A positioning system and a calibration method of an objection location are provided. The calibration method includes the following. Roadside location information of a roadside unit (RSU) is obtained. Object location information of one or more objects is obtained. The object location information is based on a satellite positioning system. An image identification result of the object or the RSU is determined according to images of one or more image capturing devices. The object location information of the object is calibrated according to the roadside location information and the image identification result. Accordingly, the accuracy of the location estimation may be improved.

An example operation includes one or more of determining a distance traveled by a transport using data points from a sensor, determining an error exists with one or more of the data points, correcting the error by the sensor, discarding one or more of the data points when the error is above a threshold or when the error cannot be corrected and determining a minimum reasonable path of the transport using a remaining portion of the data points.

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.

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.

Method for improving global positioning performance of a first road vehicle (10), the method comprising, by means of a data server (3, 4, 4″): acquiring data from onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g) arranged on the first road vehicle (10) and on at least two neighbouring road vehicles (10′, 10″, 10′″), the data comprising data on relative positions and data on heading angle and velocity of the road vehicles (10, 10′, 10″, 10′″), and acquiring global positioning data of at least two of the road vehicles (10, 10′, 10″, 10′″), processing (102) data comprising the global positioning data, the data, with corresponding timestamp, acquired from the onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g), and a motion model for each of the first road vehicle (10) and the at least two neighbouring road vehicles (10′, 10″, 10′″) using a data fusion algorithm, calculating adjusted global positioning data for the first road vehicle (10) and communicating (104) the adjusted global positioning data to a positioning system (6) of the first road vehicle (10).

The present disclosure describes a system for dynamically determining an accurate location of a light electric vehicle. For example, if a light electric vehicle is within a predetermined distance of a location for which an accurate location determination is needed or required, a light electric vehicle management system may update the determined location of the light electric vehicle with a location correction factor that is based, at least in part, on a reference location provided by a stationary reference point.

The present disclosure describes a system for dynamically determining an accurate location of a light electric vehicle. For example, if a light electric vehicle is within a predetermined distance of a location for which an accurate location determination is needed or required, a light electric vehicle management system may update the determined location of the light electric vehicle with a location correction factor that is based, at least in part, on a reference location provided by a stationary reference point.

A PVT calculation device includes a memory; and one or more processors in communication with the memory configured to perform operations including: receiving observations and ephemerides from satellites to obtain PVT data of the satellites and predicted PVT results of the receiver; setting up observation functions respectively corresponding to the satellites; calculating by a least square solution first estimated PVT results of the receiver based on the observation functions; iteratively eliminating by a Random-Sampling Iterative Kalman Filter (RSIKF) algorithm fault observation functions from the observation functions in an inner cluster until no fault observation functions detected in the inner cluster; calculating by the RSIKF algorithm a second estimated PVT results of the receiver using the observation functions in the inner cluster; and outputting final estimated PVT results of the receiver. The PVT calculation device may calculate the PVT results of the receiver with improved accuracy and stability.

A PVT calculation device includes a memory; and one or more processors in communication with the memory configured to perform operations including: receiving observations and ephemerides from satellites to obtain PVT data of the satellites and predicted PVT results of the receiver; setting up observation functions respectively corresponding to the satellites; calculating by a least square solution first estimated PVT results of the receiver based on the observation functions; iteratively eliminating by a Random-Sampling Iterative Kalman Filter (RSIKF) algorithm fault observation functions from the observation functions in an inner cluster until no fault observation functions detected in the inner cluster; calculating by the RSIKF algorithm a second estimated PVT results of the receiver using the observation functions in the inner cluster; and outputting final estimated PVT results of the receiver. The PVT calculation device may calculate the PVT results of the receiver with improved accuracy and stability.