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At least 379 records · Page 21

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NASA NTRS1995

Equalization and detection for digital communication over nonlinear bandlimited satellite communication channels

This dissertation evaluates receiver-based methods for mitigating the effects due to nonlinear bandlimited signal distortion present in high data rate satellite channels. The effects of the nonlinear bandlimited distortion is illustrated for digitally modulated signals. A lucid development of the low-pass Volterra discrete time model for a nonlinear communication channel is presented. In addition, finite-state machine models are explicitly developed for a nonlinear bandlimited satellite channel. A nonlinear fixed equalizer based on Volterra series has previously been studied for compensation of noiseless signal distortion due to a nonlinear satellite channel. This dissertation studies adaptive Volterra equalizers on a downlink-limited nonlinear bandlimited satellite channel. We employ as figure of merits performance in the mean-square error and probability of error senses. In addition, a receiver consisting of a fractionally-spaced equalizer (FSE) followed by a Volterra equalizer (FSE-Volterra) is found to give improvement beyond that gained by the Volterra equalizer. Significant probability of error performance improvement is found for multilevel modulation schemes. Also, it is found that probability of error improvement is more significant for modulation schemes, constant amplitude and multilevel, which require higher signal to noise ratios (i.e., higher modulation orders) for reliable operation. The maximum likelihood sequence detection (MLSD) receiver for a nonlinear satellite channel, a bank of matched filters followed by a Viterbi detector, serves as a probability of error lower bound for the Volterra and FSE-Volterra equalizers. However, this receiver has not been evaluated for a specific satellite channel. In this work, an MLSD receiver is evaluated for a specific downlink-limited satellite channel. Because of the bank of matched filters, the MLSD receiver may be high in complexity. Consequently, the probability of error performance of a more practical suboptimal MLSD receiver, requiring only a single receive filter, is evaluated.

Gutierrez, Alberto, Jr.↗

NASA NTRS2004

CDGPS-Based Relative Navigation for Multiple Spacecraft

This thesis investigates the use of Carrier-phase Differential GPS (CDGPS) in relative navigation filters for formation flying spacecraft. This work analyzes the relationship between the Extended Kalman Filter (EKF) design parameters and the resulting estimation accuracies, and in particular, the effect of the process and measurement noises on the semimajor axis error. This analysis clearly demonstrates that CDGPS-based relative navigation Kalman filters yield good estimation performance without satisfying the strong correlation property that previous work had associated with "good" navigation filters. Several examples are presented to show that the Kalman filter can be forced to create solutions with stronger correlations, but these always result in larger semimajor axis errors. These linear and nonlinear simulations also demonstrated the crucial role of the process noise in determining the semimajor axis knowledge. More sophisticated nonlinear models were included to reduce the propagation error in the estimator, but for long time steps and large separations, the EKF, which only uses a linearized covariance propagation, yielded very poor performance. In contrast, the CDGPS-based Unscented Kalman relative navigation Filter (UKF) handled the dynamic and measurement nonlinearities much better and yielded far superior performance than the EKF. The UKF produced good estimates for scenarios with long baselines and time steps for which the EKF would diverge rapidly. A hardware-in-the-loop testbed that is compatible with the Spirent Simulator at NASA GSFC was developed to provide a very flexible and robust capability for demonstrating CDGPS technologies in closed-loop. This extended previous work to implement the decentralized relative navigation algorithms in real time.

Mitchell, Megan Leigh↗

NASA NTRS1965

On the a priori information in sequential estimation problems.

Error effect in a priori information on sequential estimate variance of linear system states, noting optimal filter synthesis

SEQUENTIAL ANALYSIS↗

NASA NTRS1985

Temperature profile retrieval by two-dimensional filtering

A two-dimensional spatial filter, optimum in the minimum-mean-square error sense, has been used to retrieve atmospheric temperature profiles from TIROS-N and NOAA-6 Microwave Sounding Unit measurements. This approach considers correlations both in the vertical and horizontal (along the orbital track) directions. It is found that lower retrieval errors result from the 2-D technique, compared with the 1-D technique, especially in the troposphere. The additional horizontal information used in the 2-D formulation substantially improves temperature retrievals over a severe cold front where vertical correlations are lessened. The improved results of the 2-D filter in the upper troposphere point to the presence of profile components in the null space of the weighting functions that are horizontally correlated with the observed data.

Nathan, K. S.↗

NASA NTRS1987

The use of a numerical filter to correct airborne temperature measurements for the effects of sensor lag

A numerical filter for transforming measured temperature signals into a close approximation of the actual temperature signal is described. The filter is derived by minimizing the mean-square error of the system, and assuming a knowledge of the characteristics of the sensing element and its housing. The equation representing the frequency-response function of the numerical filter is given. Input and output spectra for a filter applied to a case with negligible noise and a noise level of 1.5 percent of the total power in the input spectrum are analyzed, and the numerical weights for these two cases are calculated. Phase angle and gain for the entire system are examined. It is noted that the filter can enhance spectral components as high as 8 Hz with little phase and gain degradation over the bandwidth.

Ritter, John A.↗

NASA NTRS2006

Spatiotemporal Filtering Using Principal Component Analysis and Karhunen-Loeve Expansion Approaches for Regional GPS Network Analysis

Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering.We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

displacement↗

NASA NTRS1972

Nonlinear and adaptive estimation techniques in reentry

The development and testing of nonlinear and adaptive estimators for reentry (e.g. space shuttle) navigation and model parameter estimation or identification are reported. Of particular interest is the identifcation of vehicle lift and drag characteristics in real time. Several nonlinear filters were developed and simulated. Adaptive filters for the real time identification of vehicle lift and drag characteristics, and unmodelable acceleration, were also developed and tested by simulation. The simulations feature an uncertain system environment with rather arbitrary model errors, thus providing a definitive test of estimator performance. It was found that nonlinear effects are indeed significant in reentry trajectory estimation and a nonlinear filter is demonstrated which successfully tracks through nonlinearities without degrading the information content of the data. Under the same conditions the usual extended Kalman filter diverges and is useless. The J-adaptive filter is shown to successfully track errors in the modeled vehicle lift and drag characteristics. The same filter concept is also shown to track successfully through rather arbitrary model errors, including lift and drag errors, vehicle mass errors, atmospheric density errors, and wind gust errors.

Jazwinski, A. H.↗

NASA NTRS1987

Optimizing a continuously variable filter in a hybrid optical correlator

In contrast to binary filters, continuously variable optical filters offer an ability to conpensate for certain imperfections in the optics of a hybrid correlator. Arbitrary static phase errors are introduced into a model of a phase-only filtering hybrid correlator, and a method of discovering a correction for them simulated. By a recursive technique a first approximation to the impulse's matched filter is adjusted (allowed to relax) so as to produce successively more localized distribution of the output in the correlation plane. The method is motivated by the development of continuously-variable phase-only spatial light modulators, but it is applicable to amplitude modulators and -with appropriate modification -- to binary modulators as well. The technique is robust against the form of the system's departure from ideal behavior.

Juday, Richard D.↗

NASA NTRS2011

Radar and Lidar Radar DEM

Using radar and lidar data, the aim is to improve 3D rendering of terrain, including digital elevation models (DEM) and estimates of vegetation height and biomass in a variety of forest types and terrains. The 3D mapping of vegetation structure and the analysis are useful to determine the role of forest in climate change (carbon cycle), in providing habitat and as a provider of socio-economic services. This in turn will lead to potential for development of more effective land-use management. The first part of the project was to characterize the Shuttle Radar Topography Mission DEM error with respect to ICESat/GLAS point estimates of elevation. We investigated potential trends with latitude, canopy height, signal to noise ratio (SNR), number of LiDAR waveform peaks, and maximum peak width. Scatter plots were produced for each variable and were fitted with 1st and 2nd degree polynomials. Higher order trends were visually inspected through filtering with a mean and median filter. We also assessed trends in the DEM error variance. Finally, a map showing how DEM error was geographically distributed globally was created.

remote sensing↗

NASA NTRS2008

Identifying Bearing Rotodynamic Coefficients Using an Extended Kalman Filter

An Extended Kalman Filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter's performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates for the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates for the cross-coupled damping coefficients were the least accurate.

Miller, Brad A.↗

NASA NTRS2008

Identifying Bearing Rotordynamic Coefficients using an Extended Kalman Filter

An Extended Kalman Filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor-dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter s performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor-bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates for the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates for the cross-coupled damping coefficients were the least accurate.

Miller, Bard A.↗

NASA NTRS2008

Identifying Bearing Rotordynamic Coefficients using an Extended Kalman Filter

Miller, Brad A.↗

NASA NTRS1974

Microwave landing system requirements for STOL operations

The operational/functional requirements for the new Microwave Landing System (MLS) are examined for STOL operations. The study utilizes a nonlinear six-degree-of-freedom simulation of a De Havilland Buffalo C-8A aircraft and automatic flight control system to assess the MLS/STOL accuracy, coverage, and data rate requirements for the azimuth, DME, primary elevation, and flare elevation functions. The aircraft performance is statistically determined for representative curved flight paths through touchdown. A range of MLS errors and coverages, environmental disturbances, and navigation filtering are investigated. The study indicates that STOL applications do not place any unique requirements on the MLS.

Burrous, C. N.↗

NASA NTRS1981

Estimation of trends

The application of statistical methods to recorded ozone measurements. The effects of a long term depletion of ozone at magnitudes predicted by the NAS is harmful to most forms of life. Empirical prewhitening filters the derivation of which is independent of the underlying physical mechanisms were analyzed. Statistical analysis performs a checks and balances effort. Time series filters variations into systematic and random parts, errors are uncorrelated, and significant phase lag dependencies are identified. The use of time series modeling to enhance the capability of detecting trends is discussed.

Source record↗

NASA NTRS1986

Digital Filter Separates Signal From Noise

Variance of signal-estimation error minimized. Mathematical technique extracts best estimates of signal component from periodic digital samples of signal plus noise. Technique combines Kalman- and smoothingfilter algorithms to minimize mean-square estimation error based on past, present, and predicted samples of signal plus noise. Technique useful in image analysis and other applications involving processing of noisy signals.

Lear, W. M.↗

NASA NTRS1992

Open-Loop Acquisition Of Suppressed Carrier Signals

Three digital techniques for open-loop acquisition of suppressed carrier signals in presence of binary-phase-shift-keyed (BPSK) modulation representing non-return-to-zero (NRZ) data symbols. Based on assumption that offset, tau, between timing of symbols and clock in receiver unknown, but fixed fraction of symbol period, T. In all three, fast Fourier transforms (FFT's) performed on outputs of integrate-and-dump filters that are parts of Costas-type loops used to demodulate BPSK signals. Timing of integrate-and-dump filters constant. Outputs mixed to obtain an error signal.

Shah, Biren N.↗

NASA NTRS1992

Rain compensation algorithm using adaptive linear prediction

Rain compensation algorithm using adaptive linear prediction is presented in viewgraph form. Topics covered include AMT scenario, summary of AMT-RCA, empirical basis for reducing attenuation extrapolation errors, and candidate adaptive 1-pole prediction filter for application to AMT-RCA (at the mobile terminal (MT)).

Satorius, Edgar↗

NASA NTRS1991

Headphone localization of speech stimuli

Recently, three dimensional acoustic display systems have been developed that synthesize virtual sound sources over headphones based on filtering by Head-Related Transfer Functions (HRTFs), the direction-dependent spectral changes caused primarily by the outer ears. Here, 11 inexperienced subjects judged the apparent spatial location of headphone-presented speech stimuli filtered with non-individualized HRTFs. About half of the subjects 'pulled' their judgements toward either the median or the lateral-vertical planes, and estimates were almost always elevated. Individual differences were pronounced for the distance judgements; 15 to 46 percent of stimuli were heard inside the head with the shortest estimates near the median plane. The results infer that most listeners can obtain useful azimuth information from speech stimuli filtered by nonindividualized RTFs. Measurements of localization error and reversal rates are comparable with a previous study that used broadband noise stimuli.

Begault, Durand R.↗