Channel aware target localization with quantized data in wireless sensor networks
IEEE Transactions on Signal Processing
Tracking in wireless sensor networks using particle filtering: physical layer considerations
IEEE Transactions on Signal Processing
Efficient weighted multidimensional scaling for wireless sensor network localization
IEEE Transactions on Signal Processing
Robust maximum likelihood acoustic source localization in wireless sensor networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
IEEE Transactions on Signal Processing
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In this paper, energy-based localization methods for source localization in sensor networks are examined. The focus is on least-squares-based approaches due to a good tradeoff between performance and complexity. A suite of methods are developed and compared. First, two previously proposed methods (quadratic elimination and one step) are shown to yield the same location estimate for a source. Next, it is shown that, as the errors which perturb the least-squares equations are nonidentically distributed, it is more appropriate to consider weighted least-squares methods, which are observed to yield significant performance gains over the unweighted methods. Finally, a new weighted direct least-squares formulation is presented and shown to outperform the previous methods with much less computational complexity. Unlike the quadratic elimination method, the weighted direct least-squares method is amenable to a correction technique which incorporates the dependence of unknown parameters leading to further performance gains. For a sufficiently large number of samples, simulations show that the weighted direct solution with correction (WDC) can be more accurate with significantly less computational complexity than the maximum-likelihood estimator and approaches Cramer-Rao bound (CRB). Furthermore, it is shown that WDC attains CRB for the case of a white source.