Bayesian optimisation for sensor scheduling and tracking with different acquisition functions

Joint target tracking and sensor scheduling includes resource optimisation and gathering the most informative data for purposes such as search and rescue, fire detection and surveillance tasks. For such real-time tasks, the limited access to initial tracking data can challenge the effectiveness of traditional machine learning methods, thereby motivating the development of active sensing strategies. This paper addresses such problems and formulates the joint target tracking and sensor scheduling problems within a Bayesian optimisation framework. The key question that this framework answers is: where to position the sensors in order to accurately track an object. In the considered case study, the sensors are mobile and represented by uncrewed aerial vehicles (UAVs). The active sensing of the environment is based on uncertainty-guided sampling thanks to a Gaussian process representation.

The main novelty lies in the formulation of the sensor scheduling and tracking within a Bayesian optimisation setting. Under this framework, a detailed comparison of different acquisition functions is carried out, to identify the most suitable solutions for an active sensing problem. Results with respect to accuracy and computational time are reported.