Described are methods, systems, and devices for determining position using Differential Global Navigation Satellite (DGNSS) measurements. Techniques described herein involve taking carrier phase measurements at a reference station or other GNSS receiver at a known location, and combining the carrier phase measurements with pseudorange measurements taken at the reference station to resolve carrier phase ambiguity and to, in combination with pseudorange measurements taken at a mobile device, obtain a differentially corrected measurement that can be used to estimate a position of the mobile device. The differentially corrected measurement can be a double differential measurement based on signals from a pair of GNSS satellites.

Described are methods, systems, and devices for determining position using Differential Global Navigation Satellite (DGNSS) measurements. Techniques described herein involve taking carrier phase measurements at a reference station or other GNSS receiver at a known location, and combining the carrier phase measurements with pseudorange measurements taken at the reference station to resolve carrier phase ambiguity and to, in combination with pseudorange measurements taken at a mobile device, obtain a differentially corrected measurement that can be used to estimate a position of the mobile device. The differentially corrected measurement can be a double differential measurement based on signals from a pair of GNSS satellites.

A probabilistic system for tracking a state of a vehicle using unsynchronized cooperation of information includes a probabilistic multi-head measurement model relating incoming measurements with the state of the vehicle. The first head of the model relates measurements of the satellite signals subject to measurement noise with a belief on the state of the vehicle, and a second head relates an estimation of the state of the vehicle subject to estimation noise with the belief on the state of the vehicle. A probabilistic filter of the system updates recursively the belief on the state of the vehicle based on the multi-head measurement model accepting one or a combination of the measurements of the satellite signals subject to the measurement noise and the estimation of the state of the vehicle subject to the estimation noise.

A method can include receiving sensor data, receiving satellite observations, determining a positioning solution (e.g., PVT solution, PVA solution, kinematic parameters, etc.) based on the sensor data and the satellite observations. A system can include a sensor, a GNSS receiver, and a processor configured to determine a positioning solution based on readings from the sensor and the GNSS receiver.

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 method can include receiving sensor data, receiving satellite observations, determining a positioning solution (e.g., PVT solution, PVA solution, kinematic parameters, etc.) based on the sensor data and the satellite observations. A system can include a sensor, a GNSS receiver, and a processor configured to determine a positioning solution based on readings from the sensor and the GNSS receiver.

Binary-phase-shift-key, phase-modulated waveforms with gigahertz bandwidths, suitable for kilowatt-class fiber amplifiers, can be narrowed back to the source laser’s linewidth via second-harmonic, sum-frequency, or difference-frequency generation in a second-order nonlinear crystal. The spectrum of an optical signal phase-modulated with a pseudo-random bit sequence (PRBS) waveform recovers its original optical spectrum when frequency-doubled using second-harmonic generation (SHG). Conceptually, the PRBS waveform is cancelled by the SHG process, and the underlying laser spectrum is converted to the second-harmonic wavelength as though the PRBS modulation were not present. The same cancellation is possible with sum-frequency generation (SFG) and difference frequency generation (DFG), making it possible to construct high-power, narrow-linewidth lasers at wavelengths from the visible to the long-wave infrared. Using ytterbium-, erbium-, thulium-, and neodymium-doped fibers with SHG, SFG and DFG processes allows generation of high-power beams with very narrowband optical spectra and wavelengths from below 400 nm to beyond 5 .Math.m.

Binary-phase-shift-key, phase-modulated waveforms with gigahertz bandwidths, suitable for kilowatt-class fiber amplifiers, can be narrowed back to the source laser’s linewidth via second-harmonic, sum-frequency, or difference-frequency generation in a second-order nonlinear crystal. The spectrum of an optical signal phase-modulated with a pseudo-random bit sequence (PRBS) waveform recovers its original optical spectrum when frequency-doubled using second-harmonic generation (SHG). Conceptually, the PRBS waveform is cancelled by the SHG process, and the underlying laser spectrum is converted to the second-harmonic wavelength as though the PRBS modulation were not present. The same cancellation is possible with sum-frequency generation (SFG) and difference frequency generation (DFG), making it possible to construct high-power, narrow-linewidth lasers at wavelengths from the visible to the long-wave infrared. Using ytterbium-, erbium-, thulium-, and neodymium-doped fibers with SHG, SFG and DFG processes allows generation of high-power beams with very narrowband optical spectra and wavelengths from below 400 nm to beyond 5 .Math.m.

Some embodiments of the invention relate to methods carried out by an NSS receiver and/or a processing entity capable of receiving data therefrom, for estimating parameters derived from NSS signals and detecting outliers in NSS observables. Input data comprising signals observed by the receiver is received. An estimator is operated, which uses state variables and computes the values thereof based on the input data. An outlier detection procedure comprises: computing a first statistic based on data outputted from the estimator and associated with a set of observables; identifying an observable candidate for removal; computing a second statistic based on the data outputted from the estimator from which the data associated with the identified observable is removed; and determining whether the ratio of the first to the second statistic exceeds a threshold and, if so, removing the identified observable, having the estimator recompute its state variables and performing the outlier detection procedure again.

Some embodiments of the invention relate to methods carried out by an NSS receiver and/or a processing entity capable of receiving data therefrom, for estimating parameters derived from NSS signals and detecting outliers in NSS observables. Input data comprising signals observed by the receiver is received. An estimator is operated, which uses state variables and computes the values thereof based on the input data. An outlier detection procedure comprises: computing a first statistic based on data outputted from the estimator and associated with a set of observables; identifying an observable candidate for removal; computing a second statistic based on the data outputted from the estimator from which the data associated with the identified observable is removed; and determining whether the ratio of the first to the second statistic exceeds a threshold and, if so, removing the identified observable, having the estimator recompute its state variables and performing the outlier detection procedure again.