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Two novel cooperative localization algorithms for mobile wireless networks are proposed. To continuously localize the mobile network, given the pairwise distance measurements between different wireless sensor nodes, the authors propose to use subspace tracking to track the variations in signal eigenvectors and corresponding eigen-values of the double-centered distance matrix. They compare the computational complexity of the new algorithms with a recently developed algorithm exploiting the Extended Kalman Filter (EKF) and show that their proposed algorithms are computationally efficient, and hence, appropriate for practical implementations compared to the EKF.
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