, without having to modify the GKF software. Control dynamics can be taken into account or neglected in the filter-dynamics model. Filter programs developed by use of the GKF software can be made to propagate equations of motion for linear or nonlinear dynamical systems that are deterministic or stochastic. In addition, filter programs can be made to operate in user-selectable "covariance analysis" and "propagation-only" modes that are useful in design and development stages.

Lisano, Michael E., II↗
NASA NTRS1971

The estimation of the constituent densities of the upper atmosphere by means of a recursive filtering algorithm.

The structure of the upper atmosphere can be indirectly probed by light in order to determine the global density structure of ozone, aerosols, and neutral atmosphere. Scattered and directly transmitted light is measured by a satellite and is shown to be a nonlinear function of the state which is defined to be a point-wise decomposition of the density profiles. Dynamics are imposed on the state vector and a structured estimation problem is developed. The estimation of these densities is then performed using a linearized Kalman-Bucy filter and a linearized Kushner-Stratonovich filter.

Mcgarty, T. P.↗