Systems and methods for fault detection, exclusion, isolation, and re-configuration of navigation sensors using an abstraction layer are provided. In certain embodiments, a system includes a plurality of sensors that provide redundant sensor measurements, wherein redundancy of the redundant sensor measurements is achieved based on an independence between measurements from different physical sensor units in the plurality of sensors. The system additionally includes a fusion function configured to receive the redundant sensor measurements from each sensor in the plurality of sensors and calculate fused navigation parameters. Further, the system includes an abstraction layer that calculates an estimated state based on the fused navigation parameters, wherein the estimated state comprises safety assessment information for the fused navigation parameters and the fused navigation parameters. Moreover, the system includes a plurality of user systems, wherein each user system in the plurality of user systems receives the estimated state from the abstraction layer.

The present invention relates to a method and apparatus for detecting a signal propagation type, the method comprises: when a positioning base station of an ultra-wideband positioning system currently receives a pulse response from a positioning tag, computing an actual value of a specified feature of the received pulse response at least using the received pulse response; selecting, for the specified feature, a predictive model for predicting an adopted value of the specified feature at a future moment on the basis of an adopted value of the specified feature at a historical moment; using the predictive model selected for the specified feature to acquire an adopted value of the specified feature at a future moment, to serve as a predicted value of the specified feature of the received pulse response; and determining the current type of signal propagation between the positioning base station and positioning tag on the basis of the actual value and predicted value of the specified feature of the received pulse response and the predictive model selected for the specified feature. Using the method and apparatus, it is possible to detect the type of signal propagation between the positioning base station and positioning tag of the UWB positioning system.

In some implementations, a method may comprise obtaining channel state information (CSI) data corresponding to a set of RF signals received by one or more receiving devices, wherein: the set RF signals comprises two or more reflected RF signals successively received by the one or more receiving devices after being reflected from a person, and the two or more reflected RF signals are received by the one or more receiving devices over a period of time. The method may further comprise determining an identity of the person based at least in part on an observed gait of the person and an observed shape of the person, wherein the observed gait of the person and the observed shape of the person are determined based at least in part on the CSI data. The method may further comprise outputting an indication of the determined identity of the person.

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.

Disclosed is a sensing device configured to perform movement detection by using a measurement value representing a channel state measured using a wireless LAN signal. The sensing device includes a memory, and a processor. The processor is configured to obtain a standard deviation of magnitudes of the measurement values over a predetermined number of measurements or within a predetermined time interval, and obtain a feature value of a sensing algorithm, based on the obtained standard deviation value. The sensing algorithm is an algorithm learned based on a correlation between a value obtained based on the magnitudes of the measurement values and the presence or absence of movement around devices exchanging the wireless LAN signal.

Methods, systems, and apparatus, including medium-encoded computer program products, for 3D flight tracking of objects include a method including determining a three dimensional trajectory based on initial observations, extrapolating the three dimensional trajectory backward in time to generate an extrapolated trajectory, calculating distance measures between the extrapolated trajectory and defined physical locations, waiting for additional observations when none of the distance measures satisfy a threshold distance, identifying one of the defined physical locations as an origin when only one of the distance measures satisfies the threshold distance and an error measure satisfies a predefined criteria, identifying one of the defined physical locations as the origin when two of the distance measures satisfy the threshold distance and only one of first and second error measures satisfies the predefined criteria, and waiting for additional observations when neither the first error measure nor the second error measure satisfies the predefined criteria.

A system for tracking at least one object configured to be transported by at least one vehicle may include at least one computer system. The at least one computer system may be configured to determine at least one location of the at least one vehicle and determine at least one location of the at least one object. The at least one computer system may be further configured to determine at least one location of at least one geofence adjacent the at least one vehicle based on the at least one location of the at least one vehicle. Also, the at least one computer system may be configured to determine whether the at least one object is located within the at least one geofence to determine whether a load of the at least one vehicle includes the at least one object.

A networked system for managing a physical intrusion detection/alarm includes an upper tier of server devices, comprising: processor devices and memory in communication with the processor devices, a middle tier of gateway devices that are in communication with upper tier servers, and a lower level tier of devices that comprise fully functional nodes with at least some of the functional nodes including an application layer that execute routines to provide node functions, and a device to manage the lower tier of devices, the device instantiating a program manager that executes a state machine to control the application layer in each of the at least some of the functional nodes.

This document discloses a solution for controlling measurements of a terminal device. According to an aspect, a method comprises: determining, on the basis of measurement data measured from a radio link, a distance between an access node and a terminal device and angular motion of the terminal device with respect to the access node; making a decision about a length of a time interval between consecutive radio measurements of the terminal device on the basis of the distance and the angular motion; and communicating the decision over the radio link.

Systems and methods for locating collocated assets in accordance with embodiments of the invention are disclosed. In one embodiment of the invention, an asset tracking device includes a processor, a memory connected to the processor, and a communications device connected to the processor, wherein the processor obtains asset signal data, where the asset signal strength data includes asset data identifying an asset, calculates asset signal strength data based on the obtained asset signal data, and determines chained asset data based on the asset signal data and the asset signal strength data, where the chained asset data identifies a set of assets chained to a master asset associated with the asset tracking device.