J4 ›› 2016, Vol. 43 ›› Issue (1): 30-35.doi: 10.3969/j.issn.1001-2400.2016.01.006

• 研究论文 • 上一篇    下一篇

MIMO雷达迭代降维稳健波束形成方法

虞泓波;冯大政;解虎   

  1. (西安电子科技大学 雷达信号处理国家重点实验室,陕西 西安  710071)
  • 收稿日期:2014-07-20 出版日期:2016-02-20 发布日期:2016-04-06
  • 通讯作者: 虞泓波
  • 作者简介:虞泓波(1988-),男,西安电子科技大学博士研究生,E-mail: beyond_hongbo@126.com.
  • 基金资助:

    国家自然科学基金资助项目(61271293)

Iterative dimension-reduced robust adaptive beamformer for MIMO radar

YU Hongbo;FENG Dazheng;XIE Hu   

  1. (National Key Lab. of Radar Signal Processing, Xidian Univ., Xi'an  710071, China)
  • Received:2014-07-20 Online:2016-02-20 Published:2016-04-06
  • Contact: YU Hongbo

摘要:

针对多输入多输出雷达发射、接收导向矢量失配问题,提出一种迭代降维稳健波束形成方法.首先将整体线性联合估计方法应用到多输入多输出雷达模型中,得到改进的协方差矩阵估计;接着建立多输入多输出雷达发射、接收导向矢量失配模型,根据目标信号输出功率最大原理,建立代价函数以估计真实的发射与接收导向矢量.并提出一种双迭代算法求解该代价函数,每次迭代过程仅需要求解两个低维的凸二次约束二次规划问题.仿真实验表明,与传统算法相比,在导向矢量失配严重情形下,所提算法能够取得更高的输出信干噪比,且收敛速度快,具有较低的计算复杂度.

关键词: 多输入多输出雷达, 稳健波束形成, 二次约束二次规划, 双迭代, 降维

Abstract:

Aiming at the transmitted and received steering vectors mismatch problem, an iterative dimension-reducing robust adaptive beamformer for MIMO radar is presented. The General Linear Combined(GLC) method is applied in MIMO radar to obtain the enhanced covariance matrix estimation, and the transmitted and received steering vectors mismatch model is established. The cost function is established based on the desired signal output power maximum principle to estimate the transmitted and received steering vectors. The bi-iteration method is proposed to solve the cost function and it is merely necessary to find out two low-dimensional convex quadratically constrained quadratic programming(QCQP) problems in per iteration. Simulation results show that the proposed method can obtain the higher output signal-to-noise-plus-interference(SINR) under the condition of severe steering vector mismatch than the conventional robust beamformers, and that the proposed method can converge fast so that it has the lower computational complexity.

Key words: multiple-input multiple-output(MIMO) radar, robust beamformer, quadratically constrained quadratic programming(QCQP), bi-iteration, dimension-reduced

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