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    • 17. 发明申请
    • ENHANCED POSITIONING SYSTEM USING HYBRID FILTER BACKGROUND
    • 使用混合过滤器的增强定位系统
    • US20160154083A1
    • 2016-06-02
    • US14752520
    • 2015-06-26
    • Intel Corporation
    • Yuval AmizurUri Schatzberg
    • G01S5/02H04W4/02
    • G01S5/0278H04W4/023H04W4/029
    • The disclosure generally relates to an enhanced positioning system and method using a combination or hybrid filter. In one embodiment, Time-Of-Flight (ToF) measurements are used to determine an approximate location for a mobile device in relationship to one or more Access Points. The ToF combined with known and unknown variables are then processed through a hybrid filter system to determine location of the mobile device. The hybrid filter system may include a Kalman Filter (KF) for processing linear models and generally Gaussian noise distribution. The KF assumes that the state probability of mobile device location is Gaussian. Such variables include, for example, WiFi ToF bias. The hybrid filter system may include a Bayesian Filter (BF) for processing variables having non-Gaussian noise distribution and non-linear models. Such variables include, for example, the coordinates of the mobile device. A probability determination from each of the KF and BF is then applied to estimate the mobile device location.
    • 本公开通常涉及使用组合或混合式滤波器的增强型定位系统和方法。 在一个实施例中,使用飞行时间(ToF)测量来确定与一个或多个接入点相关的移动设备的大致位置。 然后通过混合滤波器系统处理与已知和未知变量组合的ToF,以确定移动设备的位置。 混合滤波器系统可以包括用于处理线性模型和通常高斯噪声分布​​的卡尔曼滤波器(KF)。 KF假设移动设备位置的状态概率为高斯。 这样的变量包括例如WiFi ToF偏差。 混合滤波器系统可以包括用于处理具有非高斯噪声分布​​的变量的贝叶斯滤波器(BF)和非线性模型。 这样的变量包括例如移动设备的坐标。 然后应用来自KF和BF中的每一个的概率确定来估计移动设备位置。