Source Localization with Spatially Distributed Active and Passive Sensors

We consider solving the source localization problem by exploiting measurements collected from both active and passive sensors. We first briefly review some existing least squares approaches that utilize only one type of measurement (active or passive), and further establish a hybrid objective function that includes active and passive measurements simultaneously in the sense of squared least squares. We propose two different methods, Newton's method and the semidefinite relaxation method, to efficiently solve the optimization problem. Simulation results indicate that the source location estimates given by the proposed hybrid methods are superior to the peer methods that only utilize one type of measurement. The performance difference between Newton's method and the semidefinite relaxation method is also investigated.