Using Neural Networks to Generate Information Maps for Mobile Sensors

TL;DR

This paper introduces a neural network-based approach to generate information maps for mobile sensors, enabling real-time computation and improved target localization efficiency.

Contribution

The authors develop a CNN method to produce information maps quickly from target estimates, significantly reducing computation time compared to traditional methods.

Findings

Maps are accurately generated in real-time

Method offers orders of magnitude faster computation

Improves target localization efficiency

Abstract

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.