一种拥堵有效的车辆网络协同定位方案

Shahram Soroush-Jahromi, J. Abouei, G. Mirjalily, M. R. Taban
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引用次数: 1

摘要

为了满足智能交通系统的需求,提出了基于车辆间距离和运动信息共享的协同定位方法。然而,参与车辆之间频繁的数据交换给网络带来了通信开销。随着集群中车辆数量的增加,通信开销可能会导致拥塞,从而导致数据包传输的不可靠性和吞吐量的降低。为了解决上述缺陷,我们提出了一种主动的拥堵高效协同定位(CE-CP)方案,该方案可以在高密度车辆网络中提供高定位精度的同时减轻可能的拥堵。其主要思想是在传输阶段省略一些距离测量信息,然后利用矩阵补全算法在接收车辆上恢复这些省略的距离测量信息。为了提高车辆的定位精度,我们引入了一个无气味卡尔曼滤波(UKF)来融合共享的距离和运动学信息。仿真结果表明,与基于扩展卡尔曼滤波的CP方法相比,基于ukf的CE-CP方案在缓解拥塞现象方面的效率更高,定位精度提高了约17%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Congestion Efficient Cooperative Positioning scheme for vehicular networks
Cooperative positioning approaches based on sharing ranges and kinematics information among clustered vehicles are introduced to comply with the expectations of intelligent transportation systems. However, frequently exchanging data among participating vehicles imposes communication overhead on the network. As the number of vehicles in the cluster increases, the communication overhead may turn into the congestion situation which yields to an unreliability of packets transmission and the throughput degradation. To tackle the above flaws, we propose an initiative Congestion Efficient Cooperative Positioning (CE-CP) scheme which mitigates the probable congestion along with providing a high positioning accuracy in high-density vehicular networks. The main idea is to omit some of the range measurements information at the transmission stage and then recover those omitted measurements at the receiver vehicle using the matrix completion algorithm. To achieve a high accuracy in vehicles' positioning, we incorporate an Unscented Kalman Filter (UKF) to fuse the shared ranges and kinematics information. Simulation results clarify the efficiency of our UKF-based CE-CP scheme in mitigating the congestion phenomenon and attaining an approximately 17% improvement in the positioning accuracy comparing to the extended Kalman filter-based CP approach.
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