Improving classification performance through kinematic decisions

We analyze the relationship between classification performance (i.e., the mutual information and the probability of misclassification) and sensor abilities. The analysis suggests an effective region of sensor space that can improve the classification performance when multiple measurements are to be taken sequentially, and possible sensor allocation strategies are discussed. Based on the analysis, we apply the sensing strategies to a UAV path planning problem where the sensor performance depends on the relative position (i.e., range and azimuth) of the UAV with respect to the object of interest. Specifically, we use two sliding mode controllers, each of which accounts for a particular sensing strategy, with a hybrid-system switching scheme. We validate our approach with numerical simulation results.