Technical Program

Paper Detail

Paper:	SS-6.3
Session:	Task-driven sensing and distributed processing
Time:	Wednesday, May 17, 17:10 - 17:30
Presentation:	Special Session Lecture
Topic:	Special Sessions: Task-driven sensing and distributed processing
Title:	Maximum A Posteriori (MAP)-based Algorithm for Distributed Source Localization using Quantized Acoustic Sensor Readings
Authors:	Yoon Hak Kim, Antonio Ortega, University of Southern California, United States
Abstract:	In this paper, we propose a distributed source localization algorithm based on the Maximum A Posteriori (MAP) criterion, where the observations generated by each of the distributed sensors are quantized before being transmitted to a fusion node for localization. If the source signal energy is known, each quantized sensor reading corresponds to a region in which the source can be located. Aggregating the information obtained from multiple sensors corresponds to generating intersections between the regions. In our previous work we developed quantizer design techniques aimed at optimizing localization accuracy for a given aggregate rate. In this paper we develop localization algorithms based on estimating the likelihood of each of the intersection regions. This likelihood can incorporate uncertainty about the source signal energy as well as measurement noise. We show that the computational complexity of the algorithm can be significantly reduced by taking into account the correlation of the received quantized data. We also propose a technique, based on a weighted average of estimators, to address the case when the signal energy is unknown. Our simulation results show that our localization algorithm achieves good performance with reasonable complexity as compared with Minimum Mean Square Error (MMSE) estimation.