Existing networks of precisely surveyed GNSS receivers that are used for dGNSS or RTK positioning techniques can be used to measure GNSS time across a territory or region such as a country. The measured GNSS time base signals from each receiver are then fed back to a collating service, similar to the existing dGNSS/RTK systems, which also receives an accurate time base signal from a trusted third-party time base supplier which maintains a trusted time base. The collating service then compares the GNSS time signals from the network of GNSS receivers with the trusted time base and determines whether the GNSS time signals are accurate when compared to the trusted time base, and if they are not accurate, calculates the error. The collating service may provide the calculated error to users and the necessary correction that needs to be applied to measured GNSS time to obtain accurate UTC time.

A positioning method includes: acquiring the credibility of each positioning subsystem in different states and generating a credibility data table; acquiring the real-time credibility from the corresponding credibility data table according to real-time positioning data of each positioning subsystem; calculating a first information distribution weight coefficient of each positioning subsystem involving a fusion operation of an filter according to the real-time credibility of each positioning subsystem; respectively feeding back, by a main filter, a second information distribution weight coefficient of each positioning subsystem involving the fusion operation to each sub-filter according to global data; determining a final information distribution weight coefficient of each positioning subsystem involving the fusion operation according to the first information distribution weight coefficient and the second information distribution weight coefficient; and performing, by the filter, the fusion operation according to the final information distribution weight coefficient of each positioning subsystem and outputting a final positioning result.

A first bias conversion unit converts, based on a first frequency and a second frequency, a signal bias related to carrier phase for correcting a carrier phase contained in a first ranging signal having the first frequency, to a signal bias related to carrier phase for correcting a carrier phase contained in a second ranging signal having the second frequency. A first correction unit corrects the carrier phase using the converted signal bias. A second bias conversion unit converts the signal bias related to pseudorange to the signal bias related to pseudorange by making reference to a conversion table indicating values for use in conversion of the signal bias related to pseudorange to the signal bias related to pseudorange. A second correction unit corrects a pseudorange using the converted signal bias.

A positioning device measures a position of a vehicle by including a controller. The controller provides (i) a first positioning system to obtain a first positioning result having a first accuracy by performing positioning using a signal from a GNSS satellite and (ii) a second positioning system to obtain a second positioning result having a second accuracy higher than the first accuracy, by using acquired vehicle-related information, instead of or in addition to the first positioning result. The controller selects, as a selected positioning system to obtain a selected positioning result, either (i) the first positioning system or (ii) the second positioning system. In response to determining that the second accuracy of the second positioning result is lower than the first accuracy of the first positioning result, the controller is configured to switch the selected positioning system to select the first positioning system.

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.

The present invention discloses a method for real and virtual combined positioning, it not only sends positioning information to the server through the electronic device for tracking the positioning of the electronic device, but also further captures external scene image and scene sound through the electronic device, or the server generates corresponding scene image and scene sound based on the positioning information, further used to confirm the positioning of electronic device.

Method, systems, and computer-readable media containing instructions which, when executed by a computing device, cause it to receive data from an inertial measurement unit, including GPS data, velocity data, and bearing data, receive data from a digital magnetic compass, including bearing data, receive data from a Doppler sensor, including velocity data and distance data, determining whether GPS location data is in consensus with a previous derived multi-source reckoning system location, determining a consensus distance value from a weighted average of data from the inertial measurement unit and the Doppler sensor, determine a consensus heading value from a weighted average of data from the inertial measurement unit and the digital magnetic compass, determine a consensus geolocation value from a weighted average of data from the inertial measurement unit and the previous derived multi-source reckoning system location, and determine a derived multi-source reckoning system location.

A wireless device includes a satellite receiver to receive data from multiple satellites. The wireless device also includes processing circuitry and memory. The memory stores one or more neural network models. The processing circuitry is operative to identify a neural network model that has been trained to adapt to a region in which the wireless device operates, classify satellite raw measurements from each satellite at a given time into a corresponding quality level using the neural network model, and identify satellites raw measurements with a quality level higher than a threshold. The location of the wireless device is calculated using the identified satellite raw measurements.

Techniques are provided for utilizing a mobile device to estimate ionospheric delays in GNSS transmissions. An example method of determining a position of a mobile device includes obtaining a pseudorange measurements and carrier-phase measurements for a satellite at a first frequency band and a second frequency band, determining a bias estimate for the satellite based on a plurality of pseudorange measurements and carrier-phase measurements, determining a delta carrier-phase measurement for the satellite based on the carrier-phase measurements at the first frequency band and the second frequency band, and determining the position of the mobile device based at least in part on the delta carrier-phase measurement, and the pseudorange measurements, the carrier-phase measurements, or both.

A computer architecture for geolocation spoofing/meaconing detection is disclosed. According to some aspects, a computer accesses an incoming geolocation positioning signal. The computer determines, using a signal characteristics calculation subsystem, geolocation positioning signal characteristics for the incoming geolocation positioning signal. The computer provides, using a geolocation positioning spoofing/meaconing detection subsystem, the geolocation positioning signal characteristics as an input vector to a neural network, wherein the neural network determines whether the incoming geolocation positioning signal is legitimate or fake. If the incoming geolocation positioning signal is determined to be fake: the computer computes, using a Bayesian inference subsystem, a likelihood and a severity of a geolocation positioning technology based attack. The computer provides, as a digital transmission, an indication of whether the incoming geolocation positioning signal is legitimate or fake.