Learning attributes for situational awareness in the landing of an autonomous airplane

The paper investigates situational learning, which utilizes mathematics of probability and evidential theory, to determine the perceivable importance of environmental cues as they contribute to situational awareness. The situation-awareness agent´s goal is consistent with that of an aircraft pilot; namely, to land a plane under a variety of weather and runway conditions. Landing requires hypothesis selection which can be formulated as a situational-learning (SL) problem in which sensed states are represented as current situational beliefs. The objective of SL is to learn how to select the optimal set of mutually non-exclusive hypothesis in order to maximize the identification of the situation. Three methodologies for the combination of sensor measurements for situational learning are designed and analyzed for a system equipped with a position measuring device and identification sensors. Using a learning algorithm for searching, the a priori identification probabilities of recognition are known. The methods are: (1) recursive Bayesian where the probability of the current state is based on the a priori information multiplied by the likelihood function, (2) Dempster-Shafer(DS) which uses evidential reasoning/accrual to combine information of uncertainty, and (3) modified Dempster-Shafer(MDS) which uses a combination of evidential reasoning and probability analysis. The methods are assessed for cases with and without feedback