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Thompson, David R.

Publications and source records attributed to Thompson, David R..

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Two Years Onboard the MER Opportunity Rover

The Autonomous Exploration for Gathering Increased Science (AEGIS) system provides automated data collection for planetary rovers. AEGIS is currently being used onboard the Mars Exploration Rover (MER) mission's Opportunity to provide autonomous targeting of the MER Panoramic camera. Prior to AEGIS, targeted data was collected in a manual fashion where targets were manually identified in images transmitted to Earth and the rover had to remain in the same location for one to several communication cycles. AEGIS enables targeted data to be rapidly acquired with no delays for ground communication. Targets are selected by AEGIS through the use of onboard data analysis techniques that are guided by scientist-specified objectives. This paper provides an overview of the how AEGIS has been used on the Opportunity rover, focusing on usage that occurred during a 21 kilometer historic trek to the Mars Endeavour crater.

Mars Exploration Rover (MER)↗
NASA NTRS2012

Agile Science Operations: A New Approach for Primitive Exploration Bodies

Primitive body exploration missions such as potential Comet Surface Sample Return or Trojan Tour and Rendezvous would challenge traditional operations practices. Earth-based observations would provide only basic understanding before arrival and many science goals would be defined during the initial rendezvous. It could be necessary to revise trajectories and observation plans to quickly characterize the target for safe, effective observations. Detection of outgassing activity and monitoring of comet surface activity are even more time constrained, with events occurring faster than round-trip light time. "Agile science operations" address these challenges with contingency plans that recognize the intrinsic uncertainty in the operating environment and science objectives. Planning for multiple alternatives can significantly improve the time required to repair and validate spacecraft command sequences. When appropriate, time-critical decisions can be automated and shifted to the spacecraft for immediate access to instrument data. Mirrored planning systems on both sides of the light-time gap permit transfer of authority back and forth as needed. We survey relevant science objectives, identifying time bottlenecks and the techniques that could be used to speed missions' reaction to new science data. Finally, we discuss the results of a trade study simulating agile observations during flyby and comet rendezvous scenarios. These experiments quantify instrument coverage of key surface features as a function of planning turnaround time. Careful application of agile operations techniques can play a significant role in realizing the Decadal Survey plan for primitive body exploration

Agile science↗
NASA NTRS2012

Automatic Estimation of Volcanic Ash Plume Height using WorldView-2 Imagery

We explore the use of machine learning, computer vision, and pattern recognition techniques to automatically identify volcanic ash plumes and plume shadows, in WorldView-2 imagery. Using information of the relative position of the sun and spacecraft and terrain information in the form of a digital elevation map, classification, the height of the ash plume can also be inferred. We present the results from applying this approach to six scenes acquired on two separate days in April and May of 2010 of the Eyjafjallajokull eruption in Iceland. These results show rough agreement with ash plume height estimates from visual and radar based measurements.

pattern recognition↗
NASA NTRS2012

Classification of ASKAP Vast Radio Light Curves

The VAST survey is a wide-field survey that observes with unprecedented instrument sensitivity (0.5 mJy or lower) and repeat cadence (a goal of 5 seconds) that will enable novel scientific discoveries related to known and unknown classes of radio transients and variables. Given the unprecedented observing characteristics of VAST, it is important to estimate source classification performance, and determine best practices prior to the launch of ASKAP's BETA in 2012. The goal of this study is to identify light curve characterization and classification algorithms that are best suited for archival VAST light curve classification. We perform our experiments on light curve simulations of eight source types and achieve best case performance of approximately 90% accuracy. We note that classification performance is most influenced by light curve characterization rather than classifier algorithm.

radio astronomy↗
NASA NTRS2011

Multiclass Continuous Correspondence Learning

We extend the Structural Correspondence Learning (SCL) domain adaptation algorithm of Blitzer er al. to the realm of continuous signals. Given a set of labeled examples belonging to a 'source' domain, we select a set of unlabeled examples in a related 'target' domain that play similar roles in both domains. Using these 'pivot samples, we map both domains into a common feature space, allowing us to adapt a classifier trained on source examples to classify target examples. We show that when between-class distances are relatively preserved across domains, we can automatically select target pivots to bring the domains into correspondence.

correspondence learning↗
NASA NTRS2011

Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio Transients

We present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses adaptive eigenbases to combine 1) prior knowledge about uninteresting signals with 2) online estimation of the current data properties to enable highly sensitive and precise detection of novel signals. We apply the method to the problem of detecting fast transient radio anomalies and compare it to current alternative algorithms. Tests based on observations from the Parkes Multibeam Survey show both effective detection of interesting rare events and robustness to known false alarm anomalies.

anomaly detection↗
NASA NTRS2011

Current-Sensitive Path Planning for an Underactuated Free-Floating Ocean Sensorweb

This work investigates multi-agent path planning in strong, dynamic currents using thousands of highly under-actuated vehicles. We address the specific task of path planning for a global network of ocean-observing floats. These submersibles are typified by the Argo global network consisting of over 3000 sensor platforms. They can control their buoyancy to float at depth for data collection or rise to the surface for satellite communications. Currently, floats drift at a constant depth regardless of the local currents. However, accurate current forecasts have become available which present the possibility of intentionally controlling floats' motion by dynamically commanding them to linger at different depths. This project explores the use of these current predictions to direct float networks to some desired final formation or position. It presents multiple algorithms for such path optimization and demonstrates their advantage over the standard approach of constant-depth drifting.

oceanography↗
NASA NTRS2011

Multiple-Beam Detection of Fast Transient Radio Sources

A method has been designed for using multiple independent stations to discriminate fast transient radio sources from local anomalies, such as antenna noise or radio frequency interference (RFI). This can improve the sensitivity of incoherent detection for geographically separated stations such as the very long baseline array (VLBA), the future square kilometer array (SKA), or any other coincident observations by multiple separated receivers. The transients are short, broadband pulses of radio energy, often just a few milliseconds long, emitted by a variety of exotic astronomical phenomena. They generally represent rare, high-energy events making them of great scientific value. For RFI-robust adaptive detection of transients, using multiple stations, a family of algorithms has been developed. The technique exploits the fact that the separated stations constitute statistically independent samples of the target. This can be used to adaptively ignore RFI events for superior sensitivity. If the antenna signals are independent and identically distributed (IID), then RFI events are simply outlier data points that can be removed through robust estimation such as a trimmed or Winsorized estimator. The alternative "trimmed" estimator is considered, which excises the strongest n signals from the list of short-beamed intensities. Because local RFI is independent at each antenna, this interference is unlikely to occur at many antennas on the same step. Trimming the strongest signals provides robustness to RFI that can theoretically outperform even the detection performance of the same number of antennas at a single site. This algorithm requires sorting the signals at each time step and dispersion measure, an operation that is computationally tractable for existing array sizes. An alternative uses the various stations to form an ensemble estimate of the conditional density function (CDF) evaluated at each time step. Both methods outperform standard detection strategies on a test sequence of VLBA data, and both are efficient enough for deployment in real-time, online transient detection applications.

Thompson, David R.↗