Phy Wireless, LLC

Zane Rau

Steven C Thompson

Neal Riedel

Raphael Mall

Steven J Caliguri

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.

Mobile-based positioning using assistance data provided by onboard micro-BSA

A hybrid method of estimating position of a mobile device which utilizes both received signal power and timing measurements. Received signal power of signals received by the mobile device from a plurality of cells are measured and corresponding received signal power measurements are stored. The method further includes measuring, at the mobile device, times of arrival of signals received from the plurality of cells. A plurality of time difference of arrival (TDOA) measurements are determined from the times of arrival. A power-time hybrid Gaussian maximum likelihood estimator and positioning assistance data for the plurality of cells are used to generate a maximum likelihood estimate of the position of the mobile device by evaluating a joint conditional probability of the received signal power measurements and the plurality of TDOA measurements. Gaussian random variables may be used to represent the received signal power measurements and the TDOA measurements.

Mobile-based positioning using measurements of received signal power and timing

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.

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San Diego, CA, United States of America

Zane Rau