Methods, systems and computer program products for determining and filtering potential outliers in RF signals used in radionavigation are described. A radionavigation subsystem of a mobile device can determine a first location estimate of the mobile device. The mobile device can determine a free direction from the first location estimate. The free direction can be a direction along which RF signals may cause greater position errors than RF signals from other directions may cause. The mobile device can determine a potential outlier among the received RF signals, the potential outlier being an RF signal from a signal source in the free direction. The mobile device can indicate to the radionavigation subsystem that a weight of the potential outlier shall be reduced when determining a second location estimate of the mobile device using the RF signals.

A vehicle, for example, an autonomous vehicle performs localization to determine the current location of the vehicle using different localization techniques as the vehicle drives. The localization technique used by the autonomous vehicle is selected from a localization variant index that stores mapping from a driving context to localization variant, each localization variant identifying a localization technique. The driving context may comprise information including: a geographical region in which the autonomous vehicle is driving, a speed at which the autonomous vehicle is driving, an angular velocity of the autonomous vehicle, or other information. Using an optimal localization technique in each driving context improves the accuracy of localization as well as computing efficiency of the process of localization.

Examples are disclosed for techniques for a new method of calculating multiple locations of a wireless communication device based on the same or a subset of the same raw data measurements (such as WiFi Time-Of-Flight/Fine-Time-Measurements or RSSI) and client specific fixed location device locations sources and/or database. Additionally, new APIs and implementation methods to support the new method are disclosed.

A method and system for estimating an angular deviation from a reference guidance axis, a position and a velocity of an aircraft. The system includes an offset collection module for collecting an offset measured by a measurement module, a position vector collection module for collecting a position vector measured by a position vector measurement module, a velocity vector collection module, a module for estimating the angular deviation of a reference guidance axis with respect to the approach axis towards the runway, for estimating the position of the aircraft with respect to the runway and for estimating the velocity of the aircraft with respect to the runway.

The present invention relates to a multimodal sensing positioning system orientated to a high-risk production environment, the positioning system comprising: at least one positioning terminal, configured to be worn by a to-be-positioned subject and use at least one positioning technique to conduct multimodal sensing positioning so as to identify a current location information of the subject in the high-risk production environment; and a monitoring terminal, communicating with the positioning terminal so as to remotely monitor the current location of the subject. The present invention improves positioning precision while ensuring realtimeness of multimodal positioning.

Described herein are different distributed sensor network models, use cases, and details of the components of each model to achieve the goal of monitoring, detecting, tracking, and mitigating a target(s) such as a signal, an object, a phenomenon, etc. An independent sensor or a local sensor network may supply data to one or more fusion center(s) that collect(s) data and perform(s) higher logic to enhance system performance. A local sensor network allows independent sensors or other local sensor networks to merge into the local sensor network. A sensor cloud can be formed by multiple local sensor networks and independent sensors. By using different distribution models, the local sensor network can provide protection for various targets like Very Important Personnel (VIP) vehicles, lands, facilities, and cities.

A jobsite computer network has a local server connectable to a remote server via the internet, a network access transceiver connected to the local server, and a wireless device in wireless communication with the network access transceiver. The network access transceiver receives a message from the wireless device and sends a message to the local server. The local server utilizes information from the message to determine the location of the wireless device. The network access transceiver and/or the wireless device generate data which is sent to the remote server.

Embodiments of a method of locating a guideway mounted vehicle are disclosed. In one embodiment, a communication signal is transmitted to a wayside communication device. A range estimation is obtained based on the communication signal. A radar signal is transmitted to at least one reflector. An accuracy of the range estimation is increased based on the radar signal.

Disclosed are techniques for signaling of reception-to-transmission measurements from gNBs and user equipments (UEs) for round trip time (RTT) based positioning in wireless networks such as new radio (NR). In multi-RTT positioning, the flow of messages that are exchanged differ depending on the entity acting as the location server determining the position of the UE.

Various embodiments are directed to localizing and identifying a mobile computing device using a short-range mesh network. A user of the mobile computing device may be authenticated. When the mobile computing device connects to the mesh network, the location of the mobile computing device may be determined by determining which mesh network nodes the mobile computing device is connected to, triangulating the position of the mobile computing device based on signal direction via directional antennas arranged on the mesh network nodes, and/or measuring signal strength of the mobile computing device at the mesh network nodes.