Recursive nonlinear filtering for angular data based on circular distributions
Recursive nonlinear filtering for angular data based on circular distributions
复制标题
基于圆形分布的角度数据递归非线性滤波
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
U. Hanebeck
中科院分区：
文献类型：
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作者：
G. Kurz;Igor Gilitschenski;U. Hanebeck
在许多跟踪和控制应用中，循环量估计是一个普遍存在的问题。常用的卡尔曼滤波、扩展卡尔曼滤波(EKF)和无迹卡尔曼滤波(UKF)等方法没有明确考虑周期性，从而导致估计精度较低。提出了一种基于循环统计量的非线性系统角量滤波算法。新的过滤器在圆周上概率分布的三种不同表示之间切换，包裹法线、冯·米塞斯和狄拉克混合密度。它可以被视为UKF对循环统计的系统概括。我们在仿真中对所提出的滤波器进行了评估，并显示了其相对于传统方法的优越性。
Estimation of circular quantities is a widespread problem that occurs in many tracking and control applications. Commonly used approaches such as the Kalman filter, the extended Kalman filter (EKF), and the unscented Kalman filter (UKF) do not take periodicity explicitly into account, which can result in low estimation accuracy. We present a filtering algorithm for angular quantities in nonlinear systems that is based on circular statistics. The new filter switches between three different representations of probability distributions on the circle, the wrapped normal, the von Mises, and a Dirac mixture density. It can be seen as a systematic generalization of the UKF to circular statistics. We evaluate the proposed filter in simulations and show its superiority to conventional approaches.