US 10,023,323 B1Granted

Estimating wind from an airborne vehicle

AssigneeX Development LLC

InventorsRichard Joseeph William Roberts, Joshua John Bialkowski, Justin Sadowski, John Roberts

Life of this patent

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Abstract

Embodiments are described for determining wind by an airborne aerial vehicle without reliance on direct measurements of airspeed by the vehicle. Instead, wind may be computationally estimated using disclosed techniques for utilizing measurements of only ground-speed and heading, or only measurements of forces experienced by the airborne aerial vehicle during flight. In one technique, samples of ground-speed measurements and corresponding heading measurements of an airborne vehicle are used in a mathematical optimization of a wind-driven hypothesis of deviations between the two types of measurement at each of multiple sampling times. In another technique, an aerodynamic model of an aerial vehicle can be used to adjust parameters of a wind hypothesis in order to achieve a best-fit between predicted and measured forces on the aerial vehicle during flight.

Claims12

Dependent claims

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, and
computationally 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.

9 more elements

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 observed acceleration vectors, wherein the first time series has a set of time steps,
computing a second time series of predicted acceleration vectors based on a wind hypothesis applied to an analytical aerodynamic model of the UAV, wherein the second time series has the same set of time steps as the first time series, and
computationally estimating a wind vector with respect to a ground-based coordinate system by iteratively adjusting parameters of the wind hypothesis applied to the analytical aerodynamic model of the UAV so as to achieve optimal agreement between the predicted acceleration vectors and the observed acceleration vectors according to a least-squares minimization of deviations between the predicted acceleration vectors and the observed acceleration vectors.

5 more elements

Dependencies are inferred from the claim text.