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.

翻译：本研究考虑目标定位问题，提出了一种新颖的多粒子卡尔曼滤波器，用于在复杂且对称的环境中解决该问题。解决定位问题的两类经典滤波算法是基于卡尔曼滤波器的方法和基于粒子滤波器的方法。我们分析了这两类算法，展示了其互补特性，并提出了一种融合两类算法优势的新型滤波算法。我们在对称且含噪声的环境中评估了多粒子卡尔曼滤波器。这类环境对两类经典方法均构成特别挑战。我们将所提方法与粒子滤波器进行了比较，因为当初始状态未知时，唯有粒子滤波器可行。在所考虑的具有挑战性的环境中，我们的方法在定位误差和运行时间方面均优于粒子滤波器。