Localizing mobile RF targets using multiple unmanned aerial vehicles with heterogeneous sensing capabilities

In this paper, we consider the problem of locating a mobile radio frequency (RF) target using multiple unmanned aerial vehicles (UAVs) equipped with sensors with varying accuracies. We investigate the localization task performance as we vary (1) the configuration of multiple UAVs (sensor locations), (2) the type of sensors onboard the UAVs, and (3) the sensor sequence. We use the well known optimal recursive estimation techniques (Kalman filtering) to combine captured sensor values from multiple UAVs and to investigate sensor scheduling issues to minimize the target location error. We present our findings in the form of simulation results.