Abstract: Many modern Global navigational satellite systems (GNSS) utilize signaling that includes a primary pseudorandom number (PRN) code and a secondary low rate PRN code, the latter of which is modulo-2 added to the first. In some GNSS acquisition receivers in which precise a priori synchronization information is unavailable, the acquisition process performs a set of correlation operations that begin at a time that is not aligned with the epoch of the secondary PRN code. This epoch misalignment results in loss of system performance. This disclosure provides new methods for dealing with such epoch misalignments and minimizes system performance loss. In one embodiment, a method combines a first and a second correlation operation, each using a different correlation epoch, to reduce the effect of epoch misalignments.

Abstract: 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.

Abstract: Machine learning techniques can be used to mitigate multipath in a GNSS receiver that includes a first trained model that provides extra path length (EPL) corrections in the GNSS receiver. The first trained model can be updated using an updated and trained model from one or more assistance servers that are in communication with the GNSS receiver. The GNSS receiver can provide, for a particular computed position and time, extracted features from received GNSS signals to the one or more assistance servers. The assistance servers can then use the extracted features and a source of true EPL corrections (e.g., from a 3D building map database for the particular computed position and time) to train a server model. The server model, once trained to a desired level of accuracy, can be transmitted to the GNSS receiver to replace the first trained model.

Abstract: This disclosure describes methods, systems and machine readable media that can provide position solutions using, for example, pattern matching with GNSS signals in urban canyons. In one method, based upon an approximate location in an urban canyon and a set of 3D data about building structures in the urban canyon, an expected signal reception data can be generated for both line of sight and non-line of sight GNSS signals from GNSS satellites, or other sources of GNSS signals, at each point in a set of points in a grid (or other model) in the vicinity of the approximate location). This expected signal reception data can be matched to a received set of GNSS signals that have been received by a GNSS receiver, and the result of the matching can produce an adjustment to the approximate location that is used in the position solution of the GNSS receiver.

Abstract: Global navigation satellite systems and methods use L5 GNSS signals to acquire secondary code phases of those signals without using L1 GNSS signals to aid in the acquisition of secondary code phases. Various embodiments are described to perform this acquisition.

Abstract: Global navigation satellite systems and methods use L5 GNSS signals to acquire secondary code phases of those signals without using L1 GNSS signals to aid in the acquisition of secondary code phases. Various embodiments are described to perform this acquisition.

Abstract: GNSS receivers and systems within such receivers use improvements to reduce memory usage while providing sufficient processing resources to receive and acquire and track E5 band GNSS signals directly (without attempting in one embodiment to receive L1 GNSS signals). Other aspects are also described.