信号处理防御策略下的SINR与互信息准则抗干扰技术研究

136 浏览量更新于2024-08-27 收藏 453KB PDF 举报

本文主要探讨了在国防应用中日益复杂且困难的干扰（jamming）问题，特别是针对先进的信号处理反制措施。研究关注的是任务依赖的功率约束下的最优干扰策略，目的是为了使目标难以被探测。文章首先提出了一个新颖的干扰技术，该技术专注于最小化雷达接收到的信号与干扰加噪声比（Signal-to-Interference-plus-Noise Ratio, SINR），对于已知和随机的目标都适用。对于已知目标，通过精心设计的干扰信号，使得其对雷达接收机造成的信号强度降低，从而降低SINR，从而增加识别和跟踪的难度。而面对随机的目标，由于其特性难以完全预测，研究者考虑了不确定性因素，旨在提供一种鲁棒的策略来应对各种可能的情况，确保干扰的有效性。同时，为了进一步损害接收系统的性能，文中还提出了一种旨在干扰参数估计的干扰技术。这可能是通过对关键信号特征进行有针对性的干扰，如频率、幅度或相位，导致接收端的估计算法产生错误。这种干扰策略可能会利用统计学原理，比如选择那些对参数估计影响最大的干扰模式。在实施这些干扰技术时，研究者强调了功率约束的重要性，这意味着干扰信号必须在不被敌方轻易识别的前提下，尽可能地削弱目标信号。这涉及到功率分配的优化问题，可能涉及动态调整干扰功率以适应不同的环境条件和目标特性。本文的研究工作发表于《IET Communications》，并在2013年11月25日收到初稿，经过修订后于2014年2月28日接受。论文的引用DOI为10.1049/iet-com.2013.1054，国际标准连续出版物编号(ISBN)为1751-8628。作者包括来自国防科技大学电子科学与工程学院的Lulu Wang、Hongqiang Wang以及伦敦大学学院电子与电气工程系的Kai-Kit Wong和Paul V. Brennan教授，他们的电子邮件地址供同行交流和引用。这篇研究论文深入探讨了基于SINR和互信息准则的鲁棒干扰技术，它在现代通信和防御环境中具有重要的理论价值和实际应用潜力。通过优化干扰策略，本文的工作有助于提升干扰的有效性和对抗复杂信号处理技术的能力。

Published in IET Communications

Received on 25th November 2013

Revised on 5th February 2014

Accepted on 28th February 2014

doi: 10.1049/iet-com.2013.1054

ISSN 1751-8628

Minimax robust jamming techniques based on

signal-to-interference-plus-noise ratio and mutual

information criteria

Lulu Wang

, Hongqiang Wang

, Kai-Kit Wong

, Paul V. Brennan

College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, People’s

Republic of China

Department of Electronic and Electrical Engineering, University College London, Torrington Place,

London WC1E 7JE, UK

E-mail: wanglulunudt@163.com

Abstract: Jamming in defence applications is increasingly difﬁcult because of advanced signal processing countermeasures. In

this study, task-dependent power-constraint optimal jamming techniques are investigated. To prevent the target from being

detected, a novel jamming technique is proposed to minimise the signal-to-interference-plus-noise ratio (SINR) of the radar

for extended known and stochastic target. To impair the parameter estimation performance, another jamming technique is

proposed which minimises the mutual information (MI) between the radar return and the stochastic target impulse response.

The optimal jamming spectrum is obtained assuming that the jammer has intercepted the radar waveform generally. However,

the precise characteristic of radar waveform is impossible to capture in practice. To model this, it is considered that the

waveform spectrum lies in an uncertainty class conﬁned by known upper and lower bounds. Then, the minimax robust

jamming is designed based on the SINR and MI criteria, which optimises the worst-case performance. Results demonstrate

that the two criteria lead to different optimal jamming results but they have a close relationship from the Shannon’s capacity

equation which provides useful guidance on jamming power allocation for different jamming tasks. However, their behaviour

with respect to the waveform uncertainty is the same.

1 Introduction

In modern electronic warfare, the competition between radar

and jammer is increasingly intense. The anti-jamming

performance of modern radar has been greatly improved by

advanced waveforms, ultra-low sidelobe antennas, coherent

sidelobe cancellers and sidelobe blankers to inhibit signals

entering through its sidelobes [1], as well as by joint

optimisation of the transmit waveform and the receiver ﬁlter

[2, 3]. In these technologies, the radar is assumed to be a

smart sensor and can optimise its transmitted waveform on

a pulse-by-pulse basis. For self-protection, a better jamming

scheme is needed. Motivated by the development of direct

digital synthesis and digital radiofrequency memory, smart

noise jamming waveforms can be used against advanced

threat radars [1]. Thus, smart jamming techniques have

recently drawn much attention [4–6].

Adaptive jamming techniques and the interaction between

adaptive jamming and anti-jamming in communication have

long been a hot research topic [7, 8]. Intelligent jamming

techniques have been proposed for communication systems

in [9, 10]. In radar systems, jamming techniques were

usually studied against synthetic aperture radar (SAR) [11–

13]. However, optimal jamming designs against a general

radar are not well known. In this paper, two novel

task-dependent jamming techniques based on, respectively,

the signal-to-interference-plus-noise ratio (SINR) and

mutual information (MI) criteria against general radar

systems are presented [6]. Minimax robust jamming

techniques are proposed to relax the required prior

knowledge of the jamming techniques.

From the perspective of a general radar, SINR should be

maximised to have the best target detection performance [14,

15], whereas for parameter estimation, the MI between the

reﬂected echo and the target should be enhanced so that the

reﬂected echo contains more information about the target [4,

15, 16]. For waveform diversity, it is commonly assumed

that the radar is smart but the target is dumb. However,

many non-cooperative targets are equipped with

countermeasure systems to prevent a radar from operating

effectively. Therefore, as the opponent of a radar, the jammer

should attempt to reduce the SINR and MI to protect the

target from detection and estimation, respectively [4]. In

Section 2, SINR- and MI-based optimal jamming techniques

are proposed under limited jamming power. It is noted that

the problem we consider in this paper can be viewed as a

jamming unilateral optimisation problem, in which the radar

is not aware of the jamming strategy.

A common assumption in the literature of jamming is that

the reconnaissance system has intercepted the radar waveform

www.ietdl.org

IET Commun., 2014, Vol. 8, Iss. 10, pp. 1859–1867

doi: 10.1049/iet-com.2013.1054

1859

The Institution of Engineering and Technology 2014

下载后可阅读完整内容，剩余8页未读，立即下载

weixin_38698311

粉丝: 9
资源: 925

信号处理防御策略下的SINR与互信息准则抗干扰技术研究

Chinese-Chess-AI-Based-on-MiniMAX-with-Alpha-Beta-Pruning

A Robust Minimax Approach to Classification

Alpha-Beta-pruing-and-Minimax-Algorithms-implementation-in-determining-next-move-for-game-Mancala

Gobang:Java实现基于Α-β剪枝树的智能五子棋。An intelligent gobang game based on Α-β pruning algorithm, implemented in Java

the-caiquan-game-based-on-java.rar_java五子棋_五子棋java

Minimax-algorithm---connect5

MINIMAX-TIC-TAC-TOE

Fast Minimax Path-Based Joint Depth Interpolation

A minimax regret approach to robust beamforming

Gobang game based on Minimax i.zip

最新资源