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Estimating wind from an airborne vehicle

  • US 10,023,323 B1
  • Filed: 04/29/2015
  • Issued: 07/17/2018
  • Est. Priority Date: 04/29/2015
  • Status: Active Grant
First Claim
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1. An unmanned aerial vehicle (UAV) comprising:

  • a network interface;

    one or more sensors;

    one or more processors; and

    a computer-readable medium having stored therein instructions that are executable by the one or more processors to cause the UAV to carry out operations including;

    while flying along a flight path, determining from onboard sensor measurements a first time series of ground-speed vectors with respect to a ground-based coordinate system, wherein the first time series has a set of time steps,while flying along the flight path, determining from onboard sensor measurements a second time series of flight-heading vectors with respect to the ground-based coordinate system, wherein the second time series has the same set of time steps as the first time series, andcomputationally estimating a wind vector with respect to the ground-based coordinate system by optimizing an analytical model of wind-driven deviations between the ground-speed vectors and the flight-heading vectors at like time steps, wherein the optimization is carried out over all data elements of the first and second time series,wherein each ground-speed vector comprises three orthogonal components of the airborne UAV'"'"'s velocity vector at each corresponding time step with respect to the ground-based coordinate system,wherein each flight-heading is a unit vector comprising three orthogonal components of the airborne UAV'"'"'s forward-pointing direction at each corresponding time step with respect to the ground-based coordinate system,wherein the analytical model of wind-driven deviations between the ground-speed vectors and the flight-heading vectors at like time steps comprises a vector deviation at each respective time step between the ground-speed vector at the respective time step and a sum of (i) a scale factor multiplied by the flight-heading vector at the respective time step plus (ii) a wind hypothesis,and wherein optimizing the analytical model comprises determining a wind hypothesis that minimizes a sum of the vector deviation over all the time steps.

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