  We study the problem of searching for 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 tracking
problems to allow for simultaneous search and tracking with a limited FOV
sensor. The proposed framework can extract individual target tracks as well as
estimate the number and the spatial density of targets. We also show how to use
the Gaussian Process (GP) regression to extract and predict non-linear target
trajectories in this framework. We demonstrate the efficacy of our techniques
through representative simulations and a real data collected from an aerial
robot.
