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, 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 new algorithms with a recently developed algorithm exploiting the extended Kalman filter (EKF) and show that our proposed algorithms are computationally efficient, and hence, appropriate for practical implementations compared to the EKF. Simulation results further illustrate that the proposed algorithms are more accurate when the distance errors are small (low noise scenarios) in comparison with the EKF, while being more robust to the sampling period in high noise scenarios.