Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (2): 523-527.DOI: 10.11772/j.issn.1001-9081.2018061417

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Two-dimensional parameter estimation of near-field sources based on iterative adaptive approach

WANG Bo, LIU Deliang   

  1. Department of Missile Engineering, Army Engineering University(Shijiazhuang Campus), Shijiazhuang Hebei 050003, China
  • Received:2018-07-10 Revised:2018-09-05 Online:2019-02-10 Published:2019-02-15
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61601494).


王波, 刘德亮   

  1. 陆军工程大学(石家庄校区) 导弹工程系, 石家庄 050003
  • 通讯作者: 刘德亮
  • 作者简介:王波(1995-),男,安徽宿州人,硕士研究生,主要研究方向:阵列信号处理;刘德亮(1982-),男,山东蓬莱人,讲师,博士,主要研究方向:无线定位。
  • 基金资助:

Abstract: A Near-Field Iterative Adaptive Approach (NF-IAA) was proposed for the joint estimation of Direction Of Arrival (DOA) and range of near-field sources. Firstly, all possible source locations in the neaar field region were represented by dividing two-dimensional grids. Each location was considered to have a potential incident source mapping to an array, indicating the output data model of the array. Then, through the loop iteration, the signal covariance matrix was constructed by using the previous spectral estimation results, and the inverse of the covariance matrix was used as the weighting matrix to estimate the energy of the potential sources corresponding to each location. Finally, the three-dimensional energy spectrum was figured. Since only the energy of real existing source is not 0, the angles and distances corresponding to the peaks are the two-dimensional location parameters of real existing sources. Simulation experimental results show that the DOA resolution probability of the proposed NF-IAA reaches 90% with 10 snapshots, while the DOA resolution probablity of Two-Dimension Multiple Signal Classification (2D-MUSIC) algorithm is only 40%. When the number of snapshots is reduced to 2, 2D-MUSIC algorithm has failed, but NF-IAA can still distinguish 3 incident sources and accurately estimate the two-dimension location parameters. As the number of snapshots and Signal-to-Noise Ratio (SNR) increase, NF-IAA always performs better than 2D-MUSIC. The experimental results show that NF-IAA has the ability to estimate the two-dimensional location parameters of near-field sources with high precision and high resolution when the number of snapshots is low.

Key words: iterative adaptive approach, weighted least square, two-dimensional parameter estimation, near-field source, array signal processing

摘要: 针对近场源波达方向(DOA)和距离的联合估计问题,提出一种近场迭代自适应算法(NF-IAA)。首先通过划分二维网格表示出近场区域内信源所有可能的位置,每个位置都看作存在一个潜在的信源入射到阵列上,表示出阵列输出的数据模型;然后通过循环迭代利用上一次谱估计的结果构建信号的协方差矩阵,将协方差矩阵的逆作为加权矩阵估计出每个位置对应的潜在信源能量;最后绘制出三维能量谱图,由于只有真实存在的信源能量不为0,因此谱峰对应的位置即为真实存在信源的位置。仿真实验表明在10个快拍条件下,NF-IAA的DOA分辨概率达到了90%,而二维多重信号分类(2D-MUSIC)算法只有40%;当快拍数降至2时,2D-MUSIC算法已经失效,而NF-IAA仍然能很好地分辨出3个入射信源并且准确地估计出位置参数。随着快拍数和信噪比(SNR)的增加,NF-IAA的估计性能一直优于2D-MUSIC。实验结果表明,NF-IAA具备少快拍条件下高精度、高分辨地估计近场源二维位置参数的能力。

关键词: 迭代自适应方法, 加权最小二乘法, 二维参数估计, 近场源, 阵列信号处理

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