Search NASA⌕ Search

SEARCH · Search NASA

Results for “outliers”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Optical Navigation Attitude Estimation and Calibration Performance Improvement using Outlier Rejection

Spacecraft optical navigation (OpNav) systems process a sequence of images of celestial bodies against a starfield background to estimate the position and velocity of the vehicle. While attitude is sometimes available from an onboard star tracker, it is often desirable to recognize the background stars in the OpNav images to better align the image. While many image processing algorithms exist for finding stars, efficiency and reliability remain key issues in the presence of extended bodies(e.g. the Moon, Earth), especially when attempting to solve the full lost-in-space problem. Some star outliers(stars identified with high residuals)could appear in the camera field of view, however using them in the attitude estimation or camera calibration would lead to less accurate results. Therefore, we require new and robust approaches to remove these outliers before any further processing. The emphasis of the work is on developing a simple and robust iterative technique to detect and reject the outliers which could be found in any frame during the lost in space attitude determination or during the camera calibration. These outliers are determined based on the residuals of the centroids of the detected stars and the corresponding location using the star catalog. If the residuals exceed a predetermined threshold value, the object will be detected as an outlier and will be removed before another attitude determination and calibration iteration is performed. The performance for both attitude determination and on-orbit camera calibration are improved by an almost two-fold increase in accuracy when applying this outlier rejection technique.

OpNav↗
NASA NTRS1987

Robust statistical methods for automated outlier detection

The computational challenge of automating outlier, or blunder point, detection in radio metric data requires the use of nonstandard statistical methods because the outliers have a deleterious effect on standard least squares methods. The particular nonstandard methods most applicable to the task are the robust statistical techniques that have undergone intense development since the 1960s. These new methods are by design more resistant to the effects of outliers than standard methods. Because the topic may be unfamiliar, a brief introduction to the philosophy and methods of robust statistics is presented. Then the application of these methods to the automated outlier detection problem is detailed for some specific examples encountered in practice.

Jee, J. R.↗
NASA NTRS2003

Mining Distance Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule

Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the efficiency is because the time to process non-outliers, which are the majority of examples, does not depend on the size of the data set.

Bay, Stephen D.↗
NASA NTRS2016

System and Method for Outlier Detection via Estimating Clusters

An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.

Iverson, David J.↗
NASA NTRS2019

Casper-1, Part 6: Uncertainty Quantification, Factor Effects, and Outlier Analysis for an On-Board Airplane Trajectory Prediction Function

This report presents data analysis results for a simulation-based approach named CASPEr (Characterization of Airplane State Prediction Error) to characterize the performance of onboard energy state and automation mode prediction functions for terminal area arrival and approach phases of flight over a wide range of conditions. In particular, the results include quantification of energy state (i.e., altitude and airspeed) prediction performance, models for prediction performance as a function of initial energy state (i.e., initial altitude, airspeed, and weight) and weather factors, and analysis of outlier prediction performance. Wind speed, wind direction, and wind gradient were found to be major factors in energy state prediction performance. Initial energy and gust intensity were also significant factors in airspeed prediction performance. Furthermore, the results suggest that errors in automation mode prediction may be a major contributor to outlier prediction performance.

Torres-Pomales, Wilfredo↗
NASA NTRS1992

Clustering methods for removing outliers from vision-based range estimates

The present approach to the automation of helicopter low-altitude flight uses one or more passive imaging sensors to extract environmental obstacle information; this is then processed via computer-vision techniques to yield a time-varying map of range to obstacles in the sensor's field of view along the vehicle's flight path. Attention is given to two related techniques which can eliminate outliers from a sparse range map, clustering sparse range-map information into different spatial classes that rely on a segmented and labeled image to aid in spatial classification within the image plane.

Hussien, B.↗
NASA NTRS2004

Realtime Absolute Orientation with Outlier Rejection for Visual Odometry and Mesh Merging

This paper addresses a high level spatio-temporal problem, namely "absolute orientation", which arises in visual-odometry (using stereo), or registering two models created by different Structure from Motion (SFM) reconstructions. We compare the very popular method due to Horn using quaternions and our own independently derive method using the orthonormal rotation matrix R. We also introduce a novel approach for outlier rejection using spectral clustering.

Bandari, Esfandiar↗
NASA NTRS1990

Outliers of dust along the southern margin of the Tharsis region, Mars

This work documents the distinctive characteristics of two locations near the southeastern margin of the Tharsis low thermal inertia region on Mars that support the inferred south-to-north migration of dust deposits under present climatic conditions (Christensen, 1986, 1988). Low thermal inertia and high albedo indicate the presence of fine-grained surface materials on the highly fractured terrain of northern Claritas Fossae with a narrow zone of fine material connecting this location to the southern margin of the Tharsis low thermal inertia region. A similar deposit occurs in Sinai Planum on an isolated exposure of fractured terrain. In comparison to their surroundings, both locations have higher visual albedo, lower thermal inertia, and higher topographic relief coupled with lower reflectivity and higher roughness at radar wavelengths.

Zimbelman, J. R.↗
NASA NTRS2006

Improving Neural Network Generalization Ability Using Outlier Analysis and Voronoi Tessellation

The data used in this study was obtained from the Sloan Digital Sky Survey (SDSS), which provides astronomers with what is currently the most extensive mapping of the universe, covering 25% of the sky and cataloging the spectral properties (e.g., luminosity, color, surface temperature) of over 100 million celestial objects. Images generated by the SDSS are collected through 5 filters named u, g, r, l, and z that have respective wavelengths of 3540, 4750, 6222, 7632, and 9049 A. By measuring the photometric redshifts of the aforementioned wavelengths of a galaxy, astronomers can ascertain the extent to which galaxy is receding from which the distance to the galaxy can be calculated. Data collected for a small select group of galaxies (approximately 30, 000) contains accurate measurements of the galaxies' redshifts, in addition to measurements of their spectral properties. The above dataset containing both redshift measurements as well as spectral properties of the selected galaxies served as the training set for the purposes of this study; the data set containing only the spectra properties of a separate group of galaxies served as the test set.

Ho, Michelle↗
NASA NTRS2006

Gamma-Ray Burst Associated Supernovae: Outliers Become Mainstream

During the last eight years a clear connection has been established-between the two most powerful explosions in our Universe: core-collapse supernovae (SNe) and long gamma ray bursts (GRBs). Theory suggests4 that every GRB is simultaneously accompanied by a SN, but in only a few nearby cases have these two phenomena been observed together. We report the discovery and daily monitoring of SN 2006aj associated with the GRB 060218. Because the event was the second closest GRB, both explosions could be examined in detail. GRB 060218 had an unusually soft spectrum, long duration, and a total energy 100 to 1000 times less than most other GRBs. Yet SN 2006aj was similar to those in other GRBs, aside from rising more rapidly and being approximately 40% fainter. Taken together, these observations suggest that GRBs have two components: a broad, energetic, but only mildly relativistic outflow that makes a SN, and a more narrowly focused, highly relativistic jet responsible for the GRB. The properties of the GRB jet apparently vary greatly from event to event, while the broad SN outflow varies much less. Low energy transients like GRB 060218 may be the most common events in the Universe.

Pian, E.↗