Multiparticle Kalman filter for object localization in symmetric environments
This addresses localization challenges in noisy symmetric settings, but is incremental as it combines existing filter classes.
The study tackled object localization in complex symmetric environments by proposing a multiparticle Kalman filter, which outperformed particle filters with lower localization error and faster runtime.
This study considers the object localization problem and proposes a novel multiparticle Kalman filter to solve it in complex and symmetric environments. Two well-known classes of filtering algorithms to solve the localization problem are Kalman filter-based methods and particle filter-based methods. We consider these classes, demonstrate their complementary properties, and propose a novel filtering algorithm that takes the best from two classes. We evaluate the multiparticle Kalman filter in symmetric and noisy environments. Such environments are especially challenging for both classes of classical methods. We compare the proposed approach with the particle filter since only this method is feasible if the initial state is unknown. In the considered challenging environments, our method outperforms the particle filter in terms of both localization error and runtime.