Robust power allocation for active and passive localization

Power resource allocation is an important task for active and passive network localization, since it affects the localization accuracy in addition to the lifetime and throughput of the network. In this paper, we propose a robust power allocation formulation that guarantees the localization accuracy in the presence of parameter uncertainty for network localization. We first consider wireless network localization (WNL) as an example of active localization, and derive sequential upper and lower bounds for the worst-case localization accuracy as well as their convergence rates. Built on these bounds, we transform the robust formulation into a sequence of second-order cone programs (SOCPs) that yield asymptotically optimal solutions. We also develop an efficient near-optimal SOCP-based algorithm using a relaxation method. Then, we extend all the results to passive localization through the example of radar network localization (RNL). Finally, the simulation results validate the efficiency and robustness of the proposed algorithms.