Aircraft 3D Trajectory Estimation with a Single Nonlinear Filter Using Two 2D Radars

We consider 3D trajectory estimation of an aircraft using two air traffic control 2D radars with Cartesian state vector. The assumed motion of the aircraft is nearly constant velocity with nearly constant altitude. We use the cubature Kalman filter (CKF) for the nonlinear filtering problem and present three filter initialization algorithms. Using Monte Carlo simulations, we compare results from our proposed algorithms with those from existing height-parametrized unscented Kalman filter and bias-compensated pseudolinear estimator based CKF, and associated posterior Cramér-Rao lower bound (PCRLB). CKF using two single-point filter initialization algorithms achieves accurate state estimation with low computational complexity.