A method comprises receiving an approximate location of a rover platform based on satellite signals for a Global Navigation Satellite System (GNSS), and receiving for the GNSS a differential correction map (DCM) representing a non-planar surface of differential corrections that varies across a geographical area represented by the DCM. The differential corrections are based on a reference station constellation of GNSS reference stations having respective locations spanning the geographical area. The method further comprises deriving DCM-based differential corrections for the satellite signals at the approximate location based on the DCM, correcting the satellite signals using the DCM-based differential corrections, and determining a location of the rover platform using the corrected satellite signals.

An interface device that enables a GNSS-based precision approach through the Ground Base Augmentation System (GBAS) function known as the GNSS Landing System (GLS) and/or through Satellite Based Augmentation Systems (SBAS) based Localizer Performance with Vertical Guidance (LPV). The GLS interface device allows a GLS-capable multi-mode receiver to be used on a non-GLS-capable airplane without extensive changes to other airplane systems. The GLS interface device works by intercepting information to and from the multi-mode receiver and modifying the information to make the interface compatible with an airplane that uses ILS guidance. Similarly, the information modifications will make the airplane appear to the multi-mode receiver as if it were a GLS-capable airplane.

The system and method facilitates Real-Time-Kinematic (RTK) GNSS with long baseline between a rover receiver and a base station receiver, even in the presence of scintillation or ionospheric disturbances that spatially fluctuate. Residual atmospheric errors can be estimated by a dual error model in a filter to promote efficient fixing or resolution of carrier phase ambiguities.

A system includes Referential Global Positioning System (RGPS) base stations, servers and mobile devices. Each RGPS station includes: (i) global navigation satellite system (GNSS) receivers and antennas for receiving GNSS geolocation data, Cellular Location Technology (CLT) geolocation data, Wi-Fi Positioning System (WPS) geolocation data; (ii) a data processor for positioning error correction, and signal processing; and (iii) a transmitter for transmitting the error corrections and processed GPS data to the servers. The servers (i) receive, aggregate, and store, the GNSS, CLT, and Wi-Fi error corrections and processed GPS data, and (ii) transmit the location-specific GPS data to any mobile device in proximity. Each mobile device calculates a calculate a highly accurate location of the mobile device by obtaining and combining the location-specific GPS data from a RGPS server, and device-specific geolocation data from a GNSS sensor, a CLT sensor and a WPS sensor, on the mobile device.

A first positioning processing unit executes independent positioning for calculating the position of a work vehicle on the basis of a satellite signal received from a satellite. A transmission processing unit transmits point positioning information to a base station server that selects one base station. An acquisition processing unit acquires correction information associated with the one base station from the base station server. A second positioning processing unit executes RTK positioning for calculating the current position of the work vehicle on the basis of the correction information. When the RTK positioning for the work vehicle based on first correction information associated with a first base station becomes possible, the transmission processing unit transmits, to the base station server, the point positioning information immediately before the RTK positioning becomes possible.

Techniques are provided for applying plate tectonic model information to improve the accuracy of base station assisted satellite navigation systems. An example method for determining a location of a mobile device includes receiving base station measurement, coordinate and epoch information, receiving base station velocity information, receiving signals from a plurality of satellite vehicles, and determining the location of the mobile device based on the signals received from the plurality of satellite vehicles, the base station measurement, coordinate and epoch information, and the station velocity information.

One or more computing devices, systems, and/or methods for calibrating barometric sensors and/or determining altitudes of devices are provided. In an example, one or more barometric pressure measures are determined using a barometric sensor of a device. One or more locations of the device are determined based upon one or more global navigation satellite system (GNSS) signals and one or more corrective signals associated with the one or more GNSS signals. One or more reference values are determined based upon the one or more locations. A barometric offset is determined based upon the one or more barometric pressure measures and the one or more reference values. A first barometric measurement is performed using the barometric sensor to determine a first barometric pressure measure. An adjusted barometric pressure measure and/or an altitude of the device are determined based upon the first barometric pressure measure and the barometric offset.

A method for creating a correction function for improving the accuracy of a GPS device collects multiple time samples at multiple known locations wherein each time sample consists of GPS coordinates and associated satellite data from multiple satellites. The satellite data includes or permits determination of (i) satellite azimuth and elevation of an associated satellite, (ii) Signal-to-Noise Ratio of a received signal from the associated satellite, and optionally (iii) pseudo-range. For each time sample a respective error between the known location and the corresponding GPS coordinates is computed and an error correction function is created as a function of the respective GPS coordinates and the satellite data by applying deep learning/machine learning techniques to the multiple time samples.

A system for liquid level monitoring is provided. The system may include one or more rovers configured for placement on a surface of a body of liquid, a base configured for fixed placement on land, and one or more processors configured to determine one or more liquid levels of the body of liquid. The system may also include a remote server communicatively coupled to one or more components of the system via a network. The system may be further configured to display data associated with the one or more liquid levels.

A first pair of a wide-lane (WL), zero-difference (ZD) bias filter and corresponding supplemental WL bias predictive filter determines the time-variant wide-lane bias for a corresponding satellite based on adaptive estimation responsive to tuned dynamic noise provided by the supplemental wide-lane bias predictive filter for each satellite. A second pair of narrow-lane (NL), zero-difference (ZD) bias filter and corresponding NL bias filter/code-phase bias filter determines the time-variant narrow-lane bias (e.g., refraction corrected (NL) code-phase bias) for a corresponding satellite based on adaptive estimation on adaptive estimation responsive to tuned dynamic noise within a narrow-lane bias/code-phase bias filter for each satellite. The electronic data processor of a data processing center is configured to provide a correction signal comprising the wide-lane ambiguities, the time-variant wide-lane bias and the narrow-lane ambiguities and the time-variant narrow lane bias.