Detection and localization for an unknown emitter using TDOA measurements and sparsity of received signals in a synchronized wireless sensor network

We consider the problem of detection and localization for an unknown emitter in a synchronized wireless sensor network. The detection is investigated by the classical binary Bayesian hypothesis testing using TDOA measurements and the localization is tackled in the framework of support detection with the joint TDOA information and sparsity of the receiving signal. We derive the decision rule with TDOA measurements by multiple synchronized sensor nodes and simultaneously the decision threshold under the given constant false alarm (CFAR) probability according to the Neyman-Pearson principle. In the framework of support detection, we propose that the TDOA-based localization and detection could be performed in the manner similar to radar by using range cells. Simulation results show that both the proposed methods are effective in the detection and localization of an unknown emitter.