Distributed pseudolinear estimation and UAV path optimization for 3D AOA target tracking

We address the problem of angle-of-arrival (AOA) target tracking using multiple unmanned aerial vehicles (UAVs) in three-dimensional (3D) space. A distributed 3D AOA target tracking method is proposed consisting of a distributed estimator and path optimization algorithm for multiple UAVs. First a novel 3D distributed pseudolinear Kalman filter (DPLKF) is developed to improve the stability of an extended Kalman filter solution. The DPLKF consists of two coupled filters; viz., an xy-DPLKF and a z-DPLKF. The bias problem of the 3D DPLKF is analyzed and a bias reduction method is proposed. A distributed path optimization algorithm is developed subject to communication range constraints and no-fly zones. This algorithm computes UAV waypoints using gradient-descent optimization on the xy-plane and grid search along the z-axis. To improve the tracking performance, the trace of the error covariance matrix is minimized. The properties and effectiveness of the proposed strategy are discussed and validated with simulation examples.