Low-sidelobe waveform design for integrated radar-communication systems based on frequency diversity array

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC IET Signal Processing Pub Date : 2023-01-31 DOI:10.1049/sil2.12186
Haozheng Wu, Biao Jin, Zhenkai Zhang, Zhuxian Lian, Zhaoyang Xu, Xiaohua Zhu
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Abstract

Frequency diversity array (FDA) radar can provide full spatial coverage with stable gains within a pulse duration. Based on the FDA, the integrated radar-communication system can perform multi-directional communication and whole-space detection. However, the embedded communication bits disrupt the correlation of the transmitting waveform of each element. Correspondingly, the range sidelobe level (SLL) of the multi-dimensional ambiguity function increases significantly. To address this issue, a low-sidelobe waveform for integrated radar-communication systems based on the FDA was designed. Two techniques based on the subarray time delay are employed to reduce the SLL in range dimension. Both methods, however, lower the angular resolution. Thus, a tangent FM signal as the baseband waveform to improve the angular resolution was selected. Simultaneously, the received signal processing methods of radar and communication was designed. The performances of the designed waveform are verified by analysing the multi-dimensional ambiguity function and the bit error rate. The simulation results reveal that the proposed method can maintain a good radar target detection capability and satisfy the communication function.

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基于频率分集阵列的雷达通信系统低旁瓣波形设计
频率分集阵列(FDA)雷达可以在脉冲持续时间内提供具有稳定增益的全空间覆盖。基于美国食品药品监督管理局,集成雷达通信系统可以执行多方向通信和全空间探测。然而,嵌入的通信比特破坏了每个元件的发送波形的相关性。相应地,多维模糊度函数的距离旁瓣电平(SLL)显著增加。为了解决这个问题,设计了一种基于FDA的集成雷达通信系统的低旁瓣波形。采用两种基于子阵列时延的技术来减小SLL的距离维度。然而,这两种方法都会降低角度分辨率。因此,选择正切FM信号作为基带波形以提高角分辨率。同时,设计了雷达和通信的接收信号处理方法。通过分析多维模糊函数和误码率,验证了所设计波形的性能。仿真结果表明,该方法能够保持良好的雷达目标检测能力,满足通信功能要求。
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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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