公开(公告)号：US11733342B2

公开(公告)日：2023-08-22

申请号：US17380989

申请日：2021-07-20

申请人： PHY Wireless, LLC

发明人： Steven C. Thompson ,  Raphael Mall ,  Neal Riedel ,  Steven J. Caliguri ,  Zane Rau

IPC分类号： G01S5/02 ,  H04B17/327 ,  H04W64/00 ,  G01C21/00 ,  G06F18/2415 ,  G06F18/2134

CPC分类号： G01S5/0246 ,  G01C21/3815 ,  G01S5/0236 ,  G01S5/0244 ,  G01S5/0257 ,  G01S5/0284 ,  G01S5/02524 ,  G06F18/21342 ,  G06F18/2415 ,  H04B17/327 ,  H04W64/003

摘要： A method for estimating position of a mobile device which includes receiving, from a network server, observed time difference of arrival (OTDOA) assistance data for a first plurality of cells from a base station almanac (BSA) accessible to the network server. The OTDOA assistance data is stored, within a memory of the mobile device, as a first micro-BSA. A position estimate for the mobile device is determined based upon time difference of arrival (TDOA) measurements associated with an initial subset of the first plurality of cells and initial OTDOA assistance data corresponding to the initial subset of the first plurality of cells. The initial OTDOA assistance data may be generated by the micro-BSA based upon an initial seed estimate.

公开(公告)号：US20230333198A1

公开(公告)日：2023-10-19

申请号：US18212033

申请日：2023-06-20

申请人： PHY Wireless, LLC

发明人： Steven C. Thompson ,  Raphael Mall ,  Neal Riedel ,  Steven J. Caliguri ,  Zane Rau

IPC分类号： G01S5/02 ,  G01C21/00 ,  H04B17/327 ,  H04W64/00 ,  G06F18/2415 ,  G06F18/2134

CPC分类号： G01S5/0246 ,  G01S5/0244 ,  G01S5/02524 ,  G01C21/3815 ,  G01S5/0236 ,  G01S5/0257 ,  H04B17/327 ,  G01S5/0284 ,  H04W64/003 ,  G06F18/2415 ,  G06F18/21342

摘要： This disclosure provides systems, methods and apparatuses for classifying traffic flow using a plurality of learning machines arranged in multiple hierarchical levels. A first learning machine may classify a first portion of the input stream as malicious based on a match with first classification rules, and a second learning machine may classify at least part of the first portion of the input stream as malicious based on a match with second classification rules. The at least part of the first portion of the input stream may be classified as malicious based on the matches in the first and second learning machines.