A Bayesian network for autonomous sensor control during polar ice sheet measurements

摘要

The PRISM (Polar Radar for Ice Sheet Measurements) project at the University of Kansas is developing intelligent radar sensors for the measurement and study of the mass balance of the polar ice sheets. An important component of PRISM is intelligent, autonomous synthetic aperture radar that can reason about its operating mode (monostatic vs. bistatic) and frequency, based on a variety of environmental and sensor-related factors. The PRISM sensors are placed on autonomous robotic vehicles ("rovers") that use the sensor and environmental information to decide about what paths to traverse, how to traverse them, and at what speeds. In our work, we have implemented the reasoning component of the autonomous radar and the rovers, using intelligent agents and Bayesian networks. This implementation is the first ever of a dynamically modifying adaptive radar and mobile data collection system based on autonomous rovers for accurate polar ice sheet measurements.