Search NASA⌕ Search

SEARCH · Search NASA

Results for “Nonlinear filtering”

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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Post-Flight Estimation of Motion of Space Structures: Part 1

A computer program estimates the relative positions and orientations of two space structures from data on the angular positions and distances of fiducial objects on one structure as measured by a target tracking electronic camera and laser range finders on another structure. The program is written specifically for determining the relative alignments of two antennas, connected by a long truss, deployed in outer space from a space shuttle. The program is based partly on transformations among the various coordinate systems involved in the measurements and on a nonlinear mathematical model of vibrations of the truss. The program implements a Kalman filter that blends the measurement data with data from the model. Using time series of measurement data from the tracking camera and range finders, the program generates time series of data on the relative position and orientation of the antennas. A similar program described in a prior NASA Tech Briefs article was used onboard for monitoring the structures during flight. The present program is more precise and designed for use on Earth in post-flight processing of the measurement data to enable correction, for antenna motions, of scientific data acquired by use of the antennas.

Brugarolas, Paul↗
NASA NTRS1993

Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

Parker, B. Eugene, Jr.↗
NASA NTRS2000

Validation of a High-Order Prefactored Compact Scheme on Nonlinear Flows with Complex Geometries

Three benchmark problems are solved using a sixth-order prefactored compact scheme employing an explicit 10th-order filter with optimized fourth-order Runge-Kutta time stepping. The problems solved are the following: (1) propagation of sound waves through a transonic nozzle; (2) shock-sound interaction; and (3) single airfoil gust response. In the first two problems, the spatial accuracy of the scheme is tested on a stretched grid, and the effectiveness of boundary conditions is shown. The solution stability and accuracy near a shock discontinuity is shown as well. Also, 1-D nonlinear characteristic boundary conditions will be evaluated. In the third problem, a nonlinear Euler solver will be used that solves the equations in generalized curvilinear coordinates using the chain rule transformation. This work, continuing earlier work on flat-plate cascades and Joukowski airfoils, will focus mainly on the effect of the grid and boundary conditions on the accuracy of the solution. The grids were generated using a commercially available grid generator, GridPro/az3000.

Hixon, Ray↗
NASA NTRS1973

Estimation and detection of signals in multiplicative noise

A class of detection-estimation problems on matrix Lie groups is defined in which the observation noise is multiplicative in nature. By examining the differential versions of the hypotheses, which are bilinear in nature, it is possible to derive the relevant likelihood ratio formula and the associated optimal estimation equations for the signal given the observations and the assumption that the signal is present. These estimation equations are of interest in their own right, in that they represent a finite dimensional optimal solution to a nonlinear estimation problem and can be viewed as consisting of a Kalman-Bucy filter along with the on-line computation of the solution of the associated Riccati equation, which is driven by the observations. The usefulness of these results is illustrated via an example concerning the detection of an actuator failure in a rigid body rotational control system.

Willsky, A. S.↗
NASA NTRS1979

Radiometric correction and equalization of satellite digital data

Satellite digital data from Landsat and NOAA satellites is often marred by striping or streaking errors due to variations in the response of the radiometric sensors. In this paper, we discuss the equalization of the digital data as a preprocessing step, prior to image enhancement or automatic classification. The methods described make use of statistics of the data itself to generate nonlinear or linear memory-less equalization algorithms. These algorithms, by contrast to multidimensional filtering, do not result in a loss of spatial resolution. Examples of applications to Landsat and NOAA-3 thermal infrared data are given and illustrated.

Algazi, V. R.↗
NASA NTRS1987

A dual-processor multi-frequency implementation of the FINDS algorithm

This report presents a parallel processing implementation of the FINDS (Fault Inferring Nonlinear Detection System) algorithm on a dual processor configured target flight computer. First, a filter initialization scheme is presented which allows the no-fail filter (NFF) states to be initialized using the first iteration of the flight data. A modified failure isolation strategy, compatible with the new failure detection strategy reported earlier, is discussed and the performance of the new FDI algorithm is analyzed using flight recorded data from the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. The results show that low level MLS, IMU, and IAS sensor failures are detected and isolated instantaneously, while accelerometer and rate gyro failures continue to take comparatively longer to detect and isolate. The parallel implementation is accomplished by partitioning the FINDS algorithm into two parts: one based on the translational dynamics and the other based on the rotational kinematics. Finally, a multi-rate implementation of the algorithm is presented yielding significantly low execution times with acceptable estimation and FDI performance.

Godiwala, Pankaj M.↗
NASA NTRS2019

Acoustic Analysis of Sensor Ports

Acquiring data in rocket engines that is representative of the actual dynamic environment can often be difficult due to a multitude of influences. One source of contamination that is often not considered entirely is the response associated with the acoustic cavity created by a sensor offset. It is common to offset a sensor due to various reasons such as for mounting accessibility, thermal isolation, shock reduction, or prevention of debris impingement. While estimating the natural frequency of the acoustic cavity is straightforward, limited analysis has been described on the determination of the overall frequency response. The sensor port design approach usually attempts to ensure the port is short enough such that the acoustic response is negligible near the frequency-of-interest, but this requires knowledge of the frequency response and simple rules-of-thumb are not always guaranteed. Data correction and/or data interpretation is also often desired for an unsatisfactory response. The limited response analysis in the literature only offers approximations or neglects important contributions. A new approach is devised theoretically and computationally that captures the true acoustic response of a sensor port. This paper summarizes the acoustics background, the port response theoretical development, and provides comparisons of a port acoustic response using an analytical model and computational acoustics. The effects of nonlinear acoustics are also examined. Additionally, the paper summarizes the design of a specialized filter using the predicted sensor port response that can be applied to data for correction.

Casiano, M. J.↗
NASA NTRSDate not supplied

Turbulence Effects on Shaped Booms: Finite Impulse Response Filter Development

Numerical simulations of propagation through turbulent atmospheres can quantify effects on ground waveforms, but such simulations are computationally expensive. To enable quick turnaround analyses as required by NASA’s Quesst Mission, updating the N-wave filtering approach developed by researchers at The Pennsylvania State University to include shaped booms is proposed as an alternative method for estimating turbulence effects on acoustic metrics more quickly. Beginning with a nearfield pressure cylinder modeled after the on-design X-59 configuration, a database of propagation results at 45 turbulence conditions was compiled using nonlinear turbulence propagation modeling code (KZKFourier) and used as input to a process for generating finite impulse response (FIR) filters. Ground waveforms distorted by turbulence were selected to represent mean and mean±standard deviation levels for six metrics, and corresponding FIR filters were generated through a matrix deconvolution process. In order to evaluate how well the FIR filters perform, additional KZKFourier verification cases were devised with different input conditions, and results used as a benchmark. Convolution of shaped boom waveforms modeled using nonturbulent propagation simulations with the new FIR filters showed better agreement on average with KZKFourier statistical results than the N-wave-based FIR filters.

turbulence↗
NASA NTRS1984

Applying optimization software libraries to engineering problems

Nonlinear programming, preliminary design problems, performance simulation problems trajectory optimization, flight computer optimization, and linear least squares problems are among the topics covered. The nonlinear programming applications encountered in a large aerospace company are a real challenge to those who provide mathematical software libraries and consultation services. Typical applications include preliminary design studies, data fitting and filtering, jet engine simulations, control system analysis, and trajectory optimization and optimal control. Problem sizes range from single-variable unconstrained minimization to constrained problems with highly nonlinear functions and hundreds of variables. Most of the applications can be posed as nonlinearly constrained minimization problems. Highly complex optimization problems with many variables were formulated in the early days of computing. At the time, many problems had to be reformulated or bypassed entirely, and solution methods often relied on problem-specific strategies. Problems with more than ten variables usually went unsolved.

Healy, M. J.↗
NASA NTRS2006

An Ensemble-Based Smoother with Retrospectively Updated Weights for Highly Nonlinear Systems

Monte Carlo computational methods have been introduced into data assimilation for nonlinear systems in order to alleviate the computational burden of updating and propagating the full probability distribution. By propagating an ensemble of representative states, algorithms like the ensemble Kalman filter (EnKF) and the resampled particle filter (RPF) rely on the existing modeling infrastructure to approximate the distribution based on the evolution of this ensemble. This work presents an ensemble-based smoother that is applicable to the Monte Carlo filtering schemes like EnKF and RPF. At the minor cost of retrospectively updating a set of weights for ensemble members, this smoother has demonstrated superior capabilities in state tracking for two highly nonlinear problems: the double-well potential and trivariate Lorenz systems. The algorithm does not require retrospective adaptation of the ensemble members themselves, and it is thus suited to a streaming operational mode. The accuracy of the proposed backward-update scheme in estimating non-Gaussian distributions is evaluated by comparison to the more accurate estimates provided by a Markov chain Monte Carlo algorithm.

Monte Carlo↗
NASA NTRS2021

Development of an SE(3)-based Rigid Body Pose Estimation Scheme for Unknown Moments of Inertia in a Spacecraft

In real-world applications, the consideration of state and parameter uncertainties is an important part of any navigation and control system. Uncertainties can arise because of the instruments, communication systems, or external disturbances. Parameter uncertainty can be due to uncertainties in the data or the calibration process used. The objective of this research is to develop a rigid body pose estimation scheme on nonlinear manifolds of rigid body motion groups for unknown mass properties moments of inertia with application in space launch. An unscented Kalman filter is developed on special Euclidean space and their tangent bundle to address uncertainties in the states and inertia properties. This estimator accounts for uncertainties, while considering rotational-translational coupling, and avoids singularity or non-uniqueness issues. Since this estimator is based on the nonlinear manifolds on which rigid body motion evolves, it is reliable and is expected to result in high level of accuracy. The estimators are validated on a generic model of Logistic Module (LM) and a cuboid spacecraft model. The algorithms can then be tested on Dragon XL, HTV-X, and possibly other spacecraft.

Moments of Inertia↗
DOE OSTI2025

PySolate : A Python‐Based Thresholding Tool to Denoise or Designal Seismic Waveforms Based on the Continuous Wavelet Transform

PySolate is a Python‐based toolset that implements the continuous wavelet transform and nonlinear thresholding operations to denoise or designal seismic data, following Langston and Mousavi (2019). This filtering approach can remove microseismic noise to isolate intermediate‐period seismic signals that are key to enabling full‐waveform modeling and analysis of smaller‐magnitude regional events. This approach is best for the application to signals with frequency or time separation of signal and noise, in contrast to Fourier analysis, which is effective when signal and noise are separated in frequency. We demonstrate the Python toolset using the six announced Democratic People’s Republic of Korea declared nuclear tests, showing the effectiveness of isolating the seismic signal compared to standard bandpass filtering. In conclusion, we also demonstrate the ease of using the toolset with any Python processing tools.

Asia↗
NASA NTRS1986

Frequency domain laser velocimeter signal processor

The features and performance parameters of the frequency domain laser velocimeter signal processor (FDSP) for examining flow phenomena are described. A candidate for replacing the standard signal processor, the FDSP can function receiving bursts of as few as 150 photons in an instantaneous mode or down to a photon resolved regime for an averaging mode. the FDSP has a nonlinear A/D converter and data storage shift registers (high speed recorder group), an elliptic digital filter bank, a controlling microprocessor and frequency and signal gain control circuits (low speed data processing group). The high speed group amplifies, digitizes, identifies and stores burst waveforms and the low speed group identifies the signal frequency within the burst after the signal passes through digital filters. The FDSP can operate in a near-autonomous mode using photon correlation techniques and requires no human intervention for calibration.

Meyers, J. F.↗
NASA NTRS1987

Dynamic sensing of 6-axis external force and moment applied to a robot end

A dynamic force sensing method is proposed for a robot manipulator. A 6-axis wrist force sensor which is commonly used for robot force sensing does not take the direct measurement of the force and moment applied to an end effector externally from the environment of the manipulator, but takes the resultant of the gravity, the inertial force and moment on the end effector, and the external force and moment. The separation of gravitational and inertial force and moment from the measurement of the force sensor is crucial to achieving dynamically accurate force sensing and fast manipulator motion under force feedback control. A mathematical model is derived for the force sensing process and the separation of the force and moment as a nonlinear optimal state estimation problem formulated. Then an approximate solution is given using extended Kalman filtering techniques.

Yokota, Masaru Uchiyama↗
NASA NTRS1990

Performances Of Fixed-Lag Phase-Smoothing Algorithms

Report discusses performances of fixed-lag nonlinear smoothing algorithms applied to estimation of phase and frequency of sinusoidal carrier signal with process phase noise and additive observation phase noise. Algorithm of type considered functions as suboptimal filter operating on received signal. Algorithm developed for system in which signal sampled at discrete times. Process noise assumed to be Gaussian with zero mean and independent of observation noise. Nonlinear smoothing equations derived in customary matrix-and-vector forms.

Kumar, Rajendra↗
NASA NTRS1993

Lasers '92; Proceedings of the International Conference on Lasers and Applications, 15th, Houston, TX, Dec. 7-10, 1992

Papers from the conference are presented, and the topics covered include the following: x-ray lasers, excimer lasers, chemical lasers, high power lasers, blue-green lasers, dye lasers, solid state lasers, semiconductor lasers, gas and discharge lasers, carbon dioxide lasers, ultrafast phenomena, nonlinear optics, quantum optics, dynamic gratings and wave mixing, laser radar, lasers in medicine, optical filters and laser communication, optical techniques and instruments, laser material interaction, and industrial and manufacturing applications.

Wang, Charles P.↗
NASA NTRS2004

Nonlinear optics and crystalline whispering gallery mode resonators

We report on our recent results concerning fabrication of high-Q whispering gallery mode (WGM) crystalline resonators, and discuss some possible applications of lithium niobate WGM resonators in nonlinear optics and photonics. In particular, we demonstrate experimentally a tunable third-order optical filter fabricated from the three metalized resonators; and report observation of parametric frequency dobuling in a WGM resonator made of periodically poled lithium niobate (PPLN).

whispering gallery mode↗
DOE OSTI2024

Asymptotic consistency of the WSINDy algorithm in the limit of continuum data

In this work we study the asymptotic consistency of the weak-form sparse identification of nonlinear dynamics algorithm (WSINDy) in the identification of differential equations from noisy samples of solutions. We prove that the WSINDy estimator is unconditionally asymptotically consistent for a wide class of models that includes the Navier–Stokes, Kuramoto–Sivashinsky and Sine–Gordon equations. We thus provide a mathematically rigorous explanation for the observed robustness to noise of weak-form equation learning. Conversely, we also show that, in general, the WSINDy estimator is only conditionally asymptotically consistent, yielding discovery of spurious terms with probability one if the noise level exceeds a critical threshold σ c . We provide explicit bounds on σ c in the case of Gaussian white noise and we explicitly characterize the spurious terms that arise in the case of trigonometric and/or polynomial libraries. Furthermore, we show that, if the data is suitably denoised (a simple moving average filter is sufficient), then asymptotic consistency is recovered for models with locally-Lipschitz, polynomial-growth nonlinearities. Our results reveal important aspects of weak-form equation learning, which may be used to improve future algorithms. We demonstrate our findings numerically using the Lorenz system, the cubic oscillator, a viscous Burgers-growth model and a Kuramoto–Sivashinsky-type high-order PDE.

asymptotic consistency↗