Machine learning techniques are used, in one embodiment, to mitigate multipath in an L5 GNSS receiver. In one embodiment, training data is generated to provide ground truth data for excess path length (EPL) corrections for a set of received GNSS signals. A system extracts features from the set of received GNSS signals and uses the extracted features and the ground truth data to train a set of one or more neural networks that can produce EPL corrections for pseudorange measurements. The trained set of one or more neural networks can be deployed in GNSS receivers and used in the GNSS receivers to correct pseudorange measurements using EPL corrections provided by the trained set of neural networks.

Navigation device including an inertial navigation system coupled with a satellite navigation system, the information supplied by the satellite positioning system being used for adjusting the inertial navigation system, characterised in that the device further comprises means for measuring signals coming from base stations of a wireless cellular network, the satellite navigation system and the means for measuring signals coming from base stations of the wireless cellular network are coupled with each other by a tight coupling to form a satellite navigator and/or base-station navigator implementing an estimator with simultaneous localisation and mapping, and in that the satellite navigation system and the means for measuring signals coming from base stations of the wireless cellular network are coupled to the inertial navigation system by a loose coupling for adjusting the inertial navigation system.

Navigation device including an inertial navigation system coupled with a satellite navigation system, the information supplied by the satellite positioning system being used for adjusting the inertial navigation system, characterised in that the device further comprises means for measuring signals coming from base stations of a wireless cellular network, the satellite navigation system and the means for measuring signals coming from base stations of the wireless cellular network are coupled with each other by a tight coupling to form a satellite navigator and/or base-station navigator implementing an estimator with simultaneous localisation and mapping, and in that the satellite navigation system and the means for measuring signals coming from base stations of the wireless cellular network are coupled to the inertial navigation system by a loose coupling for adjusting the inertial navigation system.

Presented are intelligent electronic footwear and apparel with controller-automated features, methods for making/operating such footwear and apparel, and control systems for executing automated features of such footwear and apparel. A method for operating an intelligent electronic shoe (IES) includes receiving, e.g., via a controller through a wireless communications device from a GPS satellite service, location data of a user. The controller also receives, e.g., from a backend server-class computer or other remote computing node, location data for a target object or site, such as a virtual shoe hidden at a virtual spot. The controller retrieves or predicts path plan data including a derived route for traversing from the user's location to the target's location within a geographic area. The controller then transmits command signals to a navigation alert system mounted to the IES's shoe structure to output visual, audio, and/or tactile cues that guide the user along the derived route.

A vehicle includes receiving signals from a plurality of satellites; obtaining position information based on the received signal; detecting a driving speed and yaw rate; obtaining dead reckoning information based on position information about a position of a vehicle recognized in a previous cycle and the received detection information; predicting the position information based on the obtained dead reckoning information; obtaining a value of Euclidean distance based on the position information about the position of the vehicle recognized in the previous cycle and the obtained position information; generating a first outlier filter based on the value of the Euclidean distance; obtaining a value of Mahalanobis distance based on the obtained position information and the predicted position information; generating a second outlier filter based on the value of the Mahalanobis distance; recognizing a current position of the vehicle by fusing information passing through the first outlier filter and information passing through the second outlier filter; and outputting information about the current position of the recognized vehicle as an image or a sound.

In one embodiment, a system includes a global navigation satellite system (GNSS) receiver unit, a first inertial measurement unit (IMU) and a second IMU. The system may further include a first micro-controller unit (MCU) coupled to the first IMU and the GNSS receiver unit to receive data from the first IMU and the GNSS receiver unit and a second MCU coupled to the second IMU and the GNSS receiver unit to receive data from the second IMU and the GNSS receiver unit.

There is disclosed a method of updating a database of positioning data, using a mobile user device moved along a path through a plurality of positions, the method comprising the steps of: at each of the plurality of positions: receiving position estimate data and measurement data from a plurality of positioning modules associated with the mobile user device; calculating an estimate of the position in dependence on the data received from the plurality of positioning modules; and storing the estimate of the position and the measurement data; subsequently processing the stored measurement data to calculate at least one revised estimate of a respective position; and processing said at least one revised estimate to update the database of positioning data.

There is disclosed a method of updating a database of positioning data, using a mobile user device moved along a path through a plurality of positions, the method comprising the steps of: at each of the plurality of positions: receiving position estimate data and measurement data from a plurality of positioning modules associated with the mobile user device; calculating an estimate of the position in dependence on the data received from the plurality of positioning modules; and storing the estimate of the position and the measurement data; subsequently processing the stored measurement data to calculate at least one revised estimate of a respective position; and processing said at least one revised estimate to update the database of positioning data.

A method of determining a location of a mobile device in the presence of a spoofing signal includes obtaining current position information associated with the mobile device, determining a Global Navigation Satellite System (GNSS) signal search window for acquiring GNSS signals associated with a satellite based on the current position information, searching a GNSS signal associated with the satellite based on the GNSS signal search window, and determining updated position information of the mobile device based on at least information of the GNSS signal associated with the satellite.

A method is provided for calculating the estimated navigation performance prediction for a trajectory associated with a list of segments of a flight plan. A method for displaying the navigation performance in a corridor trajectory so as to guarantee compliance with the navigation performance requirements while offering immediate viewing of the navigation latitude in a corridor is also provided.