  In this paper, we study a joint detection, mapping and navigation problem for
a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and
flying in an unknown environment. The goal is to optimize its trajectory with
the purpose of maximizing the mapping accuracy and, at the same time, to avoid
areas where measurements might not be sufficiently informative from the
perspective of a target detection. This problem is formulated as a Markov
decision process (MDP) where the UAV is an agent that runs either a state
estimator for target detection and for environment mapping, and a reinforcement
learning (RL) algorithm to infer its own policy of navigation (i.e., the
control law). Numerical results show the feasibility of the proposed idea,
highlighting the UAV's capability of autonomously exploring areas with high
probability of target detection while reconstructing the surrounding
environment.
