Christopher M. Gifford

Advanced Multifrequency Radar Instrumentation for Polar Research

This paper presents a radar sensor package specifically developed for wide-coverage sounding and imaging of polar ice sheets from a variety of aircraft. Our instruments address the need for a reliable remote sensing solution well-suited for extensive surveys at low and high altitudes and capable of making measurements with fine spatial and temporal resolution. The sensor package that we are presenting consists of four primary instruments and ancillary systems with all the associated antennas integrated into the aircraft to maintain aerodynamic performance. The instruments operate simultaneously over different frequency bands within the 160 MHz-18 GHz range. The sensor package has allowed us to sound the most challenging areas of the polar ice sheets, ice sheet margins, and outlet glaciers; to map near-surface internal layers with fine resolution; and to detect the snow-air and snow-ice interfaces of snow cover over sea ice to generate estimates of snow thickness. In this paper, we provide a succinct description of each radar and associated antenna structures and present sample results to document their performance. We also give a brief overview of our field measurement programs and demonstrate the unique capability of the sensor package to perform multifrequency coincidental measurements from a single airborne platform. Finally, we illustrate the relevance of using multispectral radar data as a tool to characterize the entire ice column and to reveal important subglacial features.

Fuzzy association rule mining for community crime pattern discovery

Current manual inspection of crime data by analysts is limited, primarily due to the amount of data that can be processed concurrently and in a reasonable time frame. Further, complex relationships between various crime attributes can be overlooked by human analysts. Providing automated knowledge discovery tools becomes attractive to accelerate the efforts of local law enforcement. In this paper, we study the application of fuzzy association rule mining for community crime pattern discovery. Discovered rules are presented and discussed at regional and national levels. Rules found to hold in all states, be consistent across all regions, and subsets of regions are also discussed. A relative support metric was defined to extract rare, novel rules from thousands of discovered rules. Such an approach relieves the need of law enforcement personnel to sift through uninteresting, obvious rules in order to find interesting and meaningful crime patterns of importance to their community.

Low-cost multi-robot exploration and mapping

Mobile robots can perform some of the more dangerous and laborious human tasks on Earth and throughout the solar system, many times with greater efficiency and accuracy, saving both time and resources. As we explore further away from Earth, higher levels of autonomy are also becoming more desired in such applications, one of them being distributed mapping. Smaller, less expensive mobile robots are becoming more prevalent, which introduces unique challenges in terms of limited sensing accuracy and onboard computing resources. This paper presents a low-cost approach to autonomous multi-robot mapping and exploration for unstructured environments. Platform design and implementation details are discussed, along with results from a planetary style environment. Results demonstrate that mobile robots capable of SLAM can be constructed for less than

Automated Polar Ice Thickness Estimation From Radar Imagery

1250, and similar concepts could be used for planetary missions.

Collaborative multi-agent rock facies classification from wireline well log data

This paper focuses on automating the task of estimating Polar ice thickness from airborne radar data acquired over Greenland and Antarctica. This process involves the identification and accurate selection of the ice sheets surface location and interface between the ice sheet and the underlying bedrock for each measurement. Identifying the surface and bedrock locations in the radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume, and how they may contribute to global climate change. The time-consuming manual approach requires sparse hand-selection of surface and bedrock interfaces by several human experts, and interpolating between the selections to save time. Two primary methods have been studied in this paper, namely, edge-based and active contour. Results are evaluated and presented using the metrics of time requirements and accuracy. Automated ice thickness estimation results from 2006 and 2007 Greenland field campaigns illustrate that the edge-based approach offers faster processing (seconds compared to minutes), but suffers from a lack of continuity and smoothness aspects that active contours provide. The active contour approach is more accurate when compared to ground truth selections provided by human experts, and has proven to be more robust to image artifacts. It is shown that both techniques offer advantages which could be integrated to yield a more effective system.

Mobile Robots for Polar Remote Sensing

Gas and oil reservoirs have been the focus of modeling efforts for decades as an attempt to locate zones with high volumes. Certain subsurface layers and layer sequences, such as those containing shale, are known to be impermeable to gas and/or liquid. Oil and natural gas then become trapped by these layers, making it possible to drill wells to reach the supply, and extract for use. The drilling of these wells, however, is costly. In this paper, we utilize multi-agent machine learning and classifier combination to learn rock facies sequences from wireline well log data. The paper focuses on how to construct a successful set of classifiers, which periodically collaborate, to increase the classification accuracy. Utilizing multiple, heterogeneous collaborative learning agents is shown to be successful for this classification problem. Utilizing the Multi-Agent Collaborative Learning Architecture, 84.5% absolute accuracy was obtained, an improvement of about 6.5% over the best results achieved by the Kansas Geological Survey with the same data set. A number of heuristics are presented for constructing teams of multiple collaborative classifiers for predicting rock facies.

Development of a multi-frequency airborne radar instrumentation package for ice sheet mapping and imaging

Mobile robots are becoming more heavily used in environments where human involvement is limited, impossible, or dangerous. These robots perform some of the more dangerous and laborious human tasks on Earth and throughout the solar system, many times with greater efficiency and accuracy, saving both time and resources. As we explore further away from Earth, higher levels of autonomy are also becoming more desired in such applications, one of them being remote sensing. This chapter covers mobile robots that have been designed and built at the University of Kansas to facilitate seismic and radar remote sensing of ice sheets in polar regions. These robots have been developed for and deployed in unstructured, polar environments. System designs, components, deployment and data acquisition algorithms, and experimental results are discussed. In this chapter, future applications, such as an autonomous multi-robot seismic surveying surveying team, are simulated. Future planetary missions will hopefully incorporate similar robotic systems to conduct insitu experiments on other planets. Christopher M. Gifford Electrical Engineering and Computer Science Department, University of Kansas, Lawrence, KS 66045, e-mail: cgifford@eecs.ku.edu Eric L. Akers Mathematics and Computer Science Department, Elizabeth City State University, Elizabeth City, NC 27909, e-mail: elakers@mail.ecsu.edu Richard S. Stansbury Department of Computer and Software Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, e-mail: richard.stansbury@erau.edu Arvin Agah Electrical Engineering and Computer Science Department, University of Kansas, Lawrence, KS 66045, e-mail: agah@ku.edu

Precise Formation of Multi-Robot Systems

We have developed improved versions of three different radar systems and integrated them as an airborne instrumentation suite for sounding and imaging Polar ice sheets. The first instrument consists of a multi-channel, coherent, pulsed radar operating at VHF with up to 20 MHz bandwidth. This instrument is capable of sounding a few-kilometer thick ice while flying at altitudes up to 10 km above mean sea level. The second instrument is designed to operate at UHF using a burst of narrow-bandwidth signals to digitally synthesize a bandwidth of in excess of 300 MHz. This apparatus is used to measure internal layers of the ice sheet to a depth close to 100 m. The third component to the instrumentation package is based on a frequency-modulated continuous wave (FMCW) radar, which operates at microwave (Ku band) frequencies with up to 1 GHz of instantaneous bandwidth. This radar set is used to measure the ice sheet surface elevation profile with centimeter accuracy. We are presenting a description of each system, with emphasis on the VHF depth sounder. We also present sample field test results obtained during the 2009 austral summer season in Antarctica, as a validation of the performance of the instrument package.

Seismic TETwalker mobile robot design and modeling

There are several applications which require high-precision placement of sensors or objects. Unstructured, remote environments make this problem even more complex, where high precision is not attainable using conventional sensors. Some applications have units so sparsely spaced that few options remain. A team of mobile robots can be integrated into such missions, providing a higher level of precision while also removing the human footprint. This paper presents a high-precision, shape-based grid formation scheme for multi-robot systems that can be used in dense or sparse applications. Precision, time, and energy usage are analyzed, and collision risk is briefly discussed.

Subglacial water presence classification from polar radar data

The Center of Remote Sensing of Ice Sheets (CReSIS) is studying the use of the TETwalker mobile robot developed by NASA/Goddard Space Flight Center for polar seismic data acquisition. This paper discusses the design process for deploying seismic sensors within the 4-TETwalker mobile robot architecture. The 4-TETwalkerpsilas center payload node was chosen as the deployment medium. An alternative method of deploying seismic sensors that rest on the surface is included. Detailed models were also developed to study robot mobility dynamics and the deployment process. Finally, potential power options of solar sheaths and harvesting vibration energy are proposed.