面向多目标多用户的雷达通信一体化恒模波形设计

朱锦锟; 金添; 唐宇航; 宋勇平; 刘伟

doi:10.11999/JEIT230827

面向多目标多用户的雷达通信一体化恒模波形设计

doi: 10.11999/JEIT230827

国防科技大学电子科学学院长沙 410073

详细信息

作者简介:
朱锦锟：男，博士生，研究方向为雷达通信一体化

金添：男，教授，研究方向为超宽带成像与目标识别

唐宇航：男，硕士生，研究方向为雷达通信一体化

宋勇平：男，讲师，研究方向为穿墙成像、雷达成像、雷达目标检测

刘伟：男，副教授，研究方向为通信信号处理、5G/6G通信技术

通讯作者:
金添　tianjin@nudt.edu.cn

中图分类号: TN959.1
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出版历程
- 收稿日期: 2023-08-20
- 修回日期: 2023-11-02
- 网络出版日期: 2023-11-14
- 刊出日期: 2023-11-28

Multi-User and Multi-Target Constant Waveform Design of Dual-Function Radar and Communication System

School of Electronic Science, National University of Defense Technology, Changsha 410073, China

摘要

摘要: 双功能雷达通信一体化系统可以使硬件和频谱资源得到有效利用，是解决当前无线频谱资源紧张问题的一种有效途径。该文针对同时多目标探测与多用户通信场景，以雷达接收回波的信干噪比(SINR)为指标保障雷达的探测性能，采用通信多用户干扰(MUI)作为通信指标保证通信传输性能。与此同时，为保证发射端功率放大器工作在饱和区域，增加了波形恒模的约束条件。该文通过对波形以及滤波器的联合优化，提出了一种在满足通信MUI功率一定的情况下，最大化雷达多目标探测回波最小信干噪比(SINR)的波形设计优化模型。针对此优化问题，采用了交替迭代优化的方法来求解此问题。仿真结果表明，所设计的波形在多目标探测以及多用户通信场景下，通过调整通信MUI功率门限，可以在保证通信MUI功率性能前提下，实现对多目标回波最小SINR的优化。
- 雷达通信一体化 /
- 多输入多输出 /
- 波形设计 /
- 恒模
Abstract: The Dual-Functional Radar-Communication (DFRC) system can effectively utilize hardware and spectrum resources, which is an effective way to solve the shortage of the wireless spectrum resource. In this paper, for the scenario of simultaneous multi-target detection and multi-user communication, the Signal-to-Interference-Noise Ratio (SINR) of the radar received echo is used as an index to guarantee the detection performance, and the Multi-User Interference (MUI) of the communication is used as a communication index to guarantee the transmission performance of communication. Meanwhile, constraints on the constant modulus of the waveform are added to ensure that the power amplifier at the transmitter operates in the saturation region. The paper proposes a mathematical model for waveform design that maximizes the minimum SINR of the radar multi-target echoes while satisfying a certain amount of the communication MUI energy by jointly optimizing the waveforms as well as the filters. For this optimization problem, the method of alternating iterative optimization is adopted to solve the problem. Simulation results show that the designed waveform ensures both radar and communication related performance in simultaneous multi-target detection and multi-user communication scenarios.
- Dual-Functional Radar-Communication (DFRC) /
- Multi-Input Multi-Output (MIMO) /
- Waveform design /
- Constant modulus

HTML全文

图 1 双功能雷达通信系统模型示意图

下载: 全尺寸图片幻灯片

图 2 不同目标数量时算法实现的目标函数值随着迭代次数变化图

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图 3 发射波束图

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不同 ${\eta }$ 值下的SINR变化图

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算法1　求解问题(21)算法流程
输入：初始化变量 ${{\boldsymbol{s}}^{(0)}}$ , ${e^{(0)}}$ , ${\beta ^{(0)}}$ , ${\delta _s}$ 和 ${\delta _\beta }$ 。
输出：问题式(38)的解 $s$ ；
第1步： $j = 0$ ；
第2步： ${{\boldsymbol{s}}^{(j - 1)}}{\text{ = }}{{\boldsymbol{s}}^{(0)}},\;{e^{(j - 1)}}{\text{ = }}{e^{(0)}}$ ；
第3步：求解问题式(38)得到 $({{\boldsymbol{s}}^{(j)}},{e^{(j)}},{\beta ^{(j)}})$ ；
第4步：若 ${\left\\| {{\beta ^{(j)}}} \right\\|_1} < \tau$ ，则输出 ${\boldsymbol{s}} = {{\boldsymbol{s}}^{(j)}}$ ，否则继续下一步
第5步： ${{\boldsymbol{s}}^{(0)}}{\text{ = }}{{\boldsymbol{s}}^{(j)}},\;{e^{(0)}}{\text{ = }}{e^{(j)}},\;j{\text{ = }}j + 1$ 返回第2步

下载: 导出CSV

算法2　交替迭代优化算法流程
输入：初始化变量 ${s^{(0)}}$ , ${e^{(0)}}$ , ${\beta ^{(0)}}$ , ${\delta _s}$ , ${\delta _\beta }$ 和 $\kappa$ 。
输出：问题式(16)的解 ${s^{()}}$ , $\left\{ {{\boldsymbol{w}}_m^} \right\}_{m = 1}^M$ 。
第1步： $c = 0$ ，构 ${{\chi }_m}\left( {{{\boldsymbol{s}}^{(0)}}} \right)$ , ${{\boldsymbol{\varLambda}} _m}\left( {{{\boldsymbol{s}}^{(0)}}} \right)$ ；
第2步：求解问题式(19)得到 $\left\{ {{\boldsymbol{w}}_m^{(0)}} \right\}_{m = 1}^M$ ；
第3步： $c = c + 1$ ；
第4步：构造 ${{{\boldsymbol{\varOmega}} }_m}\left( {{\boldsymbol{w}}_m^{(c - 1)}} \right)$ , ${{\boldsymbol{\varGamma}}_m}\left( {{\boldsymbol{w}}_m^{(c - 1)}} \right)$ ，求解问题式(21) 　得到 ${{\boldsymbol{s}}^{(c)}}$ ；
第5步：构造 ${{\chi }_m}\left( {{{\boldsymbol{s}}^{(c)}}} \right)$ , ${{\boldsymbol{\varLambda}} _m}\left( {{{\boldsymbol{s}}^{(c)}}} \right)$ ，求解问题式(19)，得到　 $\left\{ {{\boldsymbol{w}}_m^{(c)}} \right\}_{m = 1}^M$
第6步：计算 ${\min _{\forall m}}{\text{SIN}}{{\text{R}}_m}\left( {{\boldsymbol{s}},{{\boldsymbol{w}}_m}} \right)$
若 $\|\min _{_{\forall m}}^j{{{\mathrm{SINR}}} _m}\left( {{{\boldsymbol{s}}^{(c)}},{\boldsymbol{w}}_m^{(c)}} \right) - \min _{_{\forall m}}^{(c - 1)}{{{\mathrm{SINR}}} _m}$ 　　 $\left( {{{\boldsymbol{s}}^{({c} - 1)}},{\boldsymbol{w}}_m^{(c - 1)}} \right)\|{\text{ }} < {\text{ }}\kappa$ ，
输出 ${s^{()}}$ = ${s^{(c)}}$ , $\left\{ {{\boldsymbol{w}}_m^} \right\}_{m = 1}^M = \left\{ {{\boldsymbol{w}}_m^{(c)}} \right\}_{m = 1}^M$ ，否则返回第3步。

下载: 导出CSV

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