Robust Waveform Design Based on MIMO Multi-Function Radio Frequency Systems under Angle Uncertainties
-
摘要: 基于多输入多输出(MIMO)阵列的综合射频(MFRF)系统通过优化多波形，可以在不同角度辐射不同的信号，进而同时实现多种功能。然而，当存在角度误差时，综合射频系统所合成的信号会出现畸变，这将导致系统性能下降。该文主要考虑存在角度误差时，对综合射频系统鲁棒波形设计算法进行研究。首先，提出基于最小最大化(Min-Max)框架的MIMO阵列多波形优化问题。同时，为减少非线性功放引起的波形失真，对发射波形施加了峰均比(PAPR)约束。为求解此问题，该文提出了基于交替方向乘子法(ADMM)和Majorization-Minimization(MM)方法的波形优化算法。仿真实验结果表明，当存在角度误差时，所提算法能够获得更好的性能。Abstract: Multi-Function Radio Frequency (MFRF) systems based on Multiple-Input-Multiple-Output (MIMO) array can synthesize different signals that can realize multiple functions at different angles via waveform optimization. However, due to the angle uncertainties, the waveforms synthesized by MFRF systems might be distorted and the performance of the systems might degrade as well. The robust waveform design algorithm for MFRF systems under angle uncertainties is studied in this paper. Firstly, a waveform optimization based on Min-Max framework is proposed. Additionally, the Peak-to-Average-Power-Ratio (PAPR) constraint is enforced to reduce the waveform distortion caused by nonlinearity of the power amplifiers. To tackle this problem, an algorithm based on the Alternating Direction Method of Multipliers (ADMM) and Majorization-Minimization (MM) algorithm is derived. Simulation results show that the proposed algorithm can produce signals with different functions in different regions even with angle uncertainties.
-
算法1 基于ADMM算法的综合射频系统鲁棒波形设计算法 输入：阵列天线数 $ {N_{\text{T}}} $，期望信号以及角度误差范围 $ \{ {{\boldsymbol{d}}_k},{\varOmega _k}\} _{k = 1}^K $，发射总能量 $ {e_{\text{T}}} $，峰均比 $ \rho $，惩罚因子 $ \mu $。 输出：发射阵列波形 $ {\boldsymbol{S}} $。 初始化：$ {\boldsymbol{A}} = \sum\nolimits_{k = 1}^K {\sum\nolimits_{m = 1}^{{N_k}} {{{\boldsymbol{a}}_{k,m}}{\boldsymbol{a}}_{k,m}^{\text{H}}} } ,{{\boldsymbol{A}}_{{\text{neg}}}} = {\boldsymbol{A}} - {\lambda _{\max }}({\boldsymbol{A}}){\boldsymbol{I}},l = 0,{{\boldsymbol{S}}^{(l)}},\{ {\boldsymbol{y}}_{k,m}^{(l)},\gamma _{k,m}^{(l)}\} $。 重复： $ q = 0,{{\boldsymbol{S}}^{(l,q)}} = {{\boldsymbol{S}}^{(l)}},{\boldsymbol{b}}_{k,m}^{(l)} = {\boldsymbol{y}}_{k,m}^{(l)} + {\boldsymbol{d}}_k^{\text{*}} + \gamma _{k,m}^{(l)},{{\boldsymbol{B}}^{(l)}} = \sum\nolimits_{k = 1}^K {\sum\nolimits_{m = 1}^{{N_k}} {{{\boldsymbol{a}}_{k,m}}{{({\boldsymbol{b}}_{k,m}^{(l)})}^{\text{H}}}} } $； 重复： $ {{{\tilde {\boldsymbol B}}}^{(l,q)}} = {{\boldsymbol{B}}^{(l)}} - {{\boldsymbol{A}}_{{\text{neg}}}}{{\boldsymbol{S}}^{(l,q)}} $，利用文献[29]中的算法2求解优化问题式(21)， $ q = q + 1 $； 直到收敛 $ {{\boldsymbol{S}}^{(l + 1)}} = {{\boldsymbol{S}}^{(l,q)}} $； $ {\boldsymbol{u}}_{k,m}^{(l + 1)} = {({{\boldsymbol{S}}^{(l + 1)}})^{\text{H}}}{{\boldsymbol{a}}_{k,m}} - {\boldsymbol{d}}_k^{\text{*}} - \gamma _{k,m}^{(l)} $，计算 $ f(z) $的最小值以及最小值所对应的点 $ {z^{(l + 1)}} $, $ {\boldsymbol{y}}_{k,m}^{(l + 1)} = {\boldsymbol{u}}_{k,m}^{(l + 1)} \cdot \min \left( {\sqrt {{z^{(l + 1)}}} /||{\boldsymbol{u}}_{k,m}^{(l + 1)}|{|_2},1} \right) $； $ \gamma _{k,m}^{(l + 1)} = \gamma _{k,m}^{(l)} + {\boldsymbol{y}}_{k,m}^{(l + 1)} - {({{\boldsymbol{S}}^{(l + 1)}})^{\text{H}}}{{\boldsymbol{a}}_{k,m}} - {\boldsymbol{d}}_k^{\text{*}},l = l + 1 $。 直到收敛 
下载: 导出CSV
-
[1] 肖博, 霍凯, 刘永祥. 雷达通信一体化研究现状与发展趋势[J]. 电子与信息学报, 2019, 41(3): 739–750. doi: 10.11999/JEIT180515XIAO Bo, HUO Kai, and LIU Yongxiang. Development and prospect of radar and communication integration[J]. Journal of Electronics&Information Technology, 2019, 41(3): 739–750. doi: 10.11999/JEIT180515 [2] ZHANG J A, LIU Fan, MASOUROS C, et al. An overview of signal processing techniques for joint communication and radar sensing[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(6): 1295–1315. doi: 10.1109/JSTSP.2021.3113120 [3] HUGHES P K and CHOE J Y. Overview of advanced multifunction RF system (AMRFS)[C]. 2000 IEEE International Conference on Phased Array Systems and Technology, Dana Point, USA, 2000. [4] SCOTT R. ONR plans EMC2 technology maturation effort[J]. Jane's International Defense Review:IHS Jane's International Defense Review, 2016, 49: 10. [5] 唐波, 汤俊, 胡元奎. 基于MIMO阵列的综合射频系统技术研究[J]. 信息对抗技术, 2022, 1(1): 62–72. doi: 10.12399/j.issn.2097-163x.2022.01.006TANG Bo, TANG Jun, and HU Yuankui. Multifunction radio frequency systems based on MIMO array[J]. Information Countermeasure Technology, 2022, 1(1): 62–72. doi: 10.12399/j.issn.2097-163x.2022.01.006 [6] TANG Bo and STOICA P. MIMO multifunction RF systems: Detection performance and waveform design[J]. IEEE Transactions on Signal Processing, 2022, 70: 4381–4394. doi: 10.1109/TSP.2022.3202315 [7] MA Dingyou, SHLEZINGER N, HUANG Tianyao, et al. Joint radar-communication strategies for autonomous vehicles: Combining two key automotive technologies[J]. IEEE Signal Processing Magazine, 2020, 37(4): 85–97. doi: 10.1109/MSP.2020.2983832 [8] AXELSSON S R J. Random noise radar/sodar with ultrawideband waveforms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(5): 1099–1114. doi: 10.1109/TGRS.2007.893742 [9] FIDEN W H and CZUBIAK D W. Radar-compatible data link system (U)[P]. USA Patent, US7298313B1, 2007. [10] ELLINGER J, ZHANG Zhiping, WU Zhiqiang, et al. Dual-use multicarrier waveform for radar detection and communication[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(3): 1265–1278. doi: 10.1109/TAES.2017.2780578 [11] 李晓柏, 杨瑞娟, 程伟. 基于频率调制的多载波Chirp信号雷达通信一体化研究[J]. 电子与信息学报, 2013, 35(2): 406–412. doi: 10.3724/SP.J.1146.2012.00567LI Xiaobai, YANG Ruijuan, and CHENG Wei. Integrated radar and communication based on multicarrier frequency modulation chirp signal[J]. Journal of Electronics&Information Technology, 2013, 35(2): 406–412. doi: 10.3724/SP.J.1146.2012.00567 [12] CHEN Xingbo, WANG Xiaomo, ZHANG Zhao, et al. A communication with LFM carrier modulated by MSK scheme[C]. IEEE ICCP2012, Nanjing, China, 2012: 461–463. [13] 刘国岁, 顾红, 苏卫民. 随机信号雷达[M]. 北京: 国防工业出版社, 2005: 45–52.LIU Guosui, GU Hong, and SU Weimin. Random Signal Radar[M]. Beijing: National Defense Industry Press, 2005: 45–52. [14] LI Jian and STOICA P. MIMO Radar Signal Processing[M]. Hoboken: J. Wiley & Sons, 2009: 1–20. [15] 何团, 唐波, 张玉. 基于加权SPICE的MIMO-STAP稀疏恢复算法[J]. 信号处理, 2019, 35(8): 1417–1424. doi: 10.16798/j.issn.1003-0530.2019.08.017HE Tuan, TANG Bo, and ZHANG Yu. Sparse recovery algorithm of MIMO-STAP based on weighted SPICE[J]. Journal of Signal Processing, 2019, 35(8): 1417–1424. doi: 10.16798/j.issn.1003-0530.2019.08.017 [16] WANG Xuyang, TANG Bo, and ZHANG Ming. Optimisation of practically constrained waveforms for Rician target detection with multiple-input-multiple-output radar[J]. IET Radar,Sonar&Navigation, 2022, 16(7): 1116–1130. doi: 10.1049/rsn2.12247 [17] 刘冰凡, 陈伯孝. 基于OFDM-LFM信号的MIMO雷达通信一体化信号共享设计研究[J]. 电子与信息学报, 2019, 41(4): 801–808. doi: 10.11999/JEIT180547LIU Bingfan and CHEN Baixiao. Integration of MIMO radar and communication with OFDM-LFM signals[J]. Journal of Electronics&Information Technology, 2019, 41(4): 801–808. doi: 10.11999/JEIT180547 [18] TANG Bo, WANG Hai, QIN Lilong, et al. Waveform design for dual-function MIMO radar-communication systems[C]. The IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), Hangzhou, China, 2020. [19] LIU Fan, ZHOU Longfei, MASOUROS C, et al. Toward dual-functional radar-communication systems: Optimal waveform design[J]. IEEE Transactions on Signal Processing, 2018, 66(16): 4264–4279. doi: 10.1109/TSP.2018.2847648 [20] SHI Shengnan, WANG Zhaoyi, HE Zishu, et al. Constrained waveform design for dual-functional MIMO radar-communication system[J]. Signal Processing, 2020, 171: 107530. doi: 10.1016/j.sigpro.2020.107530 [21] FAN Linhui, TANG Bo, HUANG Zhongrui, et al. Designing constant-envelope transmissions for secret communications in MISO wiretap channels[J]. IEEE Access, 2019, 7: 17791–17797. doi: 10.1109/ACCESS.2019.2896406 [22] 吴文俊, 唐波, 汤俊, 等. 杂波环境中雷达通信一体化系统波形设计算法研究[J]. 雷达学报, 2022, 11(4): 570–580. doi: 10.12000/JR22105WU Wenjun, TANG Bo, TANG Jun, et al. Waveform design for dual-function radar-communication systems in clutter[J]. Journal of Radars, 2022, 11(4): 570–580. doi: 10.12000/JR22105 [23] TSINOS C G, ARORA A, CHATZINOTAS S, et al. Joint transmit waveform and receive filter design for dual-function radar-communication systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(6): 1378–1392. doi: 10.1109/JSTSP.2021.3112295 [24] TANG Bo, LIU Jun, WANG Hai, et al. Constrained radar waveform design for range profiling[J]. IEEE Transactions on Signal Processing, 2021, 69: 1924–1937. doi: 10.1109/TSP.2021.3065830 [25] BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends ® in Machine Learning , 2010, 3(1): 1–122. doi: 10.1561/2200000016 [26] HUNTER D R and LANGE K. A tutorial on MM algorithms[J]. The American Statistician, 2004, 58(1): 30–37. doi: 10.1198/0003130042836 [27] TANG Bo, TUCK J, and STOICA P. Polyphase waveform design for MIMO radar space time adaptive processing[J]. IEEE Transactions on Signal Processing, 2020, 68: 2143–2154. doi: 10.1109/TSP.2020.2983833 [28] TANG Bo and STOICA P. Information-theoretic waveform design for MIMO radar detection in range-spread clutter[J]. Signal Processing, 2021, 182: 107961. doi: 10.1016/j.sigpro.2020.107961 [29] TROPP J A, DHILLON I S, HEATH R W, et al. Designing structured tight frames via an alternating projection method[J]. IEEE Transactions on Information Theory, 2005, 51(1): 188–209. doi: 10.1109/TIT.2004.839492 [30] BOYD S and VANDENBERGHE L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2004: 21–58. -
图(11) / 表(1)
计量
- 文章访问数: 852
- HTML全文浏览量: 366
- PDF下载量: 205
- 被引次数: 0
下载:
