A method of associating data with a physical location comprises receiving, by at least two receiver antennae, a radiofrequency (RF) signal transmitted by a mobile device, the RF signal conveying data collected by the mobile device from an external source; calculating, for each of the at least two receiver antennae, a phase of the RF signal received by each receiver antennae; calculating, based on the calculated phases, a physical location from where the mobile device transmitted the RF signal; and associating the data conveyed by the RF signal and the external source from which the data were collected with the calculated physical location from where the mobile device transmitted the RF signal.

Processes and device configurations, including a receiver structure, are provided to jointly estimate the time-of-arrival (TOA) and azimuth and elevation angles of direction-of-arrival (DOA) from signals of opportunity, such as received cellular long-term evolution (LTE) signals. In one embodiment, a matrix pencil (MP) algorithm is used to obtain a coarse estimate of the TOA and DOA. Tracking loop configurations are provided to refine the estimates and jointly track the TOA and DOA changes. One or more solutions are provided for acquisition and tracking in the presence of noise and multipath signals. Processes and devices configurations are provided to use refined estimates to determine position and for use in navigation of a device.

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 first device determines relative position data representative of a position of one or more other user devices relative to the first device. To determine relative position data between the first device and a second device, the first device determines a distance between the first device and the second device at a plurality of timestamps. Additionally, the first device determines movement data at each timestamp from one or more device sensors. The movement data at each corresponding timestamp may reflect movement of the first device and/or the second device between a prior timestamp and the corresponding timestamp. The first device computes relative position data for the second device by combining the distance measurements and movement data over the plurality of timestamps, for instance, through a process of sensor fusion. By computing the relative position data, the first device may determine a transformation that can be used to convert between a coordinate system of the second device and the coordinate system of the first device.

Systems and methods are described herein for communications bandwidth enhancement using Orthogonal Spatial Division Multiplexing (OSDM). For example, large sparse antenna arrays may be able to distinguish between signals emitted by multiple nearly collocated antennas, even if the signals have the same frequency, polarization, and coverage. Thus, the use of a large sparse antenna array may be able to resolve/isolate individual antennas on a single platform, allowing for OSDM, analogous to Orthogonal Frequency Divisional Multiplexing (OFDM). Using OSDM, multiple antennas on the same vehicle are able to reuse the same frequencies/polarizations without interference, thereby increasing spectrum availability while still providing the same transmitter power spectral density and total RF power emission.

The disclosed methods and systems use artificial intelligence (AI) and machine learning (ML) technologies to model the usage and interference on each channel. For example, units of the system can measure channel interference regularly over the time of day on all radios. The interference information is communicated to the base unit or a cloud server for pattern analysis. Interference measurements include interference from units within the system as well as interference from nearby devices. The base unit or the cloud server can recognize the pattern of the interference. Further, connected devices have a number of network usage characteristics observed and modeled including bitrate, and network behavior. These characteristics are used to assign channels to connected devices.

A device may receive a first type of data identifying measurements associated with user devices and/or base stations of a mobile radio environment, and a second type of data identifying spatiotemporal behavior associated with the user devices. The device may train a first model, with the first type of data, to generate a trained first model that yields dimensionality-reduced spatiotemporal characteristics of the first type of data, and may train a second model, with the second type of data and the dimensionality-reduced spatiotemporal characteristics, to generate a trained second model. The device may receive particular data identifying measurements associated with a user device and/or base stations, and may process the particular data, with the trained first model, to generate a dimensionality-reduced spatiotemporal characteristic of the particular data. The device may process the dimensionality-reduced spatiotemporal characteristic, with the trained second model, to predict a spatiotemporal behavior of the user device.

A hybrid distributed estimation system (DES) jointly tracks states of a plurality of moving devices configured to transmit measurements indicative of a state of a moving device and an estimation of the state of the moving device derived from the measurements. The hybrid DES selects between the measurements and the estimations, and based on this selection activates different types of DESs configures to jointly track the states of the moving devices using different types of information. Next, the hybrid DES tracks the states using the activated DES allowing track the state by different DES at different instances of time.

In an embodiment, a wireless node (e.g., UE or BS) receives from a network component (e.g., BS or core network component) a request for a positioning estimate of a UE associated with a specified time. The wireless node performs positioning measurements at a plurality of times, and determines (e.g., via interpolation or extrapolation) the positioning estimate associated with the specified time based on the positioning measurements. The wireless node transmits, to the network component, a report comprising the determined positioning estimate.

A mobile transceiver for asset tracking having route monitoring and method of operation are provided. In one aspect, the method comprises: determining a location of the mobile transceiver using the satellite receiver; determining whether the determined location deviates from a planned route; and sending an alert to an asset tracking service when the determined location deviates from the planned route.