Computationally Efficient Maximum Likelihood Direct Self-Localization

The direct localization approach has attracted significant interest owing to its superior performance over two-step localization methods. This paper investigates the array direct self-localization problem in scenarios where all anchors transmit signals at the same frequency. The existing method relies on an exhaustive search to obtain the global optimal solution, rendering it time-intensive. Moreover, exhaustive search is associated with grid quantization errors (GQEs), which reduces localization performance. This paper proposes a numerical solution based on majorization-minimization (MM) for the maximum likelihood (ML) estimation problem to address this issue. The proposed method has a guaranteed convergence property, with all limit points converging to a stationary point. Simulation results reveal that the proposed algorithm can reduce complexity and eliminate GQEs.