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    ROBUST TOA-ESTIMATION USING CONVOLUTIONAL NEURAL NETWORKS (OR OTHER FUNCTION APPROXIMATIONS) ON RANDOMIZED CHANNEL MODELS
    71.
    发明公开
    ROBUST TOA-ESTIMATION USING CONVOLUTIONAL NEURAL NETWORKS (OR OTHER FUNCTION APPROXIMATIONS) ON RANDOMIZED CHANNEL MODELS 审中-实审

    公开(公告)号：EP4350573A3

    公开(公告)日：2024-06-12

    申请号：EP24158029.9

    申请日：2021-12-06

    申请人： Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.

    发明人： FEIGL, Tobias ,  EBERLEIN, Ernst ,  MUTSCHLER, Christopher ,  KRAM, Sebastian

    IPC分类号： G01S5/02 ,  G06N3/082 ,  G06N3/084

    CPC分类号： G01S5/0273 ,  G01S5/0278 ,  G06N3/084 ,  G06N3/082 ,  G06N3/045

    摘要： There is disclosed a method (300) for inferring a predetermined time of arrival (261) of a predetermined transmitted signal on the basis of channel impulse responses, CIRs, of transmitted signals between a mobile antenna and a fixed antenna, the method comprising:
    (310) intermittently obtaining a present channel impulse response condition characteristic, CIRCC (317), which is descriptive of CIRs (315) of transmitted signals associated with mobile antenna positions within a reach of the fixed antenna (140);
    (380) checking whether the present CIRCC (317) fits to a predetermined present CIRCC (117, 217) with which a predetermined parametrization (233) of a neural network, or another function approximator, (131) is associated, and
    (387) as longs as the check (380) reveals that (381) the present CIRCC (317) fits to the predetermined present CIRCC (117, 217), inferring (260), using the neural network or other function approximator (131), parametrized using the predetermined parametrization (233), the predetermined time of arrival (261), and
    as soon as (382) the present CIRCC (317) no longer fits to the predetermined present CIRCC (117), cease (384) the use of the neural network, or other function approximator (131), parametrized using the predetermined parametrization (233), for inferring the predetermined time of arrival, and/or initiate a compensation step in which the neural network, or other function approximator (131) is analyzed and/or retrained.

    POSITIONING METHOD, DEVICE, ROBOT AND READABLE STORAGE MEDIUM
    72.
    发明公开
    POSITIONING METHOD, DEVICE, ROBOT AND READABLE STORAGE MEDIUM 审中-实审

    公开(公告)号：EP4379410A2

    公开(公告)日：2024-06-05

    申请号：EP23198448.5

    申请日：2023-09-20

    申请人： Beijing Xiaomi Mobile Software Co., Ltd.

    发明人： JIANG, Xiaolei ,  KONG, Yaxu

    IPC分类号： G01S5/02

    CPC分类号： G01S3/30 ,  G01S5/0284 ,  G01S5/04 ,  G01S2205/0120200501 ,  G01S5/02213 ,  G01S5/0205

    摘要： The present disclosure provides a positioning method and device, a robot, and a readable storage medium. The method includes: acquiring a positioning parameter collected by each positioning module and a RSSI difference between positioning modules in each set, and determining a target positioning module based on positioning parameters collected by a set of positioning modules with a largest RSSI difference, to determine a relative positional relationship between the robot and a target device. In the present disclosure, after the positioning parameter collected by each positioning module is acquired, the target positioning module is determined based on the RSSI difference between the positioning modules in each set to position the target device, so that the robot can select the positioning module with the highest accuracy of the ranging result at present in a case of being provided with the plurality of positioning modules, and uses the positioning parameter collected by this positioning module for positioning, which improves the accuracy of the positioning result of the robot while reducing the setup cost of a robot positioning system.