并置CS-MIMO雷达中SFMM设计的干扰与干扰联合抑制策略

需积分: 5 26 浏览量更新于2024-08-26 收藏 1.12MB PDF 举报

"这篇研究论文探讨了在并置压缩感知多输入多输出(CS-MIMO)雷达系统中，如何利用稀疏因子匹配矩阵(SFMM)设计来实现对目标感兴趣区域(兴趣扇区)内的主动干扰和扇区外干扰的联合抑制。" 正文: 在现代雷达系统中，干扰和干扰的抑制是提升雷达性能的关键环节。并置CS-MIMO雷达是一种先进的雷达技术，它通过使用多个天线发射和接收信号，能够提供更高的空间分辨率和更强的抗干扰能力。然而，这样的系统同时也面临着来自目标扇区内外的干扰问题，这可能会严重降低雷达的检测能力和目标识别精度。该研究论文"并置CS-MIMO雷达中的SFMM设计，用于干扰和干扰联合抑制"深入研究了这个问题，并提出了稀疏因子匹配矩阵(SFMM)的设计方法。SFMM是一种优化工具，其目的是在保持系统性能的同时，减少干扰的影响。在CS-MIMO雷达中，SFMM可以调整各个发射和接收通道之间的关系，以实现对不同类型干扰的有效抑制。论文指出，主动干扰通常集中在目标的特定扇区内，而扇区外干扰则可能来自多个方向，且分布广泛。SFMM设计的目标就是同时处理这两种干扰类型，确保在抑制干扰的同时，保护感兴趣的信号不被破坏。这种方法涉及到对雷达信号处理算法的创新，以适应复杂的电磁环境。 SFMM的构建和优化过程涉及到了压缩感知理论，该理论允许雷达系统在较低的数据采样率下捕获必要的信息，从而降低了计算复杂性和硬件需求。在SFMM的帮助下，雷达可以更有效地估计和去除干扰源，同时保持对目标的精确探测。此外，论文还可能涵盖了实验和仿真结果，以验证SFMM设计在实际应用中的有效性。这些结果可能展示了在不同干扰环境下，采用SFMM的CS-MIMO雷达系统相比于传统方法在干扰抑制方面的显著优势。总结来说，这篇研究论文为并置CS-MIMO雷达系统的干扰抑制提供了一个创新的解决方案，即使用稀疏因子匹配矩阵。通过这种方式，雷达系统能够更好地应对各种干扰源，提高目标检测的准确性和可靠性，对于提升现代雷达系统的性能具有重要的理论和实践价值。

IET Radar, Sonar & Navigation

Research Article

SFMM design in colocated CS-MIMO radar for

jamming and interference joint suppression

ISSN 1751-8784

Received on 11th October 2017

Revised 13th March 2018

Accepted on 15th March 2018

E-First on 17th April 2018

doi: 10.1049/iet-rsn.2017.0444

www.ietdl.org

Wei Xiong

1,2

, Maria Greco

, Fulvio Gini

, Gong Zhang

, Zhenni Peng

Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing

211106, People's Republic of China

Leihua Electronic Technology Research Institute, Aviation Industry Corporation of China, Wuxi 214063, People's Republic of China

Dipartimento di Ingegneria dell’ Informazione, University of Pisa, Pisa 56122, Italy

Key Laboratory of Advanced Technology for Small and Medium-sized UAV, Ministry of Industry and Information Technology, Nanjing 210016,

People's Republic of China

E-mail: gzhang@nuaa.edu.cn

Abstract: This study focuses on the problem of joint suppression of active jamming in target sector-of-interest (SOI) and out-of-

sector interference for colocated compressive sensing multiple-input–multiple-output (CS-MIMO) radar. Three effective

strategies for spatial filter measurement matrix (SFMM) design are outlined. Unlike the previous reported Capon beamformer

and minimum variance distortionless response beamformer, the proposed design strategies only depend on the target spatial

SOI rather than the accurate directions-of-arrival (DOAs) of targets, jammers and interfering sources to obtain deep nulls or

notches for the SOI jamming and low-attenuation levels for the out-of-sector interference. The SFMM design criteria are derived

using the second-order cone programming and solved as convex optimisation problems. The proposed approaches can

simultaneously suppress the SOI jamming and attenuate the out-of-sector interference. Meanwhile, better DOA estimation

accuracy can be achieved. Simulation results demonstrate the superiority of the proposed approaches over the other methods

for colocated CS-MIMO radar.

1 Introduction

Unlike the distributed multiple-input–multiple-output (MIMO)

radar, the antennas in colocated MIMO radar are closely spaced so

that, in the far-field, they look at the target under the same aspect

and, contrary to the phased array (PA) systems, waveform diversity

allowed by the transmit and receive arrays can be exploited [1, 2].

It has been shown that the colocated MIMO radars have some

advantages over PA radars such as the extended virtual array

aperture, the increased upper limit on the number of detectable

targets, improved parameter identifiability and angular resolution

and so on [1–5]. In the last decade, by exploiting the target sparsity

in the target space/scene, compressive sensing (CS) theory has

been successfully applied to MIMO radars and has shown great

potentials in target parameter estimation [6], Synthetic Aperture

Radar (SAR) imaging [7], waveform design [8] and interference

attenuation [9]. CS-MIMO radar systems have been proposed in

[6–8] and the beamforming techniques for colocated CS-MIMO

radars have also been studied to achieve processing gain or the

desired beampattern to suppress the target-like interference [9, 10].

Colocated CS-MIMO radar can achieve either the same

localisation performance as traditional methods with significantly

fewer measurements or significantly improved performance with

the same number of measurements [10]. However, the assumed

target sparsity decreases in the presence of clutter, interference and

jamming, and the parameter estimation performance would

degrade severely. Yu et al. [10] proposed a Capon-like

beamforming method to suppress clutter in the context of colocated

CS-MIMO radar. However, when the clutter covariance and

directions-of-arrival (DOAs) of interfering sources and/or jammers

are unknown, conventional methods such as the Capon

beamformer [10] and the minimum variance distortionless response

beamformer of [11, 12] fail. Without prior knowledge of the clutter

covariance function, the Capon beamforming does not enable to

improve the DOA estimation performance of colocated CS-MIMO

radar. Under the assumption of known spatial sector-of-interest

(SOI), where the targets are likely to be located, Hassanien and

Vorobyov [4] proposed a transmit beamspace energy focusing

technique for colocated MIMO radars with application to DOA

finding for multiple targets and simultaneously suppressing the

interference and clutter out of the spatial SOI. Spheroidal

sequences were employed to design the transmit beamspace matrix

so that the ratio of the energy radiated within the desired spatial

SOI to the total radiated energy was maximised [4]. For a better

DOA estimation of colocated MIMO radar in the presence of

interference and jamming, Li et al. [13] proposed three reduced

dimension beamspace matrices to achieve reasonable tradeoffs

between the desired SOI source distortion and the out-of-sector

interference/jamming (possibly in-sector) attenuation. However,

the beamspace matrix was developed by minimising the output

power of the total received signal followed by a transformation.

The transformed target signal would be also attenuated while

minimising the power of the transformed interference and

jamming. Similarly, Tao et al. [9] proposed a spatial filter

measurement matrix (SFMM) design method for colocated CS-

MIMO radar based on the pass-band beamforming for spatial target

sectors and lower gain beamforming for out-of-sector areas to

suppress the possible interference or clutter. However, it can be

seen that the attenuation levels for spatial areas out-of-sector are

limited. Only if the interference-to-noise ratio (INR) or clutter-to-

NR (CNR) is small, the out-of-sector interference or clutter would

be suppressed. Moreover, the case of possible jamming in the SOI

is not considered. Actually, with the development of electronic

countermeasure (ECM), the important target is often protected by

the countermeasure system to prevent radars from operating as well

as possible.

As an important ECM, active jamming often takes the form of

high-power transmission and blocks the radar receive system with

highly concentrated energy [14]. In view of the closed element

spacing of transmit and receive arrays, colocated CS-MIMO radars

can be blocked by active jamming. Moreover, terrain-scattered

jamming also is a typical active jamming example, which occurs in

a dispersive manner when the ground reflects the power

transmitted by a high-power jammer [11, 15]. It shows up as a

IET Radar Sonar Navig., 2018, Vol. 12 Iss. 7, pp. 702-710

702

下载后可阅读完整内容，剩余8页未读，立即下载

weixin_38697557

粉丝: 8
资源: 921

并置CS-MIMO雷达中SFMM设计的干扰与干扰联合抑制策略

稀疏随机阵列配置下的CS-MIMO雷达感知矩阵构建

FDA-MIMO雷达波束形成抗干扰理论深度解析

时间反转FDA-MIMO雷达的联合目标测距角估计方法

CS-MIMO雷达稀疏随机阵列的优化设计

基于混沌随机滤波器的 CS-MIMO 雷达测量矩阵优化设计

共置压缩感测MIMO雷达中用于干扰/干扰抑制的空间滤波器测量矩阵设计

基于ADMM改进的低截获FDA-MIMO雷达发射波束设计.docx

SFMM design in colocated CS-MIMO radar for jamming and interference joint suppression

MIMO雷达波束综合研究学报-MIMO雷达波束综合研究.pdf

TDM-MIMO雷达信号处理仿真

最新资源