A method for detecting a presence of interference during global navigation satellite system (GNSS)-based and inertial sensor signals (INS)-based localization of a vehicle includes determining localization results using a first filter configured to read in GNSS data and INS data, and storing a plurality of the determined localization results. The plurality of the determined localization results are after one another in terms of time and are each determined using the first filter. The method further includes analyzing the stored plurality of localization results using a second filter which differs from the first filter.

The receiver captures an electromagnetic transmission carrying a bauded signal, such as a transmission from an orbiting satellite, and processes it for Doppler shift analysis. The electromagnetic transmission is captured and a non-linear operation is performed to expose a cyclostationary feature of the captured transmission that will define a rate-line. This rate-line will exist at a frequency that is related to the bauded signal and Doppler shift relative to the motion of the transmitter to the receiver. The rate-line frequency is tracked in time to generate data indicative of a Doppler shift associated with the satellite and processed by an estimator fed by satellite propagator to supply positioning, navigation and timing services at the receiver output.

A satellite radio wave receiving device including: one or more processors configured to: cause a receiver to start a receiving operation of receiving radio waves from positioning satellites; perform a current position calculation to calculate a current position based on the radio waves received; calculate a positioning accuracy of the current position; decide whether or not to adopt the current position based on a number of positioning satellites from which the receiver has received radio waves and the positioning accuracy; in response to deciding to adopt the current position, cause the receiver to stop the receiving operation; and in response to deciding to not adopt the current position, cause the receiver to continue the receiving operation of receiving radio waves from the positioning satellites and repeat performance of the current position calculation to calculate current positions based on the radio waves received during the continued receiving operation.

Methods and systems are provided for navigating a mobile platform in an environment. A processor obtains information about an object in the environment, obtains information about a first satellite, and estimates a probability indicator for a non-line of sight signal transmission between a current satellite location of the first satellite and a current location of the mobile platform using the information about the first satellite and the information about the object. The processor further determines a discrepancy indicator using a movement information of the mobile platform and a movement information of the first satellite such that a weighting indicator can be determined using the estimated probability indicator and the determined discrepancy indicator. The processor then assigns a weighting indicator to a satellite signal transmitted from the first satellite in order to provide a first weighted signal for navigating the mobile platform.

Aspects of the disclosure relate to computing platforms that utilize improved machine learning techniques for dynamic device quality evaluation. A computing platform may receive driving data from a mobile device. Using the driving data, the computing platform may compute a plurality of driving metrics, which may include: a geopoint expectation rate score, a trips per day rank score, a consecutive geopoint time difference score, a global positioning system (GPS) accuracy rating score, and a distance between consecutive trips score. By applying a machine learning model to the plurality of driving metrics, the computing platform may compute a device evaluation score, indicating a quality of the driving data received from the mobile device. Based on the device evaluation score, the computing platform may set flags, which may be accessible by a driver score generation platform, causing the driver score generation platform to perform an action with regard to the mobile device.

A solution for providing user equipment (UE) location information during an emergency call (e.g., an E911 call) includes: detecting an emergency call originating from the UE; determining a location of the UE; based on at least detecting the emergency call originating from the UE, transmitting the location of the UE across a cellular network to an emergency monitoring node (e.g., a public safety answering points (PSAP) and/or a gateway mobile location center (GMLC)); and based on at least detecting the emergency call originating from the UE, transmitting the location of the UE across a packet data network (e.g., the internet, using a data plan) to the emergency monitoring node. This provides an alternate path for the location information, and some examples use a larger set of location information sources. In some examples, during the emergency call, based on available battery power, the UE location information may be updated.

A positioned location adjustment method and apparatus. The method includes: a first vehicle sends a request message to a plurality of reference vehicles, where the request message includes current location information of the first vehicle; the first vehicle receives a response message from the reference vehicle, where the response message includes positioned location information of the reference vehicle, a positioning error value of the reference vehicle, and vehicle identifier information of the reference vehicle; the first vehicle determines a second vehicle from the plurality of reference vehicles based on the positioning error value of the reference vehicle; and the first vehicle adjusts the first positioned location information based on positioned location information of the second vehicle and vehicle identifier information of the second vehicle, to obtain second positioned location information. According to the embodiments, positioning precision and accuracy can be improved.

In one aspect of the present disclosure, an apparatus for locating a mobile railway asset is provided that includes a power source, GNSS circuitry configured to utilize electrical power from the power source to receive GNSS data, and a controller operatively coupled to the power source and the GNSS circuitry. The controller has a power saving mode wherein the controller inhibits the GNSS circuitry from receiving GNSS data and a standard accuracy mode wherein the controller permits the GNSS circuitry to receive GNSS data for a first time period. The controller has a higher accuracy mode wherein the controller permits the GNSS circuitry to receive GNSS data for a second time period longer than the first time period, and subsequently across multiple instances, in order to collect more GNSS data that can be qualified, filtered, sorted, and averaged to produce a more accurate result.

An automatic vehicle positioning management system includes an on-vehicle apparatus and a portable device. The on-vehicle apparatus, installed on a vehicle, acquires a first location of the vehicle through wireless positioning. The first location is sent to the portable device which acquires a second location of the vehicle through GPS. When multiple vehicles form a fleet, each vehicle respectively sends its first and second locations to a server through its portable device. The second location of each vehicle is corrected by operations of point error analysis, image overlay and point error correction, so that the fleet can be managed more precisely.

Aspects concern a method for correcting position estimates of a movable object. According to various embodiments, the method comprises establishing (1001) a hidden Markov model, HMM, instance for a movable object and, for positioning times of a sequence of positioning times, receiving (1002) a position estimate from a positioning device of the movable object for a respective positioning time, determining (1003) a set of candidate path segments for the positioning time, determining (1004) likelihoods for the candidate path segments to correspond to the position estimate by application of the Viterbi algorithm to the HMM instance, expanding (1005) the HMM instance by the determined likelihoods for the candidate path segments for the positioning time and determining (1006) a corrected position estimate from a candidate path segment of the set of candidate path segments with the highest likelihood.