基于二阶扩展卡尔曼滤波理论的毫米波波束跟踪算法

毫米波(mm wave)通信中波束覆盖方向性给高速移动的终端设备(如高速车辆,火车和无人机)带来了巨大挑战.现有基于扩展卡尔曼滤波理论(extended Kalman filter theory,EKF)的波束跟踪算法存在估计精度较低的问题,为此,采用二阶扩展卡尔曼滤波理论(second-order extended Kalman filter theory,SOEKF)作为波束跟踪算法,并辅以一种低复杂度的波束切换方案,随后基于时变信道演化模型构建状态向量和量测方程,在数值仿真中给出了信噪比,阵列大小,AoA/AoD(angles of arrival/angles of departure)的变化速度对算法性能的影响.仿真结果表明,所采用的SOEKF算法相较于同类算法具有更高的估计精度,同时保持了较低的复杂度,更适合快速变化的信道环境.

Beam tracking based on second-order extended Kalman filter theory in millimeter wave communication systems

Beam coverage directionality in millimeter wave (mm wave) communications poses significant challenges for high-speed mobile terminal equipment such as high-speed vehicles, trains and drones. The existing low-complexity beam tracking algorithm based on extended Kalman filter theory (EKF) has low estimation accuracy. Therefore, this paper adopts the second-order extended Kalman filter theory (SOEKF) as the beam tracking algorithm, supplemented by a low-complexity beam switching scheme, and then constructs the state vector based on the time-varying channel evolution model and the measurement equation, and finally studies the influence of the signal-to-noise ratio, array size, AoA/AoD (angles of arrival/angles of departure) change speed on the algorithm performance in the numerical simulation. The simulation results show that the SOEKF algorithm used in this paper has higher estimation accuracy than similar algorithm. While maintaining low complexity, the proposed algorithm is more suitable for the rapidly changing channel environment.