采用联合收发设计抑制主瓣多假目标欺骗性干扰

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Yipin Liu , Lei Yu , Yinsheng Wei
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引用次数: 0

摘要

提出了一种采用联合收发设计抑制主瓣多假目标欺骗干扰的方法。在发射端采用多输入多输出(MIMO)雷达波形设计,并采用元脉冲编码(EPC)对接收信号混合矩阵进行调制,从而提高了目标与干扰之间的盲分离性。接收端采用盲源分离改进的盲源提取(BSE)技术进行处理。通过建立合适的优化模型,采用动态优劣子群粒子群优化算法,得到目标回波的最优提取向量,实现对干扰的抑制。该方法将接收端盲信号处理理论与发射端波形设计理论相结合,优势互补。数值结果表明,与传统方法相比,该方法提供了一系列性能增强,传统方法仅依赖于接收端或发射端。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mainlobe multiple false targets deceptive jamming suppression via joint transmit-receive design
A method for suppressing mainlobe multiple false targets deceptive jamming using joint transmit-receive design is proposed in this paper. We utilize multiple-input multiple-output (MIMO) radar waveform design at the transmitting end and employ element-pulse coding (EPC) to modulate the received signal mixing matrix, thereby enhancing the blind separability between the target and jamming. The receiving end adopts blind source extraction (BSE) technology improved by blind source separation (BSS) for processing. By formulating an appropriate optimization model and employing the dynamic superior-inferior subgroup particle swarm optimizer algorithm, the optimal extraction vector for the target echo is obtained to achieve jamming suppression. This method combines the blind signal processing theory at the receiving end with the waveform design theory at the transmitting end, thereby utilizing complementary advantages. The numerical results demonstrate that the proposed method offers a spectrum of performance enhancements over conventional approaches, which are dependent exclusively on either the receiving or transmitting terminal.
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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