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Toll object detection in a GNSS system using particle filter

  • US 9,886,849 B2
  • Filed: 06/01/2015
  • Issued: 02/06/2018
  • Est. Priority Date: 06/03/2014
  • Status: Active Grant
First Claim
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1. A method for assessing passages by a vehicle through a tolling object utilizing a global navigation satellite system (GNSS) comprising an on-board unit (OBU) in every vehicle to be surveyed by the system, said OBU receiving signals from satellites to consistently and frequently estimate position coordinates for the vehicles, comprising the steps of:

  • (A) obtaining an initial vehicle position including a degree of uncertainty (50),(B) using computer hardware and software to assign (51) a predetermined number of particles for each vehicle, in a meaning understood by the Sequential Monte Carlo mathematical method, comprising a process model, a measurement model and a probability distribution,(C) using computer hardware and software to assign to each particle;

    (i) a common initial probability, and(ii) an initial state comprising at least three dimensional spatial position,(D) using computer hardware and software to define epochs in time within each of which the following procedure is conducted;

    (i) in a prediction step (52), using said process model to predict with uncertainty the state of each particle in the next epoch, generally represented as xt

    t(xt-1)+η

    t, wherein xt is a state vector, φ

    t(xt-1) is a function used to predict the state xt in one epoch from information of the state in the previous epoch, and η

    t is a process noise term, the predictions of all particles within each epoch thereby representing the probability distribution of the state vector xt given all previous measurements zt,(ii) in an updating step (53), using computer hardware and software to update the probability of the particles according to how well each particle'"'"'s state from the prediction step agrees with GNSS pseudo range measurements, according to the equation zt=ht(xt)+ε

    t, wherein zt is a measurement vector, ht(xt) is a possibly time-varying function of the state, and ε

    t is measurement noise, thereby updating the probability distribution,(iii) using computer hardware and software to assess passage or non-passage (54) by mathematically comparing the spatial definition of the tolling objects in question with the updated particle information from the prediction step (52) while applying a defined confidence level,(iv) recursively repeating steps (D)(i) through (D)(iii) at a predetermined rate.

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