Abstract
Aiming at the limitation of the traditional four-dimensional (4-D) trajectory-prediction model of unmanned aerial vehicles (UAV), a 4-D trajectory combined prediction model based on a genetic algorithm is proposed. Based on historical flight data and the UAV motion equation, the model is weighted dynamically by a genetic algorithm, which can predict UAV trajectory and the time of entering the protection zone instantly and accurately. Then, according to the number of areas where the tangent line of the current trajectory point intersects with the collision area, alarm area, alert area, and the time of entering the protection zone, the UAV’s behavior intention can be estimated. The simulation experiments verify the dangerous behaviors of UAV under different danger levels, which provides reference for the subsequent maneuvering strategies.
Highlights
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; State Key Laboratory of Air Traffic Management System and Technology, Nanjing 210007, China; Abstract: Aiming at the limitation of the traditional four-dimensional (4-D) trajectory-prediction model of unmanned aerial vehicles (UAV), a 4-D trajectory combined prediction model based on a genetic algorithm is proposed
Support vector machine regression (SVR) is a regression algorithm derived from the support vector machine (SVM) classification algorithm, which is suitable for small samples
The data of UAV obtained at ti moment by transformation are X, Y, Z, Vl, Vv, β(ti ), θ, and T, where X, Y take the ADS-B receiving end as coordinates center, the geomagnetic north pole and its vertical eastward direction are the values of the X and Y axes, respectively, Z is the height, Vl
Summary
Support vector machine regression (SVR) is a regression algorithm derived from the support vector machine (SVM) classification algorithm, which is suitable for small samples. The basic idea is to find an optimal hyperplane so that the distance between all sample points and the hyperplane is minimal. The total deviation between all sample points and the hyperplane is minimal [10,11,12]. Suppose there is a set of sample data, ( x1 , y1 ), · · · , ( xn , yn ). When the regression problem is nonlinear, a kernel function should be introduced to map the nonlinear sample data into a high-dimensional space to linearize it so that linear regression can be carried out. After mapping through the kernel function, the sample data are linear in the high-dimensional space, so there must be a linear regression function. Where C is the penalty factor and C > 0, and ξ i , ξei is slack factor to avoid the trained fitting, and ε is the upper limit of allowable error
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