Single base station tracking approaches with hybrid TOA/AOD/AOA measurements in different propagation environments

IF 2.9 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shixun Wu;Miao Zhang;Kanapathippillai Cumanan;Kai Xu;Zhangli Lan
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引用次数: 0

Abstract

In this paper, mobile terminal (MT) tracking based on time of arrival (TOA), angle of departure (AOD), and angle of arrival (AOA) measurements with one base station is investigated. The main challenge is the unknown propagation environment, such as line-of-sight (LOS), non-line-of-sight (NLOS) modeled as one-bounce scattering or mixed LOS/NLOS propagations, which may result in heterogeneous measurements. For LOS scenario, a linear Kalman filter (LKF) algorithm is adopted through analyzing and deriving the estimated error of MT. For NLOS scenario, as the position of scatterer is unknown, a nonlinear range equation is formulated to measure the actual AOD/AOA measurements and the position of scatterer, and three different algorithms: The extended Kalman filter (EKF), unscented Kalman filter (UKF) and an approximated LKF are developed. For mixed LOS/NLOS scenario, the modified interacting multiple model LKF (M-IMM-LKF) and the identified LKF algorithms (I-LKF) are utilized to address the issue of the frequent transition between LOS and NLOS propagations. In comparison with EKF and UKF algorithms, the simulation results and running time comparisons show the superiority and effectiveness of the LKF algorithm in LOS and NLOS scenarios. Both M-IMM-LKF and I-LKF algorithms are capable to significantly reduce the localization errors, and better than three existing algorithms.
不同传播环境下TOA/AOD/AOA混合测量的单基站跟踪方法
本文研究了单基站下基于到达时间(TOA)、出发角(AOD)和到达角(AOA)的移动终端跟踪问题。主要的挑战是未知的传播环境,例如视距(LOS),非视距(NLOS)建模为单弹跳散射或混合LOS/NLOS传播,这可能导致异构测量。对于LOS场景,通过分析和推导MT的估计误差,采用线性卡尔曼滤波(LKF)算法。对于NLOS场景,由于散射体的位置未知,建立了非线性距离方程来测量实际的AOD/AOA测量值和散射体的位置,并提出了扩展卡尔曼滤波(EKF)、无气味卡尔曼滤波(UKF)和近似LKF三种不同的算法。对于混合LOS/NLOS场景,利用改进的交互多模型LKF (M-IMM-LKF)和识别的LKF算法(I-LKF)解决LOS和NLOS传播频繁转换的问题。通过与EKF和UKF算法的比较,仿真结果和运行时间对比表明LKF算法在LOS和NLOS场景下的优越性和有效性。M-IMM-LKF和I-LKF算法均能显著降低定位误差,优于现有的三种算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.60
自引率
5.60%
发文量
66
审稿时长
14.4 months
期刊介绍: The JOURNAL OF COMMUNICATIONS AND NETWORKS is published six times per year, and is committed to publishing high-quality papers that advance the state-of-the-art and practical applications of communications and information networks. Theoretical research contributions presenting new techniques, concepts, or analyses, applied contributions reporting on experiences and experiments, and tutorial expositions of permanent reference value are welcome. The subjects covered by this journal include all topics in communication theory and techniques, communication systems, and information networks. COMMUNICATION THEORY AND SYSTEMS WIRELESS COMMUNICATIONS NETWORKS AND SERVICES.
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