Distributed path optimization of multiple UAVs for AOA target localization

This paper is concerned with unmanned aerial vehicle (UAV) path optimization for AOA target localization via distributed processing. A distributed UAV path optimization algorithm based on gradient descent method is developed using the diffusion extended Kalman filter (DEKF). With this algorithm, a group of UAVs can realize self-adaptive path optimization in order to improve estimation performance. The presented distributed path optimization strategy aims to minimize the estimation mean squared error (MSE) by minimizing the trace of the error covariance matrix. The UAV dynamic communication topology caused by communication range constraint is analyzed. Furthermore, the UAV 6-degree-of-freedom (DOF) dynamic modeling is taken into consideration to generate realistic UAV trajectories. The properties and effectiveness of the proposed algorithm are discussed and verified with simulation examples.

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