Department of Information Countermeasures, Air Force Early Warning Academy, Wuhan 430010, China.
Sensors (Basel). 2023 Jul 5;23(13):6172. doi: 10.3390/s23136172.
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.
本研究提出了三种分布式波束形成算法，以解决无人机 (UAV) 阵列中定位和信号相位误差的挑战，这些挑战会影响波束形成的有效性。首先，分析了近场条件下的阵列接收信号相位误差模型。在没有导航数据的情况下，提出了一种基于扩展卡尔曼滤波器 (EKF) 的波束形成算法。在有导航数据的情况下，利用泰勒展开简化模型，将补偿后的接收信号相位的非高斯噪声近似为高斯噪声，并估计卡尔曼滤波器 (KF) 中的噪声协方差矩阵。然后，开发了一种基于 KF 的波束形成算法。为了进一步估计接收信号相位的高斯噪声分布，利用无迹变换 (UT) 迭代估计噪声协方差矩阵，提出了一种基于无迹卡尔曼滤波器 (UKF) 的波束形成算法。通过仿真验证了所提出的算法，表明它们能够抑制误差对近场 UAV 阵列波束形成的不利影响。本研究为在不同条件下实现 UAV 阵列波束形成提供了参考。
