基于指纹库更新补偿的波束跟踪算法研究

CUI ZiYu,WEI Hao,Zhao Qi,Wang Yazhou

摘要:

针对拒止、复杂电磁环境下,高动态无人节点定向通信面临的坐标信息不精确、飞行姿态和轨迹变化剧烈等问题,为保持可靠的波束对准与跟踪,提出了一种基于卡尔曼滤波的指纹库更新补偿算法。首先,利用卡尔曼滤波算法对自身姿态进行预测更新,建立新的载体坐标系；利用改进的算法对波束指向进行预测更新,并利用指纹库对状态向量均值和协方差矩阵进行更新补偿,调节采样比例,并将新的数据存入指纹库对指纹库数据更新,然后进行二次状态信息预测,完成最终波束指向。整体设计的波束跟踪算法流程更加符合实际应用场景,满足无人机自组网的需求。仿真结果表明,在半波束宽度为3°,100个通信时隙中,维持正常通信的成功率有92%以上,相比传统跟踪算法提高了8%,具有更加稳定的通信质量。

Abstract:

In view of the problems of inaccurate coordinate information, dramatic changes in flight attitude and trajectory in the directional communication of high dynamic unmanned nodes under the complex electromagnetic environment. In order to maintain reliable beam aligning and tracking, a fingerprint database update compensation algorithm based on Kalman filter is proposed. Firstly, the Kalman filter algorithm is used to predict and update its own attitude, and a new carrier coordinate system is established. The improved algorithm is used to predict and update the beam pointing, and the fingerprint database is used to update and compensate the mean value and covariance matrix of the state vector. Adjusting the sampling ratio , and the new data is stored in the fingerprint database to update the fingerprint database data. then the secondary state information is predicted to complete the final beam pointing. The overall design of the beam tracking algorithm process is more in line with the actual application scenarios and meets the needs of UAV ad hoc networks. The simulation results show that the success rate of maintaining normal communication is more than 92 % in 100 communication time slots with half-beam width of 3 °, which is 8 % higher than that of traditional tracking algorithm. It has more stable communication quality.