One or more computer processors encode a plurality of time sequenced global position system (GPS) datapoints onto a grided dimensional area; determine a general trajectory between each time sequenced GPS datapoint in the plurality of encoded time sequenced GPS datapoints and a subsequent encoded time sequenced GPS datapoint; cluster the encoded time sequenced GPS datapoints based on a respective determined trajectory with a plurality of encoded historical GPS datapoints; calculate an azimuth for each encoded time sequenced GPS datapoint in the plurality of time sequenced GPS datapoints utilizing a plurality of adjacent historical GPS datapoints contained within a respective cluster; generate a plurality of interpolated GPS datapoints utilizing calculated azimuths, determined general trajectories, and historical GPS datapoints; and aggregate the generated interpolated GPS datapoints with the plurality of time sequenced GPS datapoints into an interpolated route, wherein each GPS datapoint in the interpolated route is within respective azimuth thresholds.

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.

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.

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.

An aircraft includes at least one sensor, an altitude actuator, a memory device, and an electronic controller. The at least one sensor is configured to detect altitude of the aircraft, current position of the aircraft and speed of the aircraft. The altitude actuator is configured to change the altitude of the aircraft. The memory device is configured to store predetermined terrain data of an area. The electronic controller is configured to estimate a future position of the aircraft based on a detected current position of the aircraft and a detected speed of the aircraft. The electronic controller is further configured to control the altitude actuator based on the future position, a detected altitude of the aircraft and the predetermined terrain data.

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.

A probabilistic system for tracking a state of a vehicle using unsynchronized cooperation of information includes a probabilistic multi-head measurement model relating incoming measurements with the state of the vehicle. The first head of the model relates measurements of the satellite signals subject to measurement noise with a belief on the state of the vehicle, and a second head relates an estimation of the state of the vehicle subject to estimation noise with the belief on the state of the vehicle. A probabilistic filter of the system updates recursively the belief on the state of the vehicle based on the multi-head measurement model accepting one or a combination of the measurements of the satellite signals subject to the measurement noise and the estimation of the state of the vehicle subject to the estimation noise.

A navigation method applied to an intelligent vehicle includes obtaining a navigation request, where the navigation request includes location information of a start place and location information of a destination that are of a vehicle; and navigating the vehicle based on the location information of the start place, the location information of the destination, and a heading of the vehicle, where the heading of the vehicle is determined based on a traveling track stored before the navigation request is obtained.

Aspects of the present disclosure relate to identifying points of interest by generating and storing virtual geofence information that is captured around a physical structure based in part on global positioning system (GPS) data from a plurality of devices that is then processed to identify GPS trajectory and kernel density estimation. Specifically, the techniques include receiving, at the network-based control computer, GPS data from a plurality of devices and grouping the GPS data from the plurality of devices to generate GPS trajectory information for each group of the plurality of devices. Based on the GPS trajectory information, the network-based control computer may calculate kernel density estimation and determine an isoline on a virtual map for the each group of the plurality of devices. By overlaying the isoline data on a geographic coordinate information of a physical structure, the network-based control computer may generate a virtual geofence around the physical structure and store, in a memory, geofence information for the facility.

A method of navigating with a global navigation satellite system (GNSS) includes receiving a GNSS signal, calculating a GNSS navigation solution according to the GNSS identifying map information corresponding to the GNSS navigation solution, detecting features from the identified map information, and correcting a GNSS navigation based on the features detected from the map information and the GNSS signal.