摘要
在穿墙雷达成像中,墙体的强反射信号会干扰墙后目标的成像与检测,甚至湮没临近墙体的目标.传统的空域滤波法从回波幅值出发,通过空间域线性滤波实现对墙体杂波的抑制.然而,该方法在墙体与目标临近的情况下效果较差,且不适用于MIMO穿墙雷达.本文针对超宽带MIMO穿墙雷达成像过程中墙体杂波抑制问题,提出了一种基于子空间投影的奇异值分解方法,该方法从信号回波的特征出发,通过回波信号矩阵最主要的一个或几个奇异值成分确定墙杂波子空间,再利用正交子空间投影去除回波信号中的墙杂波成分.MATLAB仿真结果表明,提出的墙体杂波抑制方法在消除墙体杂波对目标成像结果的影响方面效果更佳,还能消减随机噪声等弱干扰杂波,抑制多目标探测中的虚假成像,改善成像质量,实现目标的精确成像.
Imaging and detection of the targets behind the wall are usually interfered by strong wall reflections in Through-the-Wall Radar Imaging（TWRI）.The targets near the wall may even be drowned completely.Therefore, mitigating the wall clutter is of great significance for identifying the target clearly in radar imaging.Traditional methods for wall-clutter mitigation in single input and single output system（SISO）usually make use of interference canceling and spatial filtering,which are based on the invariance of the spatial characteristics of wall.The spatial filter is applied along the array aperture to remove zero-frequency and low-frequency components,which correspond to the wall electromagnetic responses.However,they are not applicable to multiple inputs and multiple outputs systems（MIMO）.To remove the clutter for UWB MIMO TWR,this paper presents an effective SVDmethod based＆amp;nbsp;on subspace proj ection.As an Eigen-structure technique,SVD is applied to the received signal matrix to extract the target signatures.The signal space is decomposed into three subspaces：the clutter subspace,the target subspace,and the noise subspace.The clutter subspace mainly includes wall reflections,which is much stronger than target signal and noise.The clutter subspace and the target subspace can be isolated from the signal matrix according to the dominant singular components（SCs）.And then,orthogonal subspace proj ection are used to mitigate wall components and make target signals prominent.Finally,MATLAB simulation software is used for modeling and simulating. From the experimental results,we can see that the proposed method is effective in removing wall reflections, suppressing clutter,and highlighting the target.This method is superior to interference canceling method. Furthermore,this SVDmethod can also remove random noises and suppress false imaging in multi-obj ect detecting, improving image quality and reliability.
出处
《南京大学学报（自然科学版）》
CAS
CSCD
北大核心
2016年第6期1146-1153,共8页
Journal of Nanjing University（Natural Science）
基金
毫米波国家重点实验室开放课题(K201514)
