A receiver authenticates a wideband (WB) signal from global navigation satellites (GNSS) using a narrowband (NB) signal that is also transmitted from the satellites. The NB signal includes segments that are transmitted in time and frequency slots of successive transmission frames. The NB signal is less susceptible to a smart WB jammer. Also, the NB signal segments may also be transmitted at a relative power level with respect to the WB signal, where the relative power level may vary in a known pattern so as to distinguish the WB signal of the satellite from a stronger WB signal from a smart jammer.

A method for processing GPS position signals in a vehicle is proposed, wherein the GPS position signals of the vehicle are received successively at a predefined time interval and are processed in order to control the vehicle, and wherein at least one intermediate position value of the vehicle is determined within the time period formed by the time interval between two successive GPS position signals. Furthermore, a control device is proposed for carrying out the method.

A position measurement system including: an absolute positioning measuring unit configured to measure an absolute position of a mobile object, attach a first time stamp to an absolute position measurement result, and output the absolute position measurement result with the first time stamp; a relative positioning measuring unit configured to measure a relative displacement of the mobile object, attach a second time stamp to a relative displacement measurement result, and output the relative displacement measurement result with the second time stamp; and a positioning computation unit configured to execute positioning computation for calculating the absolute position of the mobile object based on the absolute position measurement result with the first time stamp and the relative displacement measurement result with the second time stamp, attach a third time stamp to a positioning computation result, and output the positioning computation result with the third time stamp.

In one embodiment, a method for calculating a location of an autonomous driving vehicle includes receiving new global navigation satellite system (GNSS) data. The method further includes identifying a first previously estimated location from a plurality of previously estimated locations with a timestamp that is closest to the timestamp of the new GNSS data and identifying a second previously estimated location from the plurality of previously estimated locations with a most recent timestamp. The method further includes calculating a difference between the first previously estimated location and the second previously estimated location, adjusting the new GNSS data based on the difference; and calculating a current estimated location of the ADV based on the adjusted GNSS data.

In one embodiment, a method for calculating a location of an autonomous driving vehicle includes receiving new global navigation satellite system (GNSS) data. The method further includes identifying a first previously estimated location from a plurality of previously estimated locations with a timestamp that is closest to the timestamp of the new GNSS data and identifying a second previously estimated location from the plurality of previously estimated locations with a most recent timestamp. The method further includes calculating a difference between the first previously estimated location and the second previously estimated location, adjusting the new GNSS data based on the difference; and calculating a current estimated location of the ADV based on the adjusted GNSS data.

Techniques provided herein are directed toward virtually extending an updated set of output positions of a mobile device determined by a VIO by combining a current set of VIO output positions with one or more previous sets of VIO output positions in such a way that ensure all outputs positions among the various combined sets of output positions are consistent. The combined sets can be used for accurate position determination of the mobile device. Moreover, the position determination further may be based on GNSS measurements.

Techniques provided herein are directed toward virtually extending an updated set of output positions of a mobile device determined by a VIO by combining a current set of VIO output positions with one or more previous sets of VIO output positions in such a way that ensure all outputs positions among the various combined sets of output positions are consistent. The combined sets can be used for accurate position determination of the mobile device. Moreover, the position determination further may be based on GNSS measurements.

The present technology relates to an information processing apparatus and an information processing method that make it possible to properly report the reliability of information that is available with use of an inertial sensor. The information processing apparatus includes a state estimation section that estimates a state of a predetermined object and an output controller that controls, on the basis of the estimated state of the object, output of reliability information indicating the reliability of object information of the object, the object information of the object being available with use of an inertial sensor. The present technology is applicable to a portable information terminal such as a smartphone or a wearable device, for example.

A method (100) for creating a model for positioning, the method (100) comprising: receiving (S150) a training dataset (1) comprising samples (10) of an estimation labeled type, each sample (10) of the estimation labeled type being associated with a measurement position, each sample (10) of the estimation labeled type comprising sensor data (12), measured at the measurement position associated with the sample (10), the sensor data (12) being characteristic of the measurement position associated with the sample (10); an estimated measurement position (14), being an estimate of the measurement position associated with the sample (10); an estimated accuracy (16), being an estimate of an accuracy of the estimated measurement position (14); training (S160) a machine learning model (20) to convert sensor data (12) to a position, wherein the training is based on the samples (10) of the estimation labeled type in the training dataset (1), whereby the model for positioning is created.

Method, apparatuses, and computer program products for automatically tracking a heading of an object. An example method comprising receiving, one or more internal measurement values which pertain to an object; determining an internal heading uncertainty value for each internal measurement value of the one or more internal measurement values; generating, using a probabilistic heading model, an estimated heading data object for the object based at least in part on the one or more internal measurement values; and providing the estimated heading data object to one or more associated user devices.