Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks

By Xiaohong Sheng, Y. Hu. IEEE Transactions on Signal Processing, 2005

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Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference (2011, background): “The near-field case is typically studied in the framework of sensor networks where the aim is to estimate the coordinates of the source(s) [2–4].”
Materials that couple sensing, actuation, computation, and communication (2015, methodology): “This example, using embedded MEMS microphones, lends itself to many related material-centric applications such as sound localization (13, 35), vibration analysis (36, 37), or—when combined with piezo actuators—structural health monitoring (9, 38–40).”
Information fusion for wireless sensor networks: Methods, models, and classifications (2007, methodology): “In this context, the method is often used to obtain accurate distance (or direction, angle) estimations that are used to compute the location of nodes [Patwari et al. 2003; Fang et al. 2005] or sources (targets) [Sheng and Hu 2005; Niu and Varshney 2006; Li et al. 2006; Chen et al.”
Energy-based sensor network source localization via projection onto convex sets (2005, methodology): “The MLE was found by performing a grid search over the field area followed by a local search initiated at the highest maximum.”
Gossip Algorithms for Distributed Signal Processing (2010, background): “Centralized maximum likelihood estimators for single and multiple-source scenarios based on this model are presented in [109] and [110].”