Described herein are techniques for determining motion characteristics of trains traveling along a train track. In some embodiments, a processor may determine an estimated position of a train using an observed position obtained using one or more UWB antennas and an observed position obtained using one or more GNSS receivers. In some embodiments, a processor may access information specifying a geometry of a train track and determining the position of a train along the train track using an observed position determined using one or more UWB antennas and/or GNSS receiver(s) and the information specifying the geometry of the train track. In some embodiments, a processor may determine estimated positions of a train using the geometry of the train track and at least one observation of the train obtained using one or more positioning devices and select the position of the train from among the estimated positions.

Technologies for determining the location of a telematics device during communication mode switching of the telematics device include switching a communication processor of the telematics device between a location mode and a data mode. In the location mode, the communication processor is configured to receive location data from a location beacon system and determine a present location of the telematics device. In the data mode, the communication processor is configured to transmit and receive data communications from a remote computer system(s). While in the data mode, the telematics device may estimate its location based on a last determined present location and inertial sensor data received from inertial sensors of the telematics device using dead reckoning.

There is disclosed a system and method for forecasting the positions of marine vessels. In an aspect, the present system is adapted to execute a forecasting algorithm to forecast the positions of one or a great many marine vessel(s) based on one or more position reporting systems including coastal and satellite AIS (S-AIS) signals or LRIT received from the vessel. The forecasting algorithm utilizes location and direction information for the vessel, and estimates one or more possible positions based on previous paths taken by vessels from that location, and heading in substantially the same direction. Thus, a body of water can be divided into “bins” of location and direction information, and a spatial index can be built based on the previous paths taken by other vessels after passing through that bin. Other types of information may also be taken into account, such as ship-specific data, nearby weather, ocean currents, the time of year, and other spatial variables specific to that bin.

A mobile computing device includes: a tracking sensor; a proximity sensor; and a controller coupled to the tracking sensor and the proximity sensor, the controller configured to: obtain a sequence of sensor datasets, each sensor dataset including: (i) a location of the mobile computing device, in a local coordinate system, generated using the tracking sensor, (ii) a proximity indicator generated using the proximity sensor, defining a range to a fixed reference device, and (iii) a predefined location of the reference device in a facility coordinate system; determine, from the sequence, an adjusted pose of an origin of the local coordinate system in the facility coordinate system; and generate, using a current location of the mobile device in the local coordinate system and the adjusted pose, a corrected location of the mobile computing device in the facility coordinate system; and execute a control action based on the corrected location.

A system for locating and tracking an object is provided. The system includes a measuring device configured to determine a property of a paving-related material, a locating device configured to determine a location of the measuring device, a tracking system configured to store tracking information associated with the measuring device and one or more properties determined by the measuring device, and a communications system configured to transfer, to a remote device, the location of the measuring device and the tracking information associated with the measuring device.

Examples herein involve estimating a first position of a mobile device based on first communication signals, assigning a first set of particles to a number of respective first sampling locations within a threshold distance of the first position, adjusting the assignment of the first set of particles to second sampling locations based on movement of the mobile device, and estimating a second position of the mobile device based on the second sampling locations.

A method for determining an object's position using different items of sensor information includes: a) reading the sensor information into a Kalman filter, b) merging the sensor information with the Kalman filter, with the Kalman filter supplying as a result estimated values for states and information associated with the estimated values regarding the accuracy of the estimates, c) monitoring the results of the Kalman filter by estimating a probability of accuracy, with which the estimation error lies within an error band, with the probability of accuracy being determined on the basis of a plurality of conditional probabilities, the conditions for which each relate to estimation errors from at least one earlier series.

A method and a system for vehicle-to-pedestrian collision avoidance system, the system comprising participants consisting of Long-Term Evolution (LTE)-capable user equipment (UE) terminals physically linked to at least one vehicle and at least one pedestrian; wherein a spatiotemporal positioning of the terminals is determined from Long Term Evolution (LTE) cellular radio signals mediated by Long-Term Evolution (LTE) cellular base stations (BS) and a Location Service Client (LCS) server including an embedded Artificial Intelligence algorithm comprising a Recurrent Neural Network (RNN) algorithm and analyzes the spatiotemporal positioning of the terminals and determines the likely future trajectory and communicates the likely future trajectory of the participants to the terminals physically linked to the pedestrian; the terminals physically linked to the pedestrian include an embedded Artificial Intelligence algorithm comprising a Conditional Random Fields (CRFs) algorithm to determine if the likely future trajectory of the pedestrian is below a vehicle-to-pedestrian proximity threshold limit and, if this condition is reached, communicates a collision-avoidance emergency signal to the at least one pedestrian and/or vehicle that meet the proximity threshold limit.

Object tracking anti-jitter filtering systems and methods. A plurality of raw location points for a tracking tag attached to a tracked object is received. The raw location points are stored within a raw location points buffer. Raw location points within an averaging window are averaged to generate an averaged location point. The averaged location point is stored within an averaged location points buffer for use within the object tracking system.

Methods, systems, and computer program products for determining transit routes through crowd-sourcing, for determining an estimated time of arrival (ETA) of a vehicle of the transit route at a given location, and for providing predictive reminders to a user for catching a vehicle of the transit route. A server receives signal source information about wireless signal sources detected by user devices, including information about a first wireless signal source detected by some devices. The server determines that the first wireless signal source is moving. The server determines that the first wireless signal source is associated with a public transit route upon determining that the signal source information satisfies one or more selection criteria. The server stores information associating the first wireless signal source with the public transit route as transit movement data corresponding to the public transit route.