Location determination using dual statistical filters

摘要

Methods, systems, and computer program product for location determination using dual statistical filters are described. A mobile device can determine a location of the mobile device using a particle filter and a Kalman filter. The particle filter can filter candidate locations of the mobile device using measurements of environment variables in the venue. The Kalman filter can filter inputs from a sensor of the mobile device for measuring angular movement of the mobile device. The particle filter and the Kalman filter can be linked by heading of the mobile device. Output of the Kalman filter can be used to determine where to place particles, or candidate locations, in a next iteration of the particle filter. Output from the particle filter can be used to determine a center mode of the Kalman filter and to determine a bias of the sensor for measuring angular movement.

主权项

1. A method of iteratively estimating a location of a mobile device at a venue, comprising:
from an initial estimated location of the mobile device at the venue at an initial time in a first iteration, obtaining a set of first candidate locations of the mobile device at a first time that is after the initial time using a statistical model of a first Bayesian filter, each first candidate location being represented using location coordinates and being associated with a respective heading, the heading being inferred from a direction from the initial estimated location to a respective candidate position; from readings of a first sensor of the mobile device that measures an angular value, determining a statistical distribution of the angular value as a function of the heading of the mobile device using a statistical model of a second Bayesian filter; determining a set of second candidate locations of the mobile device at a second time that is after the first time, wherein each second candidate location is determined using a first candidate location and a respective heading that has been adjusted according to the statistical distribution of the angular value; weighting each second candidate location by matching readings of a second sensor of the mobile device against location fingerprint data that specifies expected readings of the second sensor at various locations of the venue, including giving higher weight to a second candidate location that has a higher degree of match; and determining a most likely location of the mobile device at the venue using the weighted second candidate locations and designating the most likely location as an estimated location of the mobile device at the second time.