中国邮电高校学报(英文) ›› 2012, Vol. 19 ›› Issue (4): 110-116.doi: 10.1016/S1005-8885(11)60290-2

• Others • 上一篇    下一篇

Novel high-resolution DOA estimation using subspace projection method

司伟建, 蓝晓宇,ZOU Yan   

  1. 1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China 2. No. 91404 Army, Qinhuangdao 066000, China
  • 收稿日期:2011-11-15 修回日期:2012-03-21 出版日期:2012-08-31 发布日期:2012-09-12
  • 通讯作者: 蓝晓宇 E-mail:lanxiaoyu911@163.com
  • 基金资助:

    This work was supported by the National Basic Research Program of China (61393010101-1).

Novel high-resolution DOA estimation using subspace projection method

SI Jian-wei,LAN Xiao-yu,LAN Xiao-Yu   

  1. 1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China 2. No. 91404 Army, Qinhuangdao 066000, China
  • Received:2011-11-15 Revised:2012-03-21 Online:2012-08-31 Published:2012-09-12
  • Contact: Xiao-Yu LAN E-mail:lanxiaoyu911@163.com
  • Supported by:

    This work was supported by the National Basic Research Program of China (61393010101-1).

摘要:

The performance of multiple signal classification (MUSIC) algorithm with regard to solving closely spaced direction of arrivals (DOAs) depends strongly upon the signal-to-noise ratio (SNR) and snapshots. In order to solve this problem, a method by reconstructing the spatial spectrum function with both noise subspace and signal subspace is presented in this paper. The key idea is to apply the full information contained in covariance matrix and change the projection weights of steering vector on the noise and signal subspace by their revised eigenvalues, respectively. Comparing with the MUSIC algorithm, it does not increase any computational complexity either, and remarkably, it has the advantages of simultaneously reducing noise and keeping the high-resolution ability under low SNR and small sample sized scenarios. Simulation and experiment results are included to demonstrate the superior performance of the proposed algorithm

关键词:

resolution, subspace projection

Abstract:

The performance of multiple signal classification (MUSIC) algorithm with regard to solving closely spaced direction of arrivals (DOAs) depends strongly upon the signal-to-noise ratio (SNR) and snapshots. In order to solve this problem, a method by reconstructing the spatial spectrum function with both noise subspace and signal subspace is presented in this paper. The key idea is to apply the full information contained in covariance matrix and change the projection weights of steering vector on the noise and signal subspace by their revised eigenvalues, respectively. Comparing with the MUSIC algorithm, it does not increase any computational complexity either, and remarkably, it has the advantages of simultaneously reducing noise and keeping the high-resolution ability under low SNR and small sample sized scenarios. Simulation and experiment results are included to demonstrate the superior performance of the proposed algorithm

Key words: