中国邮电高校学报(英文) ›› 2012, Vol. 19 ›› Issue (6): 29-34.doi: 10.1016/S1005-8885(11)60315-4

• Wireless • 上一篇    下一篇

Near-field time-frequency localization method using sparse representation

王波1,刘娟娟1,孙晓颖张妍君   

  1. College of Communication Engineering, Jilin University, Changchun 130025, China
  • 收稿日期:2012-05-28 修回日期:2012-09-26 出版日期:2012-12-31 发布日期:2012-12-14
  • 通讯作者: 刘娟娟 E-mail:juanjuan10@mails.jlu.edu.cn
  • 基金资助:

    This work was supported by the National Natural Science Foundation of China (60901060).

Near-field time-frequency localization method using sparse representation

  1. College of Communication Engineering, Jilin University, Changchun 130025, China
  • Received:2012-05-28 Revised:2012-09-26 Online:2012-12-31 Published:2012-12-14
  • Contact: Liu Juanjuan E-mail:juanjuan10@mails.jlu.edu.cn
  • Supported by:

    This work was supported by the National Natural Science Foundation of China (60901060).

摘要:

This paper presents a novel near-field source localization method based on the time-frequency sparse model. Firstly, the method converts the time domain data of array output into time-frequency domain by time-frequency transform; then constructs sparse localization model by utilizing the specially selected time-frequency points, and finally the greedy algorithms are chosen to solve the sparse problem to localize the source. When the coherent sources exist, we propose an additional iterative selection procedure to improve the estimation performance. The proposed method is suitable for uncorrelated and coherent sources, moreover, the improved estimation accuracy and the robustness to low signal to noise ratio (SNR) are achieved. Simulations results verify the efficiency of the proposed algorithm.

关键词:

near-field source, time-frequency distribution, sparse representation, DOA estimation, range estimation, greedy algorithm

Abstract:

This paper presents a novel near-field source localization method based on the time-frequency sparse model. Firstly, the method converts the time domain data of array output into time-frequency domain by time-frequency transform; then constructs sparse localization model by utilizing the specially selected time-frequency points, and finally the greedy algorithms are chosen to solve the sparse problem to localize the source. When the coherent sources exist, we propose an additional iterative selection procedure to improve the estimation performance. The proposed method is suitable for uncorrelated and coherent sources, moreover, the improved estimation accuracy and the robustness to low signal to noise ratio (SNR) are achieved. Simulations results verify the efficiency of the proposed algorithm.

Key words:

near-field source, time-frequency distribution, sparse representation, DOA estimation, range estimation, greedy algorithm

中图分类号: