Improving Localization Success Rate of Three Magnetic Targets Using Individual Memory-Based WO-LM Algorithm

Magnetic localization technique has the advantages of millimeter-level localization accuracy, fast localization speed, and no limit-of-sight constraints. However, most studies are limited to the tracking of single or two magnets because of the low localization success rate for multiple magnets, thereby restricting its application scenarios. In this study, a novel multi-magnetic target localization method, named individual memory-based whale optimization and Levenberg-Marquardt (IMWO-LM) algorithm, is proposed to overcome the problem of low localization success rate. The IMWO algorithm adds the individual historical optimal location memory to each whale, which improves the global search ability of the population. This IMWO algorithm result is employed as the initial guess of the LM method. Afterward, the LM result is adopted as the initial guess of the LM method in the subsequent localization process. In 20 groups of 500 localizations, the average localization success rates of the particle swarm optimization-LM algorithm, whale optimization-LM algorithm, and the proposed IMWO-LM algorithm were 85.1%, 83.82%, and 98.28%, respectively. In the experiment of locating three magnetic targets simultaneously, the average position error and the average orientation error were 3.46 mm and 6.77°, respectively. Results show that this method can significantly improve the localization success rate while maintaining high localization accuracy.