Algorithm for searching and tracking an unknown and varying number of mobile targets using a limited FoV sensor

We study the problem of searching and tracking a collection of moving targets using a robot with a limited Field-of-View (FoV) sensor. The actual number of targets present in the environment is not known a priori. We propose a search and tracking framework based on the concept of Bayesian Random Finite Sets (RFSs). Specifically, we generalize the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter which was previously applied for only tracking problems to allow for simultaneous search and tracking. The proposed framework can extract individual target tracks as well as estimate the number and spatial density of the targets. We also show how to use Gaussian Process (GP) regression to extract and predict nonlinear target trajectories in this framework. We demonstrate the efficacy of our techniques through representative simulations where we also compare the performance of two active control strategies.