Project Title:

Principal Investigator:

Anneliese von Mayrhauser, Professor
Department of Computer Science
Colorado State University

Collaborating Company:

IBM

Company Representative:

Pat McCourt

The aim of testing is to find errors as early and cheaply as possible. System testing should prevent release of products that would result in (expensive) discovery of post release errors. The effectiveness of a system testing group depends on many factors, not only the quality and expertise of the testers and the techniques they employ. We propose to develop an evaluation instrument that enables root cause analysis of post release problems by tracing them to one or more of the factors that influence system test efficacy. We will apply this method to existing project data. During testing, testers may need to decide whether to continue testing, switch to a different testing strategy, or recommend release. We propose to compare and evaluate quantitative techniques for determining stopping points during testing on actual test result data from industry. This can be used to evaluate the efficiency of currently used test methods. Further, we propose to customize a stopping rule for the organization that can be used for improved efficiency in future testing efforts.

Project Title:

Principal Investigators:

Lucy Pao, Assistant Professor
Department of Electrical and Computer Engineering
University of Colorado - Boulder

Collaborating Company:

Data Fusion Corporation

Company Representative:

John Thomas

Current multisensor fusion tracking systems can be easily overwhelmed by incoming data, especially as the number of targets and sensors increases. Sensor management schemes have been proposed to reduce the computational demand of these systems while minimizing the loss of tracking performance. This report presents a system that will maintain a desired covariance level for each target while reducing the resource demands on the tracking system. Other functions performed by a sensor manager like prioritizing and scheduling are assumed to be done elsewhere, but result in delays in the execution of sensing requests made by the system. Three sensor selection algorithms are presented based on different resource and performance metrics and show a dramatic improvement over 'dumb" sensing system in simulation. Since the proposed system is sensitive to delays in the execution of sensor assignments, we further analyze the effect of that delay and examine methods of eliminating that effect. Because of the lack of a closed form solution for the covariance propagation of the discrete-time Kalman filter, the analysis centers on the performance of the continuous-time scalar Kalman-Bucy filter and then extends those results to the discrete-time case. The analysis shows that for all stable systems and unstable systems under certain conditions, the sensitivity of covariance estimate to delays of sensing actions decreases steadily with time. Furthermore, when attempting to estimate unknown delays, overestimating the delay will produce smaller covariance prediction errors than underestimating the delay by a similar amount.