Multiple Source Localization in Wireless Sensor Networks Based on Time of Arrival Measurement

• Published in: IEEE transactions on signal processing Vol. 62; no. 8; pp. 1938 - 1949
• Main Authors: Hong Shen, Zhi Ding, Dasgupta, Soura, Chunming Zhao
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 15.04.2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Approximation; convex relaxation; Detection, estimation, filtering, equalization, prediction; Educational institutions; Exact sciences and technology; Information, signal and communications theory; Localization; Multiple source localization; Optimization; Position (location); Position measurement; Remote sensors; Sensors; Signal and communications theory; Signal processing algorithms; Signal, noise; source-measurement association; Telecommunications and information theory; Time measurement; time-of-arrival (TOA); Vectors; Wireless networks; Parameter estimation; Multiple source localization; source-measurement association; Wireless telecommunication; Measurement sensor; Algorithm; Transmission time; Optimization; Remote sensing; Arrival time; Relaxation; Algorithm performance; time-of-arrival (TOA); Source localization; Signal processing; Wireless network; Signal transmission; Sensor array; convex relaxation; Target detection; Signal detection
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2014.2304433

We investigate the localization of multiple signal sources based on sensors performing time-of-arrival (TOA) measurement in wireless sensor networks. Moving beyond the widely studied single source localization problem, concurrently active multiple sources substantially complicate the problem since anchored sensor nodes are unaware of associations between measured signals and source nodes. At the same time, as the total number of possible source-measurement associations grows exponentially with the number of sensor nodes, it is inefficient to attempt conventional single-source localization algorithm for each possible association in a brute-force manner. In this work, we address this difficult problem from a joint optimization perspective. Specifically, we consider simultaneous estimation of source-measurement associations and the source locations, in addition to finding the initial signal transmission time. This joint optimization problem includes both discrete and continuous variables. We propose an efficient three-step algorithm that progressively simplifies the original problem through convex relaxation and sensible approximations. Our proposed algorithm demonstrates results comparable to a genie-aided method that utilizes known source-measurement associations.