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Anchorless cooperative localization for mobile wireless sensor networks

We propose two algorithms for anchorless cooperative network localization in mobile wireless sensor networks (WSNs). In order to continuously localize the mobile network, given the pairwise distance measurements between different wireless sensor nodes, we propose to use subspace tracking to track the variations in signal eigenvectors and corresponding eigenvalues of the double-centered distance matrix. We compare the computational complexity of the proposed algorithms with a recently developed anchorless algorithm exploiting the extended Kalman filter (EKF) as well as an anchored algorithm exploiting ordinary least squares (LS). We show that our proposed algorithms are computationally efficient, and hence, are appropriate for practical implementations. Simulation results further illustrate that the proposed algorithms have an acceptable accuracy in comparison with the aforementioned algorithms and are more robust to an increasing sampling period of the measurements.