In some implementations, a processor may retrieve predicted positioning data and predicted orbital data from global navigation satellite service (GNSS) positioning circuitry of a wireless device in response to a request for device time. The processor may retrieve long-term learning (LTL) data for a temperature sensing crystal (TSX) of the wireless device, the LTL data including S-curve characteristics of the TSX, and the time tracking uncertainty of the TSX. The processor may generate a GNSS-based device time estimate using the predicted positioning data and the predicted orbital data. The processor may perform TSX-based device time processing by updating the GNSS-based device time estimate using a clock signal of the TSX to generate a final device time estimate, the updating based on the retrieved LTL data for the TSX.

A vehicle positioning method includes obtaining satellite filtering parameters and satellite data, the satellite data comprising at least one of (i) a pseudo range observation value or (ii) a Doppler observation value indicating a Doppler effect. The method further includes determining a first parameter correction amount corresponding to the vehicle at a first time point to obtain positioning information of the vehicle at the first time point. The method further includes determining a second parameter correction amount corresponding to the vehicle at the second time point according to a constraint matrix corresponding to the motion state of the vehicle, and obtaining positioning information of the vehicle at the second time point by modifying the positioning information at the first time point using the second parameter correction amount.

A method for estimating the pressure measurement bias of a barometric sensor in a wireless terminal. A location engine using the method generates an enhanced estimate of the measurement bias. The location engine generates the enhanced estimate based in part on relatively coarse estimates of the elevation of the wireless terminal. The coarse estimates are used to generate instantaneous estimates of measurement bias and bias uncertainty. As needed, the location engine adjusts the instantaneous estimate of bias uncertainty, in order to reflect an instantaneous estimate of measurement bias that is recognized as being in error. The adjustment is based on what is expected as a probable measurement bias value for the particular wireless terminal. Once the location engine generates the enhanced estimate of measurement bias, it can generate improved estimates of elevation of the wireless terminal.

A Global Navigation Satellite System (GNSS) receiver for performing GNSS outlier detection and rejection is provided. When the GNSS receiver receives GNSS signals from satellites in the GNSS, the GNSS receiver processes the GNSS signals to perform positioning. Then, the GNSS receiver sequentially performs a Doppler-pseudorange comparison, a Random Sampling Consensus (RANSAC) check for selected subsets of the satellites, and a history-based check for the satellites to determine a status of each satellites as an outlier or an inlier. Specifically, in the RANSAC check, the subsets of the satellites are selected using results of the Doppler-pseudorange comparison as inputs to filter the satellites, thus reducing the number of subsets needed for computation in the RANSAC check. The status of the satellites are recorded for the history-based check, which further exploits the correlations of outliers across time.

An apparatus and a method for performing positioning using a Global Navigation Satellite System (GNSS) with a state machine based localization engine are provided. When the apparatus receives GNSS signals, the apparatus provides the localization engine to process the GNSS signals, and determines, based on a GNSS status and a position-velocity-time (PVT) status, a state of the localization engine. Specifically, the state of the localization engine is switchable between at least 3 states, including a dead reckoning state, a tightly coupling state, and a loosely coupling state. Once the state is determined, the localization engine may determine a local accuracy status based on the state of the localization engine. Thus, a downstream module on the apparatus may use the local accuracy status to perform a corresponding downstream action.

A wearable training computer includes a global navigation satellite system (GNSS) antenna arrangement configured to provide a group of antenna configurations for receiving a GNSS signal, wherein each antenna configuration provides different radio frequency properties. The wearable training computer further includes a measurement circuitry configured to measure performance of the GNSS antenna and a processing circuitry configured to select, based on at least an activity type of a user of the wearable training computer, a subset of the antenna configurations from the group of the antenna configurations, and further configured to select, from the subset of the antenna configurations based on the measured GNSS antenna performance, an antenna configuration for receiving the GNSS signal.

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.

A position measuring apparatus for positioning a mobile object includes a positioning control unit that determines a candidate area type of a position of the mobile object in accordance with an attribute of the mobile object and geospatial information, divides a candidate area corresponding to the candidate area type into a plurality of grids, specifies a grid in which the mobile object is estimated to be located from among the plurality of grids, and outputs a positioning solution of a carrier-phase based positioning calculation by an absolute position positioning unit, the positioning solution being obtained by using the grid specified.

A method of determining a posterior error probability distribution for a parameter measured by a Global Navigation Satellite System (GNSS) receiver. The method comprises receiving a value for each of one or more GNSS measurement quality indicators associated with the GNSS measurement of the parameter. The or each received measurement quality indicator value is provided as an input into a multivariate probability distribution model to determine the posterior error probability distribution for the GNSS measurement, wherein the variates of the multivariate probability distribution model comprise error for said parameter, and the or each measurement quality indicator.

A method of determining a posterior error probability distribution for a parameter measured by a Global Navigation Satellite System (GNSS) receiver. The method comprises receiving a value for each of one or more GNSS measurement quality indicators associated with the GNSS measurement of the parameter. The or each received measurement quality indicator value is provided as an input into a multivariate probability distribution model to determine the posterior error probability distribution for the GNSS measurement, wherein the variates of the multivariate probability distribution model comprise error for said parameter, and the or each measurement quality indicator.