To efficiently navigate their surrounding environments and complete missions, unmanned aerial systems (UASs) should be able to detect multiple objects in their surroundings and track their movements over time. So far, however, enabling multi-object tracking in unmanned aerial vehicles has proved to be fairly challenging.

Researchers at Lockheed Martin AI Center have recently developed a new deep learning technique that could allow UASs to track multiple objects in their surroundings. Their technique, presented in a paper pre-published on arXiv, could aid the development of better performing and more responsive autonomous flying systems.

“We present a robust object tracking architecture aimed to accommodate for the noise in real-time situations,” the researchers wrote in their paper. “We propose a kinematic prediction model, called deep extended Kalman filter (DeepEKF), in which a sequence-to-sequence architecture is used to predict entity trajectories in latent space.”

Exoplanet hunters have found thousands of planets, most orbiting close to their host stars, but relatively few alien worlds have been detected that float freely through the galaxy as so-called rogue planets, not bound to any star. Many astronomers believe that these planets are more common than we know, but that our planet-finding techniques haven’t been up to the task of locating them.

Most exoplanets discovered to date were found because they produce slight dips in the observed light of their host stars as they pass across the star’s disk from our viewpoint. These events are called transits.

NASA.