Abstract
The localization of multiple signal sources based on Time Of Arrival (TOA) measurements in wireless sensor networks is investigated in this paper. When the signal sources cannot be distinguished by their signatures or other unique characteristics, the correspondence between the sources and the TOA measurements at different sensors is unknown, which makes the multi-source localization problem quite challenging. A self-clustering measurement combination method is proposed for the problem. The source location estimate obtained by the hyperbolic localization algorithm is used as the clustering pattern, and the scatter of the patterns of different subsets of TOA measurements is defined as a criterion function, which is extremized by the combination of TOA measurements from the same source. A three-step heuristic clustering algorithm is pursued to resolve the TOA ambiguity, and its mean square error performance and computational complexity are also analyzed. The simulation and experiment indicate that the presented method has higher location accuracy and lower complexity compared with the existing methods.
Highlights
Wireless Sensor Networks (WSNs) consist of a number of nodes equipped with one or multiple spatially distributed sensors [1], [2]
Guo et al.: Multi-Source Localization Using Time Of Arrival (TOA) Self-Clustering Method. These existing Time Difference Of Arrival (TDOA)-based methods mainly focus on the single source localization, or assume that the signals from different sources are separable in time, frequency or both for multi-source localization [23]–[26]
Note that we focus on a propagation environment in which either a line-of-sight (LOS) path exists or scatterers are near the sources or the sensors to provide a near LOS path
Summary
Wireless Sensor Networks (WSNs) consist of a number of nodes equipped with one or multiple spatially distributed sensors [1], [2]. Each node in the TDOA-based methods only needs one sensor to receive the source signal, and the equipment cost and the installation complexity are lower. The location accuracy is not directly related to the baseline distance between sensors [29] Considering these advantages of TDOA, the TDOA-based localization methods are discussed in this paper. These existing TDOA-based methods mainly focus on the single source localization, or assume that the signals from different sources are separable in time, frequency or both for multi-source localization [23]–[26]. It does not directly deal with the association problem and the resulting location estimates are not accurate. For the ease of reading, some proofs are included in the Appendix
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