Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (11): 3343-3348.DOI: 10.11772/j.issn.1001-9081.2019040672

• Network and communications • Previous Articles     Next Articles

Multi-objective automatic identification and localization system in mobile cellular networks

MIAO Sheng1,2, DONG Liang2, DONG Jian'e1, ZHONG Lihui1   

  1. 1. College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming Yunnan 650224, China;
    2. Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, Kunming Yunnan 650011, China
  • Received:2019-04-22 Revised:2019-07-23 Online:2019-08-21 Published:2019-11-10
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (11303094), the Commission for Collaborating Research Program of Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences (OP201506).


苗晟1,2, 董亮2, 董建娥1, 钟丽辉1   

  1. 1. 西南林业大学 大数据与智能工程学院, 昆明 650224;
    2. 中国科学院 天体结构与演化重点实验室, 昆明 650011
  • 通讯作者: 董亮
  • 作者简介:苗晟(1982-),男,云南昆明人,讲师,博士,主要研究方向:无线电信号处理、人工智能;董亮(1982-),男,云南昆明人,高级工程师,博士,CCF会员,主要研究方向:射电天文、无线电信号处理;董建娥(1984-),女,陕西汉中人,讲师,硕士,主要研究方向:图像处理、智能算法;钟丽辉(1984-),女,云南丽江人,讲师,硕士,主要研究方向:无线电信号处理。
  • 基金资助:

Abstract: Aiming at difficult multi-target identification recognition and low localization accuracy in mobile cellular networks, a multi-objective automatic identification and localization method was presented based on cellular network structure to improve the detection efficiency of target number and the localization accuracy of each target. Firstly, multi-target existence was detected through the analysis of the result variance of multiple positioning in the monitoring area. Secondly, cluster analysis on locating points was conducted by k-means unsupervised learning in this study. As it is difficult to find an optimal cluster number for k-means algorithm, a k-value fission algorithm based on beam resolution was proposed to determine the k value, and then the cluster centers were determined. Finally, to enhance the signal-to-noise ratio of received signals, the beam directions were determined according to cluster centers. Then, each target was respectively positioned by Time Difference Of Arrival (TDOA) algorithm by the different beam direction signals received by the linear constrained narrow-band beam former. The simulation results show that, compared to other TDOA and Probability Hypothesis Density (PHD) filter algorithms in recent references, the presented multi-objective automatic identification and localization method for solving multi-target localization problems can improve the signal-to-noise ratio of the received signals by about 10 dB, the Cramer-Mero lower bound of the delay estimation error can be reduced by 67%, and the relative accuracy of the positioning accuracy can be increased more than 10 percentage points. Meanwhile, the proposed algorithm is simple and effective, is relatively independent in each positioning, has a linear time complexity, and is relatively stable.

Key words: cellular network, Time Difference Of Arrival (TDOA), multi-target localization, unsupervised clustering, narrow-band beam forming

摘要: 针对移动蜂窝网对多目标难以检测识别且定位精度不高的问题,提出一种基于蜂窝网结构的多目标自动辨识定位方法。首先,根据对监测区域内目标源的多次定位结果方差来判别是否有多目标存在;其次,采用k-means无监督学习对定位点进行聚类,由于k-means算法的最优簇数难以确定,因此提出了一种基于波束分辨率的k值裂变算法来确定k值,并确定聚类中心;最后,为了提高接收信号的信噪比,通过各聚类中心确定波束方向,再使用基于线性约束的窄带波束形成器依次接收不同波束方向信号,分别对各目标源进行到达时间差定位。仿真结果表明,对于解决多目标定位问题,相对于时延估计算法和概率假设密度(PHD)滤波器算法,所提多目标自动辨识定位方法能够提高接收信号约10 dB的信噪比,对应的时延估计误差的克拉美罗下界能够下降约67%,定位精度相对误差可提高10个百分点以上,而且算法简洁有效,各次定位相对独立,具有较高的效率和较好的稳定性。

关键词: 蜂窝网, 到达时间差, 多目标定位, 无监督聚类, 窄带波束形成

CLC Number: