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 useful to determine a position, and for estimating an expected accuracy. The method comprises receiving input data comprising NSS signals observed by the NSS receiver and/or information derived from said NSS signals; operating an estimation process, hereinafter referred to as “estimator”, using state variables and computing the values of its state variables based on the received input data; obtaining a combination of residuals from the estimator, each residual being associated with at least one observed NSS signal; and estimating an expected accuracy based on the combination of residuals and/or information derived therefrom. Systems and computer programs are also disclosed. Some embodiments may for example be used for safety-critical applications such as highly automated and autonomous driving.

A method for reduced-outlier satellite positioning includes receiving a set of satellite positioning observations at a receiver; generating a first receiver position estimate; generating a set of posterior observation residual values from the set of satellite positioning observations and the first receiver position estimate; based on the set of posterior observation residual values, identifying a subset of the satellite positioning observations as statistical outliers; and after mitigating an effect of the statistical outliers, generating a second receiver position estimate having higher accuracy than the first receiver position estimate.

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 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.

A non-transitory computer-readable medium and apparatus for restoring navigational performance for a navigational system. The apparatus including a receiver for receiving by a first navigational system and a second navigational system a collection of data points to establish a real-time navigational route for the aircraft, and a computer for comparing navigational performance values or drift ranges. The computer capable of establishing a new navigational route based on the collection of data points.

A kinematic positioning system configured to determine position coordinates of moving bodies by receiving positioning signals from positioning satellites, comprises an on-vehicle device configured to calculate the position coordinates of one of the moving bodies based on carrier wave phases of the positioning signals received from the positioning satellites, and a ground management device configured to transmit correction data used to calculate the position coordinates to the on-vehicle device in response to a request from the on-vehicle device. The on-vehicle device executes a first processing sequence of performing precise point positioning computation by acquiring precise orbit data of each positioning satellite from any of the positioning satellite and the ground management device, and calculating the position coordinates, and a second processing sequence of sending the ground management device a pseudorange concerning a positioning satellite selected from the positioning satellites, a carrier wave, and the position coordinates of the one moving body, performing the precise point positioning computation by acquiring the correction data from the ground management device, and calculating the position coordinates. The on-vehicle device selects the position coordinates having a smaller data error out of the position coordinates calculated in the first processing sequence and the position coordinates calculated in the second processing sequence as the position coordinates of the one moving body.

Systems and techniques are described for detecting one or more timing errors. For example, a system can receive, from a navigation system, navigation timestamp information at a first instance and a second instance. The system can determine a navigation system time difference based on the navigation timestamp information at the first instance and the second instance. The system can further receive, from a wireless device, network timestamp information at the first instance and the second instance. The system can determine a network time difference based on the network timestamp information at the first instance and the second instance. The system can further determine whether time reporting by the navigation system is correct based on the navigation system time difference and the network time difference.

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.

An approach is provided for providing predictive classification of sensor error. The approach involves, for example, receiving sensor data from at least one sensor, the sensor data collected at a geographic location. The approach also involves extracting a set of input features from the sensor data, map data representing the geographic location, or combination thereof. The approach further involves processing the set of input features using a machine learning model to calculate a predicted sensor error of a target sensor operating at the geographic location. The machine learning model, for instance, has been trained on ground truth sensor error data to use the set of input features to calculate the predicted sensor error.

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.