  Target localization is a critical task for mobile sensors and has many
applications. However, generating informative trajectories for these sensors is
a challenging research problem. A common method uses information maps that
estimate the value of taking measurements from any point in the sensor state
space. These information maps are used to generate trajectories; for example, a
trajectory might be designed so its distribution of measurements matches the
distribution of the information map. Regardless of the trajectory generation
method, generating information maps as new observations are made is critical.
However, it can be challenging to compute these maps in real-time. We propose
using convolutional neural networks to generate information maps from a target
estimate and sensor model in real-time. Simulations show that maps are
accurately rendered while offering orders of magnitude reduction in computation
time.
