In a simplistic sensor fusion environment, each sensor generates data, together with an uncertainty estimate of that data. Then, when sensor fusion is to occur, the uncertainties on each sensor are used to weight each sensor measurement according to well-known estimation algorithms such as the Kalman filter. The purpose of this topic is to explore (individually or jointly) four main difficulties associated with this general framework. First, in many cases the uncertainty due to a certain set of measurements may not be known a-priori. This may be due to the expense of calibration procedures, or due to the dependence of the uncertainty on operatings conditions that cannot all be specified a-priori. Therefore, estimation algorithms that can perform accurate data fusion, even without accurate a-priori measures of uncertainty, are of interest to this topic. Second, once a model for uncertainty is known, outliers (rare events that do not fit the uncertainty model) may still be present in the data. Ensuring the estimation algorithm is robust to outliers, while still generating accurate estimates of uncertainty for its a-posterior estimates are another area of interest. Third, models for uncertainty, especially when the uncertainty has been estimated from samples (a possible approach to solving the first problem), can be very complex and may not even be have a closed-form representation. Performing Bayesian fusion on these general (e.g., non-parametric) representations of uncertainty then becomes even more difficult. Advances in implementing fusion of different, sampled distributions are therefore desired to enable more complex estimation routines. Fourth, performing estimation is a fully distributed system, where there is limited communication between sensors/agents, but better estimation of system state can be achieved using data from all agents, requires new algorithms and approaches than outlined in the "simplistic" scenario above. Any of these four, estimation theory based topics are of significant interest to this topic.

 

key words

Estimation theory; Outlier rejection; Distributed data fusion; Sampling theory; Bayesian estimation; Uncertainty quantification;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral and Senior applicants