MIMO DFRC 系统中的能量共享和性能界限:权衡分析

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Ziheng Zheng, Xiang Liu, Tianyao Huang, Yimin Liu, Yonina C. Eldar
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引用次数: 0

摘要

分析多输入多输出双功能雷达通信系统的性能约束是一个基本问题。为此,我们推导了雷达性能约束下的通信功能性能约束。为便于分析,本文考虑了只有一个下行用户和一个雷达目标的简化情况。我们分析了性能约束的特性以及实现约束的相应波形设计策略。当下行链路用户和雷达目标满足特定条件时,我们就能得到性能约束和相应波形设计策略的解析表达式。结果揭示了通信和雷达性能之间的权衡,这主要是由系统中雷达和通信功能之间的能量共享和分配引起的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Energy Sharing and Performance Bounds in MIMO DFRC Systems: A Trade-Off Analysis

Energy Sharing and Performance Bounds in MIMO DFRC Systems: A Trade-Off Analysis

It is a fundamental problem to analyze the performance bound of multiple-input multiple-output dual-functional radar-communication systems. To this end, we derive a performance bound on the communication function under a constraint on radar performance. To facilitate the analysis, in this paper, we consider a simplified situation where there is only one downlink user and one radar target. We analyze the properties of the performance bound and the corresponding waveform design strategy to achieve the bound. When the downlink user and the radar target meet certain conditions, we obtain analytical expressions for the bound and the corresponding waveform design strategy. The results reveal a tradeoff between communication and radar performance, which is essentially caused by the energy sharing and allocation between radar and communication functions of the system.

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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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