A multibeam Radio Frequency (RF) lens antenna is designed as a receiver for Global Navigation Satellite System (GNSS) applications, such as GPS (Global Positioning System), Galileo, GLONASS, COMPASS, and others. The RF lens and plurality of associated feed elements and receiver circuits combine to form a plurality of resulting high-gain relatively narrow beams that, taken together, allow reception of signals from GNSS satellites over the entire upper hemisphere. Any kind of RF lens can be used, where the lens can be of homogeneous or inhomogeneous, dielectric or metamaterial/metasurface construction. The benefit of this approach to build a GNSS receiver over existing alternatives is increased gain and decreased noise at each receiver, which improves the signal to noise ratio (SNR) and improves the accuracy and reliability of the position and time measurements, while also reducing the impact of, and sensitivity to, interference, jamming, and spoofing signals. The approaches described in this patent can be combined with existing signal processing and accuracy improvement methods (such as Real-Time Kinematic (RTK), Precise-Point Positioning (PPP), and Differential GPS (DEPS)) for further benefits. This system has applications within the surveying, maritime, land mobility, aerospace, and government positioning market areas.

A multibeam Radio Frequency (RF) lens antenna is designed as a receiver for Global Navigation Satellite System (GNSS) applications, such as GPS (Global Positioning System), Galileo, GLONASS, COMPASS, and others. The RF lens and plurality of associated feed elements and receiver circuits combine to form a plurality of resulting high-gain relatively narrow beams that, taken together, allow reception of signals from GNSS satellites over the entire upper hemisphere. Any kind of RF lens can be used, where the lens can be of homogeneous or inhomogeneous, dielectric or metamaterial metasurface construction. The benefit of this approach to build a GNSS receiver over existing alternatives is increased gain and decreased noise at each receiver, which improves the signal to noise ratio (SNR) and improves the accuracy and reliability of the position and time measurements, while also reducing the impact of, and sensitivity to, interference, jamming, and spoofing signals. The approaches described in this patent can be combined with existing signal processing and accuracy improvement methods (such as Real-Time Kinematic (RTK), Precise-Point Positioning (PPP), and Differential GPS (DEPS)) for further benefits. This system has applications within the surveying, maritime, land mobility, aerospace, and government positioning market areas.

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. The server model can be compared to the first trained model to verify it can provide more accurate EPL corrections than the first trained model. The server model and the source of true EPL corrections can be specific for a geographic region, so different regions have different server models based on the corresponding sources of true EPL corrections.

A PVT calculation device includes a memory; and one or more processors in communication with the memory configured to perform operations including: receiving observations and ephemerides from satellites to obtain PVT data of the satellites and predicted PVT results of the receiver; setting up observation functions respectively corresponding to the satellites; calculating by a least square solution first estimated PVT results of the receiver based on the observation functions; iteratively eliminating by a Random-Sampling Iterative Kalman Filter (RSIKF) algorithm fault observation functions from the observation functions in an inner cluster until no fault observation functions detected in the inner cluster; calculating by the RSIKF algorithm a second estimated PVT results of the receiver using the observation functions in the inner cluster; and outputting final estimated PVT results of the receiver. The PVT calculation device may calculate the PVT results of the receiver with improved accuracy and stability.

A receiver determines whether an outbound carrier frequency among a plurality of outbound carrier frequencies, as received, includes interference. Based at least in part on a result of the determining, a new outbound carrier frequency is selected for the receiver. Optionally, the receiver sends an interference report to a system controller.

A receiver determines whether an outbound carrier frequency among a plurality of outbound carrier frequencies, as received, includes interference. Based at least in part on a result of the determining, a new outbound carrier frequency is selected for the receiver. Optionally, the receiver sends an interference report to a system controller.

The disclosure relates to a method for GNSS-based localization of a vehicle, comprising at least the following steps: a) receiving GNSS-satellite signals from GNSS satellites and determining at least one item of distance information about the distance between the vehicle and the GNSS satellite emitting the relevant GNSS-satellite signal, b) determining at least one item of environmental information about the environment around the vehicle using image information determined using at least one environment sensor of the vehicle, which is capable of capturing images of at least part of the environment around the vehicle from different perspectives, c) determining at least one item of correction information using the at least one environmental information item, and d) correcting the at least one distance information item by means of the at least one correction information item.

Systems and methods for selective and/or opportunistic GNSS/GPS navigation that actively mask or filter satellite signals based on identified “clear sky” or “obstructed sky” regions.

A method for estimating a time of arrival of a signal transmitted over a wireless channel, includes receiving the signal by a receiving device; correlating the received signal with a filtered code sequence to create a correlation output, identifying in the correlation output, an observation window associated with a main lobe in the correlation output; and processing the observation window to determine a time of arrival of a first path component in the received signal. The filtered code sequence is formed by incorporating a time of arrival matched filter (TOA-MF) inside predetermined shaped code sequence. The TOA-MF is matched to the predetermined shaped code sequence and is based upon a power delay profile of the wireless channel. The predetermined shaped code sequence is a convolution of a predetermined shaping sequence and a predetermined code sequence.

A device implementing a system for estimating device position includes at least one processor configured to receive a first sensor measurement of a device at a first time, the first sensor measurement having a first variance in measurement error, and to receive a second sensor measurement of the device at a second time, the second sensor measurement having a second variance in measurement error. The at least one processor is further configured to determine a speed of the device based on at least one of the first or second sensor measurements, and adjust the second variance in measurement error based on the determined speed. The at least one processor is further configured to estimate a device position based at least in part on the first variance in measurement error and the adjusted second variance in measurement error.