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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

The analysis of the pilot's cognitive and decision processes

Articles are presented on pilot performance in zero-visibility precision approach, failure detection by pilots during automatic landing, experiments in pilot decision-making during simulated low visibility approaches, a multinomial maximum likelihood program, and a random search algorithm for laboratory computers. Other topics discussed include detection of system failures in multi-axis tasks and changes in pilot workload during an instrument landing.

Curry, R. E.↗
NASA NTRS1975

Use of LANDSAT imagery for wildlife habitat mapping in northeast and east central Alaska

The author has identified the following significant results. Two scenes were analyzed by applying an iterative cluster analysis to a 2% random data sample and then using the resulting clusters as a training set basis for maximum likelihood classification. Twenty-six and twenty-seven categorical classes, respectively resulted from this process. The majority of classes in each case were quite specific vegetation types; each of these types has specific value as moose habitat.

Lent, P. C.↗
NASA NTRS1975

Stability and control derivatives of the T-37B airplane

Subsonic stability and control derivatives were determined by a modified maximum likelihood estimator from flight data for the longitudinal and lateral-directional modes of the T-37B airplane. Data from two flights, in which 166 stability and control maneuvers were performed, were used in the determination. The configurations investigated were: zero flaps, gear up; half flaps, gear up; full flaps, gear up; and zero flaps, gear down.

Shafer, M. F.↗
NASA NTRS1975

DSN telemetry system performance with convolutionally code data

The results obtained to date and the plans for future experiments for the DSN telemetry system were presented. The performance of the DSN telemetry system in decoding convolutionally coded data by both sequential and maximum likelihood techniques is being determined by testing at various deep space stations. The evaluation of performance models is also an objective of this activity.

Mulhall, B. D. L.↗
NASA NTRS1975

Mapping of the wildland fuel characteristics of the Santa Monica mountains of Southern California

LANDSAT digital data was successfully used to map and evaluate the wildland fuels of the Santa Monica Mountains in Southern California. A mixed classification scheme was used where training areas of known vegetation types were entered and the maximum likelihood classifier run, followed by an evaluation of the results and an unsupervised retraining of the classifier using an image of the probability of misclassification. Estimation of maturity class and crown closure percents of the major cover types were assigned to each computer class by associating the photointerpretation of 159 large scale photo samples with the resultant computer classes using analysis of variance and analysis of categorized data. The result of the computer classification and statistical analysis were then transformed from the LANDSAT Coordinate California State Plane Coordinate system for use in a digital format in the FIRESCOPE data retrieval and fire modeling system.

Nichols, J. D.↗
NASA NTRS1975

Geological mapping in northwestern Saudi Arabia using LANDSAT multispectral techniques

Various computer enhancement and data extraction systems using LANDSAT data were assessed and used to complement a continuing geologic mapping program. Interactive digital classification techniques using both the maximum-likelihood and thresholding statistical approaches achieve very limited success in areas of highly dissected terrain. Computer enhanced imagery developed by color compositing stretched MSS ratio data was constructed for a test site in northwestern Saudi Arabia. Initial results indicate that several igneous and sedimentary rock-types can be discriminated.

Blodget, H. W.↗
NASA NTRS1975

Layered classification techniques for remote sensing applications

The single-stage method of pattern classification utilizes all available features in a single test which assigns the unknown to a category according to a specific decision strategy (such as the maximum likelihood strategy). The layered classifier classifies the unknown through a sequence of tests, each of which may be dependent on the outcome of previous tests. Although the layered classifier was originally investigated as a means of improving classification accuracy and efficiency, it was found that in the context of remote sensing data analysis, other advantages also accrue due to many of the special characteristics of both the data and the applications pursued. The layered classifier method and several of the diverse applications of this approach are discussed.

Swain, P. H.↗
NASA NTRS1975

M-DAS: System for multispectral data analysis

M-DAS is a ground data processing system designed for analysis of multispectral data. M-DAS operates on multispectral data from LANDSAT, S-192, M2S and other sources in CCT form. Interactive training by operator-investigators using a variable cursor on a color display was used to derive optimum processing coefficients and data on cluster separability. An advanced multivariate normal-maximum likelihood processing algorithm was used to produce output in various formats: color-coded film images, geometrically corrected map overlays, moving displays of scene sections, coverage tabulations and categorized CCTs. The analysis procedure for M-DAS involves three phases: (1) screening and training, (2) analysis of training data to compute performance predictions and processing coefficients, and (3) processing of multichannel input data into categorized results. Typical M-DAS applications involve iteration between each of these phases. A series of photographs of the M-DAS display are used to illustrate M-DAS operation.

Johnson, R. H.↗
NASA NTRS1975

Satellite information on Orlando, Florida

A land use map of Orange County, Florida was prepared from EREP photography while LANDSAT and EREP multispectral scanner data were used to provide more detailed information on Orlando and its suburbs. The generalized maps were prepared by tracing the patterns on an overlay, using an enlarging viewer. Digital analysis of the multispectral scanner data was basically the maximum likelihood classification method with training sample input and computer printer mapping of the results. Urban features delineated by the maps are discussed. It is concluded that computer classification, accompanied by human interpretation and manual simplification can produce land use maps which are useful on a regional, county, and city basis.

Hannah, J. W.↗
NASA NTRS1976

Analytical evaluation of tilting proprotor wind tunnel test requirements

Specific test requirements related to the wind tunnel testing of the XV-15 advanced tilt rotor research aircraft were determined. The following analytical tools were developed: (1) digital simulation of the XV-15, incorporating a simplified tunnel support model, control system loop, measurement lags, gust disturbances, and sensor noise, (2) specialization of existing data analysis programs to the high order XV-15 dynamical model (transfer function program, a time series analysis program, an advanced maximum likelihood parameter identification program), (3) several auxiliary programs to provide estimates of damping from transfer functions as well as calculations of model decomposition of system response. The following results were discussed: (1) modelling of the aircraft, instrumentation, and controls, (2) results of the rotor/cantilever wing model and coupled wing, (3) examples of data prediction with system identification techniques, and (4) detailed conclusions and recommendations.

Hall, W. E., Jr.↗
NASA NTRS1975

UHMLE: Program description user guide

A program which computes maximum likelihood estimates for the general normal mixture is introduced. The program allows the user to fix any subsets of the mixture parameters; this fixed set may be redefined at various times in the iteration process. There is considerable time saved in computing the likelihoods if the diagonal form is specified. Hence, in the early iterations the diagonal assumption might be used, changing over to the full covariance mode later in the iteration process for a more refined solution. This flexibility allows the user to choose the sequence of parameter configurations in the iteration process which he feels will optimize the computation time required as well as possibly avoid convergence to suboptimal local maxima of the likelihood function.

Cobely, W. A.↗
NASA NTRS1975

Multidimensional stochastic approximation using locally contractive functions

A Robbins-Monro type multidimensional stochastic approximation algorithm which converges in mean square and with probability one to the fixed point of a locally contractive regression function is developed. The algorithm is applied to obtain maximum likelihood estimates of the parameters for a mixture of multivariate normal distributions.

Lawton, W. M.↗
NASA NTRS1976

Dynamics and identification of flexible aircraft

The equations of motion and a maximum likelihood parameter identification formulation are developed for a flexible aircraft. The various levels of approximation associated with the modal substitution representation of the elastic displacement field are discussed and illustrated when appropriate. The necessary extension of the parameter set of stability and control derivatives due to the aeroelastic effects is obtained.

Wells, W. R.↗
NASA NTRS1975

Mapping forest vegetation with ERTS-1 MSS data and automatic data processing techniques

This study was undertaken with the intent of elucidating the forest mapping capabilities of ERTS-1 MSS data when analyzed with the aid of LARS' automatic data processing techniques. The site for this investigation was the Great Dismal Swamp, a 210,000 acre wilderness area located on the Middle Atlantic coastal plain. Due to inadequate ground truth information on the distribution of vegetation within the swamp, an unsupervised classification scheme was utilized. Initially pictureprints, resembling low resolution photographs, were generated in each of the four ERTS-1 channels. Data found within rectangular training fields was then clustered into 13 spectral groups and defined statistically. Using a maximum likelihood classification scheme, the unknown data points were subsequently classified into one of the designated training classes. Training field data was classified with a high degree of accuracy (greater than 95 percent), and progress is being made towards identifying the mapped spectral classes.

Messmore, J.↗
NASA NTRS1976

Optical pulse timing resolution

This correspondence evaluates the performance of the maximum likelihood (ML) and several suboptimal arrival time estimators for optical pulses. The performance analysis of a 'sliding window' counter followed by a threshold detector is achieved by deriving an expression for the first-crossing density of the output of the counter.

Lee, G.↗
NASA NTRS1975

The decision tree classifier - Design and potential

A new classifier has been developed for the computerized analysis of remote sensor data. The decision tree classifier is essentially a maximum likelihood classifier using multistage decision logic. It is characterized by the fact that an unknown sample can be classified into a class using one or several decision functions in a successive manner. The classifier is applied to the analysis of data sensed by Landsat-1 over Kenosha Pass, Colorado. The classifier is illustrated by a tree diagram which for processing purposes is encoded as a string of symbols such that there is a unique one-to-one relationship between string and decision tree.

Hauska, H.↗