The present disclosure discloses a method for positioning a device. The method includes obtaining a location parameter associated with the device. The location parameter includes at least one of first location information or speed information. The method also includes obtaining an orientation parameter associated with the device. The orientation parameter includes at least one of first orientation information or angle velocity information. Further, the method includes determining target location information of the device by performing a first fusion calculation based on the location parameter and determining target orientation information of the device by performing a second fusion calculation based on the orientation parameter.

According to one embodiment, a computer-implemented method for dynamic, cognitive hybrid positioning within an indoor environment includes: receiving fingerprinting training data corresponding to the indoor environment, trilateration data corresponding to the indoor environment, triangulation data corresponding to the indoor environment, or a combination of the fingerprinting training data, the trilateration data, and/or the triangulation data; estimating a layout of the indoor environment based at least in part on the fingerprinting training data; classifying at least some areas of the estimated layout according to one of a plurality of predetermined area types; and determining an optimum positioning technique to utilize for each area of the estimated layout, wherein the optimum positioning technique is determined based at least in part on the area type. Corresponding system and computer program product embodiments are also disclosed, as well as hybrid techniques for determining user position within an environment.

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.

The present disclosure describes a self-positioning system, a tracking beacon and a self-positioning method for a vehicle. The self-positioning system is configured to estimate an direction of arrival of a radio wave tracking beacon signal arriving at an antenna array of the vehicle from a non-stationary tracking beacon unit, estimate Euclidian distance between the self-positioning system and the tracking beacon unit by using wireless radio-frequency communication between the self-positioning system and the tracking beacon unit, and determine position data identifying a three-dimensional position of the self-positioning system with respect to tracking beacon unit on the basis of the estimates of the direction of arrival and the Euclidian distance.

Disclosed are techniques for wireless positioning. In an aspect, a user equipment (UE) performs one or more positioning measurements of positioning reference signals (PRS) transmitted by at least one set of transmission-reception points (TRPs) of one or more sets of TRPs, wherein each set of TRPs of the one or more sets of TRPs satisfies a dilution of precision (DOP) threshold, and reports the one or more positioning measurements or location information derived from the one or more positioning measurements.

Aspects of the subject disclosure may include, for example, selecting, according to selection information wirelessly transmitted from one or more anchors, a particular position estimation mode from among a group of position estimation modes that are each associated with a different radio measurement locating process. One or more radio measurements can be obtained from at least one of the one or more anchors and position information can be determined for the mobile device based on the one or more radio measurements by applying a particular radio measurement locating process of the particular position estimation mode. Other embodiments are disclosed.

A system includes a plurality of wireless transmitters and a processor configured to: receive, from a mobile device, signal strengths of signals from the wireless transmitters as detected by the mobile device. The processor is also configured to determine a location of the mobile device in a vehicle, based on a distance from the mobile device to each of the wireless transmitters, as indicated by the received signal strengths and store the location of the mobile device as an occupant location.

A hyper-precise positioning and communications (HPPC) system and network are provided. The HPPC system is a next-generation positioning technology that promises a low-cost, high-performance solution to the need for more sophisticated positioning technologies in increasingly cluttered environments. The HPPC system is a joint positioning-communications radio technology that simultaneously performs relative positioning and secure communications. Both of these tasks are performed with a single, co-use waveform, which efficiently utilizes limited resources and supports higher user densities. Aspects of this disclosure include an HPPC system for a network which includes an arbitrary number of network nodes (e.g., radio frequency (RF) devices communicating over a joint positions-communications waveform). As such, networking protocols and design of data link and physical layers are described herein. An exemplary embodiment extends the HPPC system for use with existing cellular networks, such as third generation partnership project (3GPP) long term evolution (LTE) and fifth generation (5G) networks.

A system includes a plurality of wireless transmitters and a processor configured to: receive, from a mobile device, signal strengths of signals from the wireless transmitters as detected by the mobile device. The processor is also configured to determine a location of the mobile device in a vehicle, based on a distance from the mobile device to each of the wireless transmitters, as indicated by the received signal strengths and store the location of the mobile device as an occupant location.

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.