Sensor management using a new framework for observation modeling

In previous work, a sensor management framework has been developed that manages a suite of sensors in a search for static targets within a grid of cells. This framework has been studied for binary, non-binary, and correlated sensor observations, and the sensor manager was found to outperform a direct search technique with each of these different types of observations. Uncertainty modeling for both binary and non-binary observations has also been studied. In this paper, a new observation model is introduced that is motivated by the physics of static target detection problems such as landmine detection and unexploded ordnance (UXO) discrimination. The new observation model naturally accommodates correlated sensor observations and models both the correlation that occurs between observations made by different sensors and the correlation that occurs between observations made by the same sensor. Uncertainty modeling is also implicitly incorporated into the observation model because the underlying parameters of the target and clutter cells are allowed to vary and are not assumed to be constant across target cells and across clutter cells. Sensor management is then performed by maximizing the expected information gain that is made with each new sensor observation. The performance of the sensor manager is examined through performance evaluation with real data from the UXO discrimination application. It is demonstrated that the sensor manager is able to provide comparable detection performance to a direct search strategy using fewer sensor observations than direct search. It is also demonstrated that the sensor manager is able to ignore features that are uninformative to the discrimination problem. © 2009 SPIE.