Reliable Inference with Missing, Masking, Malfunctioning or Malicious Sensors

Sponsor: IARPA

This project is concerned with how to reliably make inferences concerning physical phenomena given practical sensor impairments. The goal is construction of a general theoretical framework that can apply to a variety of domains. The initial applications are cognitive radios, wireless health, and utility monitoring.

The focus of Prof. Pottie’s group is on the wireless health application, with the main impairments being noise and drift, improper sensor placement, and missing sensors. One research thrust is to easily go back and forth between data from visual motion capture systems and inertial sensors, since the former are costly but accurate, while the latter are portable and low cost in themselves but as noted above it is difficult to establish ground truth. If carefully modeled, it would be possible to simulate a variety of sensor positions on the body, in order to develop more robust algorithms without the need for costly experiments to acquire large quantities of ground truth. Along similar lines, the motions themselves do not usually need all the observations to be recorded in order for motion trajectories to be reconstructed. Thus motion models can also lead to data compression.

These experimental activities support the main goal of understanding how to best construct a multi-level modeling framework on Bayesian principles so that the best quality of inference can be made, given the observations available. Abstraction levels in this case include context of the motions (where, when), activities, sensor pose and placement, and motion trajectories for particular limbs. Particular algorithms are associated for making inferences at each of these levels, and also for iterating among decisions at different levels to improve the inference quality.

For more information on this project, see http://www.loris.ee.ucla.edu/rim4s