  The paper presents a receding horizon planning and control strategy for
quadrotor-type \ac{mav}s to navigate reactively and intercept a moving target
in a cluttered unknown and dynamic environment. Leveraging a lightweight
short-range sensor that generates a point-cloud within a relatively narrow and
short \ac{fov}, and an \acs{ssd}-MobileNet based Deep neural network running on
board the \ac{mav}, the proposed motion planning and control strategy produces
safe and dynamically feasible \ac{mav} trajectories within the sensor
\acs{fov}, which the vehicle uses to autonomously navigate, pursue, and
intercept its moving target. This task is completed without reliance on a
global planner or prior information about the environment or the moving target.
The effectiveness of the reported planner is demonstrated numerically and
experimentally in cluttered indoor and outdoor environments featuring maximum
speeds of up to 4.5-5~m/s.
