基于联合胞腔并发接收的5G网络节点定位算法

发布时间：2018-05-09 04:06

本文选题：G移动网络 + 节点定位　；参考：《计算机工程与设计》2017年07期

【摘要】：当前5G移动网络节点定位算法因采用星座接收模式,难以消除定位角镜像误差,在加性高斯白噪声干扰下,易导致节点定位精度不高,为此提出一种基于联合胞腔并发接收结构与角度估计矢量误差消除机制的5G移动网络节点定位算法。依据小区基站与信号发射源之间的物理距离,构建联合胞腔并发接收结构,准确接收节点信号;考虑节点与接收天线之间的切线方向与法线方向的夹角关系,取得待测节点子信号的初步发射角度;通过计算观测角度的极大似然估计值,建立方位角估计矢量误差消除机制,采用极大似然估计方式降低定位角度估计偏差,一旦发生精度下降现象,小区基站通过方位角估计矢量实时获取最低水平的估计偏差,改善信道噪声对节点定位过程的影响。仿真结果表明,与ACB算法及CIPS算法相比,该算法可较好抵抗无线信道中加性高斯白噪声,具有更低的定位误差水平与定位时耗。
[Abstract]:Because the current 5G mobile network node localization algorithm adopts constellation receiving mode, it is difficult to eliminate the positioning angle mirror image error. Under the additive Gao Si white noise interference, it is easy to cause the node localization accuracy to be low. In this paper, a 5G mobile network node localization algorithm based on the joint cellular concurrent receiver structure and angle estimation vector error cancellation mechanism is proposed. According to the physical distance between the cell base station and the signal transmitter, the combined cell cavity concurrent receiving structure is constructed to receive the node signal accurately, and the angle between the tangent direction and the normal direction between the node and the receiving antenna is considered. The initial transmitting angle of the sub-signal of the node to be tested is obtained, the maximum likelihood estimation of the observation angle is calculated, the azimuth estimation vector error elimination mechanism is established, and the maximum likelihood estimation method is used to reduce the orientation angle estimation deviation. Once the accuracy drops, the cell base station obtains the lowest level estimation deviation by azimuth estimation vector in real time, which improves the influence of channel noise on the node location process. The simulation results show that compared with ACB algorithm and CIPS algorithm, this algorithm can resist the additive Gao Si white noise in wireless channel, and has lower localization error level and localization time consuming.
【作者单位】：安阳工学院计算机科学与信息工程学院;郑州铁路职业技术学院软件学院;河南工业大学国际教育学院;
【基金】：国家自然科学基金项目(U1204613) 河南省自然科学基金项目(1311010426)
【分类号】：TN929.5

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