Bearing-only tracking with a mixture of von Mises distributions
Bearing-only tracking with a mixture of von Mises distributions
复制标题
混合冯米塞斯分布的仅方位跟踪
DOI：
10.1109/iros.2012.6385600
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
I. Petrović
中科院分区：
文献类型：
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作者：
Ivan Marković;I. Petrović
提出了一种新的贝叶斯纯方位跟踪方法。与涉及使用高斯分布的经典方法不同，跟踪过程完全覆盖冯米塞斯分布，包括状态表示、转移概率和测量模型，因为它很好地捕捉和建模了方向数据的特殊性。状态用冯·米塞斯分布的混合物表示，从而提供能够建模多峰分布、处理非线性状态转换和测量模型以及完全覆盖整个状态空间的优点，所有这些都具有适度数量的参数。跟踪过程通过与von Mises分布的卷积（预测步骤）和与表示测量模型的混合物的相乘（更新步骤）来求解。由于在更新步骤中的混合物成分的数量呈指数增长，一种方法的冯米塞斯混合物的成分减少。此外，一个封闭的形式的解决方案，推导出二次雷诺熵的冯米塞斯混合。该算法进行了测试，并在合成数据集和现实世界的录音说话人跟踪的情况下，粒子滤波表示。实验结果支持了该方法的有效性，并显示出与粒子滤波相似的性能。
This paper presents a novel method for Bayesian bearing-only tracking. Unlike the classical approaches, which involve using Gaussian distribution, the tracking procedure is completely covered with the von Mises distribution, including state representation, transitional probability, and measurement model, since it captures and models well the peculiarities of directional data. The state is represented with a mixture of von Mises distributions, thus offering advantages of being able to model multimodal distributions, handle nonlinear state transition and measurement models, and to completely cover the whole state space, all with a modest number of parameters. The tracking procedure is solved by convolution with a von Mises distribution (prediction step) and multiplication with a mixture representing the measurement model (update step). Since in the update step the number of mixture components grows exponentially, a method is presented for component reduction of a von Mises mixture. Furthermore, a closed-form solution is derived for quadratic Rényi entropy of the von Mises mixture. The algorithm is tested and compared to a particle filter representation in a speaker tracking scenario on a synthetic data set and real-world recordings. The results supported the proposed approach and showed similar performance to the particle filter.