IN-ME Position Error Compensation Algorithm for the Near-Field Beamforming of UAVs

Abstract

The target of an unmanned aerial vehicle swarm will present near-field characteristics when it is integrated as an array, and the existence of the unmanned aerial vehicle swarm motion error will greatly deteriorate the beam pattern formed by the array. To solve these problems, a near-field array beamforming model with array element position error is constructed, and the Taylor expansion of the phase difference function is used to approximately simplify the model. The improved Newton maximum entropy algorithm is proposed to estimate and compensate for the phase errors. The maximum entropy objective function is established, and the Newton iterative algorithm is used to estimate the phase error iteratively. To select the proper Newton iteration initial value, based on a single reference source signal, the initial value of the phase error is estimated through the phase gradient information of the received array signal. Beamforming is carried out after phase error compensation regarding the array. In order to assess the mismatch of the phase error compensation function based on the proposed method, when the beam is scanning, the effectively compensated spatial area of the array beamforming is divided, which lays a foundation for subsequent spatial region division and unmanned aerial vehicle swarm path planning. The simulation results show that the beam formed by the method proposed in this paper has a lower sidelobe level, and as the signal-to-noise ratio changes, the robustness of the proposed method is better validated. The proposed algorithm can effectively suppress the adverse influence of array element position error on array beamforming, and when the beam is scanning, the effectively compensated area of the phase error compensation function is divided, based on the proposed method.

Preface to the Special Issue on “Recent Advances in Swarm Intelligence Algorithms and Their Applications”—Special Issue Book

“…The proposed algorithm would be used to determine the load demand of a power system, by sustaining the various equality and inequality constraints, to diminish the overall generation cost. To overcome unmanned aerial vehicle swarm motion error, a near-field array beam-forming model with array element position error is constructed in [12], and the Taylor expansion of the phase difference function is used to approximately simplify the model. The improved Newton maximum entropy algorithm is proposed to estimate and compensate for the phase errors.…”