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基于回溯筛选的稀疏重构时延估计算法

冷雪冬 巴斌 逯志宇 王大鸣

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基于回溯筛选的稀疏重构时延估计算法

冷雪冬, 巴斌, 逯志宇, 王大鸣

Sparse reconstruction time delay estimation algorithm based on backtracking filter

Leng Xue-Dong, Ba Bin, Lu Zhi-Yu, Wang Da-Ming
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  • 针对无线定位中时延估计在小样本(单快拍)、低信噪比条件下需要大量独立分布测量数据问题,提出了一种基于回溯筛选的稀疏重构时延估计算法,实现了单快拍、低信噪比条件下接收信号的精确时延估计.该算法首先建立接收信号的稀疏表示模型,然后基于该模型建立正交观测矩阵,最后在重构算法中引入回溯筛选思想,利用时延与观测矩阵之间的一一对应关系得到时延的无偏估计.对该模型下时延估计的克拉美罗界进行了推导.仿真分析表明,所提方法在单快拍、低信噪比条件下精度远高于求根多重信号分类算法,相比于正交匹配追踪算法,在较小的复杂度代价下性能得到了较大提升.
    The time delay estimation is widely used in wireless location field, and is the research emphasis in complex environment of this field. The current delay estimation algorithms can be classified as five methods of correlation, high-order statistics, self-adaption, maximum likelihood and subspace. However, the existing algorithms can hardly achieve an ideal performance in small sample(single snapshot) and low signal-to-noise ratio environment during wireless location. In order to solve the problem about the insufficiency of the current algorithms in the above conditions, many new methods have been introduced into the delay estimation problem. The compressed sensing sparse reconstruction method has been applied to the signal processing field as a newly-proposed algorithm in recent years. The delay estimation is realized by using the method of sparse reconstruction, in which the sparse representation of the signal is the premise. The rational construction of the measurement matrix and the design of the signal reconstruction algorithm are the core of correct estimation.The purpose of this article is to deal with the lack of measurement data in small sample(single snapshot) and low signal-to-noise ratio environment during wireless location. In the model of wireless location, the signal can be represented as a sparse matrix form by selecting suitable sparse representation matrix. The wireless multi-channel is measured in the time domain, the propagation delay varies with channel and the delay representation in the time domain is sparse, so that it can be directly constructed into the form of sparse signal. Since the necessary and the sufficient condition of the coefficient sparse matrix successfully reconstructed by the measurement matrix are the measurement matrix meeting the restricted isometry property(RIP). The orthogonal measurement matrix based on the steering vector by the method of Gram-Schmidt is proven to achieve the RIP. A novel sparse reconstruction algorithm based on backtracking filter is constructed to estimate the time delay. In order to guarantee that the first selection includes the optimal atom, several atoms are selected. And then the backtracking mechanism is introduced, and the selected atoms are approached by the method of the minimum square to sequence the obtained signals and select the optimal atom. Therefore, this method can be used to guarantee that the optimal atom is selected. The presented algorithm can achieve the delay estimation by using the corresponding relation between the time delay and the measurement matrix in a high precision. Furthermore, the Cramer-Rao bound(CRB) of this model is derived. Finally, simulations show that the proposed approach is suitable for small sample(single snapshot) and low signal-to-noise ratio environment. The proposed method can achieve a higher precision than Root-Music and improve performance at low complexity cost compared with OMP algorithm. The simulation result proves that the algorithm is stable and reliable.
      通信作者: 冷雪冬, lengxuedong@outlook.com
    • 基金项目: 国家自然科学基金(批准号:61401513)资助的课题.
      Corresponding author: Leng Xue-Dong, lengxuedong@outlook.com
    • Funds: Project supported by the National Natural Science Foundation of China(Grant No. 61401513).
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    Wang F Q, Zhang X F 2014 ETRI J. 36 460

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    Stoica P, Nehorai A 1989 IEEE Trans. Acoust. Speech Signal Process. 37 720

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  • [1]

    Zhang M, Ma Y L, Li B Q 2013 Chin. Phys. B 22 030511

    [2]

    Knapp C, Carter G 1976 IEEE Trans. Acoust. Speech Signal Process. 24 320

    [3]

    Ma W, Huang J G 2001 Comput. Eng. Appl. 37 54(in Chinese)[马雯, 黄建国2001计算机工程与应用37 54]

    [4]

    Cheng L, Chen G, Gao W Z, Zhang F, Li G 2014 IEEE Trans. Smart Grid 5 2957

    [5]

    Champagne B, Eizenman M, Pasupathy S 1989 Acoustics, Speech, and Signal Processing Glasgow, UK, May 23-26, 1989 p2633

    [6]

    Li X M, Tao R, Wang Y 2010 Radar Sci. Technol. 8 362(in Chinese)[李雪梅, 陶然, 王越2010雷达科学与技术8 362]

    [7]

    Wang F Q, Zhang X F, Wang F 2014 J. Commun. 35 137(in Chinese)[王方秋, 张小飞, 汪飞2014通信学报35 137]

    [8]

    Li J, Zhu J D, Feng Z H, Zhao Y J, Li D H 2015 Circ. Syst. Signal Process. 34 3897

    [9]

    Donoho D L 2006 IEEE Trans. Inform. Theory 52 1289

    [10]

    Wang H T, Wang J 2013 J. Electron. Inform. Technol. 35 877(in Chinese)[王海涛, 王俊2013电子与信息学报35 877]

    [11]

    Lin B, Zhang Z H, Zhu J B 2014 J. Electron. Inform. Technol. 36 589(in Chinese)[林波, 张增辉, 朱炬波2014电子与信息学报36 589]

    [12]

    Chen Y F, Huang J G, Su J J 2013 Torpedo Technol. 21 110(in Chinese)[陈玉凤, 黄建国, 苏建军2013鱼雷技术21 110]

    [13]

    Wang F Q, Zhang X F 2014 ETRI J. 36 460

    [14]

    Candès E J, Tao T 2005 IEEE Trans. Inform. Theory 51 4203

    [15]

    Stoica P, Nehorai A 1989 IEEE Trans. Acoust. Speech Signal Process. 37 720

    [16]

    Stoica P, Nehorai A 1990 IEEE Trans. Acoust. Speech Signal Process. 38 1783

    [17]

    Gast M S(translated by O'Reilly Media, Inc.) 2007802.11 Wireless Networks:The Definitive Guide (2nd Ed.)(Nanjing:Publishing House of Southeast University) pp309-317(in Chinese)[加斯特M S著(O'Reilly公司译) 2007802.11 无线网络权威指南(第二版)(南京:东南大学出版社)第309-317页]

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出版历程
  • 收稿日期:  2016-06-02
  • 修回日期:  2016-07-14
  • 刊出日期:  2016-11-05

基于回溯筛选的稀疏重构时延估计算法

  • 1. 解放军信息工程大学信息系统工程学院, 郑州 450001
  • 通信作者: 冷雪冬, lengxuedong@outlook.com
    基金项目: 国家自然科学基金(批准号:61401513)资助的课题.

摘要: 针对无线定位中时延估计在小样本(单快拍)、低信噪比条件下需要大量独立分布测量数据问题,提出了一种基于回溯筛选的稀疏重构时延估计算法,实现了单快拍、低信噪比条件下接收信号的精确时延估计.该算法首先建立接收信号的稀疏表示模型,然后基于该模型建立正交观测矩阵,最后在重构算法中引入回溯筛选思想,利用时延与观测矩阵之间的一一对应关系得到时延的无偏估计.对该模型下时延估计的克拉美罗界进行了推导.仿真分析表明,所提方法在单快拍、低信噪比条件下精度远高于求根多重信号分类算法,相比于正交匹配追踪算法,在较小的复杂度代价下性能得到了较大提升.

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