利用多输入多输出雷达低秩杂波的降维空时自适应算法

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利用多输入多输出雷达低秩杂波的降维空时自适应算法

更新时间：2025-05-08

- 利用多输入多输出雷达低秩杂波的降维空时自适应算法
- Issue 8, Pages: 76-81(2012)
- 作者机构：
  
  西安电子科技大学雷达信号处理国家重点实验室
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN957.51
- Online First：10 August 2012，
  
  Published：2012
- 稿件说明：
移动端阅览
[J]. 2012, (8): 76-81.
DOI：

[J]. 2012, (8): 76-81. DOI：

摘要

为了抑制机载多输入多输出(MIMO)雷达接收信号中的杂波和有源干扰

提出一种利用MIMO雷达低秩杂波进行降维的空时自适应处理算法(LRC-RD).首先根据系统参数离线构造杂波子空间矩阵

再结合有源干扰加噪声协方差矩阵以及目标空时导向矢量来构造降维矩阵

最后用降维后的数据计算自适应权值.LRC-RD算法可将全维数据维数降为杂波的秩加1

从而降低了计算复杂度和计算自适应权值所需的训练样本数

所以收敛速度快

并且其理论性能可以达到全维处理的理论性能.仿真实验表明

LRC-RD算法在没有误差、样本数为降维后的数据维数的2倍时

其信噪比损失在高速区比基于双迭代的算法和基于子阵划分的算法分别高出约5dB和17dB.

Abstract

A new reduced dimension space-time adaptive processing algorithm that exploits low rank clutter(LRC-RD)is proposed to suppress clutter and jamming for airborne MIMO radar. Firstly

a clutter subspace matrix is constructed off-line with known radar system parameters

and then the transform matrix with reduced dimension is generated by combining the jamming plus noise covariance matrix with the target space-time steering vector. Finally

adaptive weights are calculated from the data with reduced dimension. The data dimension after dimension reducing equals clutter rank plus one. Thus the computational cost and the number of samples for computing adaptive weights are reduced apparently which make LRC-RD converge fast

and the theoretical performance of LRC-RD can reach that of full dimensional adaptive processing. Numerical results show that when the number of training samples is double of the dimension reduced by LRC-RD and there is no array errors

and the signal to interference plus noise ratio loss of the proposed algorithm is about 5 dB and 17 dB higher than those of the bi-iteration based algorithm and the sub-array based algorithm

respectively.

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