On the semidefinite programming algorithm for energy-based acoustic source localization in sensor networks

The received energy has been becoming an efficient and attractive measure for acoustic source localization due to its cost saving in both energy and computation capability. We investigated the acoustic source localization problem based on received energy measurements in sensor networks. Focusing on the non-logarithmic energy attenuation model, we developed and compared a suite of semidefinite programming (SDP)-based source localization methods due to computational efficiency and numerical reliability. First, we proposed a general SDP-based estimator by jointly estimating the source location and the source radiation power. It yields an efficient estimate for both the scenario where the source is located inside the convex hull formed by sensors and the scenario where the source is located outside the convex hull. Next, a min-max approximation is given to cope with the applicable application of the existing energy-based source localization algorithms relying on the Gaussian energy noise assumption. Furthermore, a novel norm approximation method is proposed according to norm equivalence, which can provide a comparable performance with lower computational complexity. Simulations show that our proposed methods exhibit a superior performance than the existing energy-based source localization estimators.