Near-Field Beamforming Algorithms for UAVs

Abstract

This study presents three distributed beamforming algorithms to address the challenges of positioning and signal phase errors in unmanned aerial vehicle (UAV) arrays that hinder effective beamforming. Firstly, the array’s received signal phase error model was analyzed under near-field conditions. In the absence of navigation data, a beamforming algorithm based on the Extended Kalman Filter (EKF) was proposed. In cases where navigation data were available, Taylor expansion was utilized to simplify the model, the non-Gaussian noise of the compensated received signal phase was approximated to Gaussian noise, and the noise covariance matrix in the Kalman Filter (KF) was estimated. Then, a beamforming algorithm based on KF was developed. To further estimate the Gaussian noise distribution of the received signal phase, the noise covariance matrix was iteratively estimated using unscented transformation (UT), and here, a beamforming algorithm based on the Unscented Kalman Filter (UKF) was proposed. The proposed algorithms were validated through simulations, illustrating their ability to suppress the malign effects of errors on near-field UAV array beamforming. This study provides a reference for the implementation of UAV array beamforming under varying conditions.

Design of Robust Sparse Wideband Beamformers with Circular-Model Mismatches Based on Reweighted ℓ2,1 Optimization

“…Beamforming, as one of the key technologies in array signal processing, has widely applied in practice, such as wireless communication, medical imaging, radar, remote sensing, etc. [1][2][3][4][5][6][7][8][9][10][11][12]. In wireless communication and remote sensing array signal processing fields, wideband signals are mostly concerned and wideband beamformers based on finite impulse response (FIR) of sensor arrays have been applied in recent years.…”