Subspace Fitting DOA Estimator Fast Algorithm by MIMO Sonar

Jin Yong; Liu Xian-xing; Huang Jian-guo; HU Zhen-tao

doi:10.3969/j.issn.0372-2112.2013.10.015

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Subspace Fitting DOA Estimator Fast Algorithm by MIMO Sonar

更新时间：2025-07-16

- Subspace Fitting DOA Estimator Fast Algorithm by MIMO Sonar
- Acta Electronica Sinica Vol. 41, Issue 10, Pages: 1964-1968(2013)
- 作者机构：
  
  1. 河南大学图像处理与模式识别研究所,河南,开封,475000
  2. 西北工业大学航海学院,陕西,西安,710072
  3. 河南大学图像处理与模式识别研究所,河南,开封,475000
  4. 西北工业大学航海学院,陕西,西安,710072
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.U1204611, No.61300214);Research Program of Basic and Frontier Technology of Science and Technology Department of Henan Province (No.132300410278);Henan University Innovative Team Support Program (No.13IRTSHTN021)
- DOI：10.3969/j.issn.0372-2112.2013.10.015
  CLC： TN911.7
- Published：2013
- 稿件说明：
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Jin Yong, Liu Xian-xing, Huang Jian-guo, et al. Subspace Fitting DOA Estimator Fast Algorithm by MIMO Sonar[J]. Acta Electronica Sinica, 2013, 41(10): 1964-1968.
DOI：

Jin Yong, Liu Xian-xing, Huang Jian-guo, et al. Subspace Fitting DOA Estimator Fast Algorithm by MIMO Sonar[J]. Acta Electronica Sinica, 2013, 41(10): 1964-1968. DOI： 10.3969/j.issn.0372-2112.2013.10.015.

摘要

为了充分利用多输入多输出(multiple input multiple output

MIMO)声纳的虚拟阵列孔径的性能

针对目标的方位估计问题

提出了一种MIMO声纳系统的目标方位估计方法

该方法由发射阵列发射正交信号

接收阵列首先对接受信号进行匹配滤波

然后构造子空间拟合关系

最后采用马尔可夫蒙特卡罗吉布斯抽样方法解决子空间拟合求解运算量大的问题.仿真研究表明

两目标条件下

采用该方法的MIMO声纳系统方位估计精度在中低信噪比条件下接近MIMO最大似然方位估计

优于其它MIMO阵列方位估计方法

而运算量只是最大似然方法的约1/4.水池试验结果验证了算法的有效性.

Abstract

A new DOA(direction of arrival)estimator based on MIMO sonar system is proposed to estimate the DOA of targets.First

orthogonal signals are sent out by transmitting sensors and the output of receiving sensors is matched filter by transmitting signal.Then

solution of space fitting is achieved.Finally

Markov Monte Carlo methods Gibbs sampling is combined with subspace fitting method (GSF) to lighten computation burden.The simulations show that on the condition of two sources

GSF provides similar performance to that achieved by the ML method and better performance than other method in low and mid SNR range but its computational cost is only 1/4 of ML.Moreover

our proposed method is verified by the pool experiment.

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