Source Localization and Tracking of a Wideband Source Using a Randomly Distributed Beamforming Sensor Array

We consider the array signal processing aspect of a power efficient self-organized and synchronized wireless sensor network for source detection, signal enhancement, localization, and identification. The beamforming and source localization are the most computationally intensive operations in the network. In this paper, we introduce a class of computationally efficient source localization algorithms. The novel source localization estimators also include the speed of propagation estimation since it is often unknown. For a more robust solution, a parametric source tracking algorithm is developed based on a linear track assumption. We also derive the Cramér-Rao bound for source localization and speed of propagation estimations using a randomly distributed sensor array. A blind beamforming approach, which enhances the strongest signal while attenuating other interferences, using only the measured data is presented. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector of the sample correlation matrix. In many practical scenarios, the locations of some of the sensors may be unknown or non-stationary. From three or more source locations and source distances, we formulate a least-squares estimator for the unknown sensor location. Systematic evaluations via simulations show the proposed algorithms are effective and efficient with respect to the Cramér-Rao bound. The proposed algorithms are also shown to be effective in the examples using measured data.