Low-Complexity Parameter Estimation Algorithm Based on Two-Stage QPSO-OMP

Single access point (AP) localization techniques are commonly used due to their cost-effectiveness and the absence of time synchronization requirements across multiple APs. However, these techniques involve the estimation of multiple parameters, which can lead to challenges such as increased time consumption and storage requirements. To overcome these challenges, this paper presents a two-stage quantum particle swarm optimization-orthogonal matching search (QPSO-OMP) algorithm. In the first stage, the OMP algorithm is employed to obtain a rough parameter range and effectively narrow down the search space. This helps reduce the computational burden and storage pressure on the device. In the second stage, the QPSO algorithm is utilized to perform accurate parameter estimation, further improving the efficiency of the localization process. Through simulation experiments, it has been demonstrated that the proposed algorithm offers a significant reduction in time complexity while maintaining a comparable level of localization effectiveness.