Volume 53 Issue 2
Apr.  2024
Article Contents

XU Rui, WEN Ruiming, HUANG Zihan, LI Gang, WEN Guangjun. Design of Dual-Function Radar Communication System Based on Delay Breakpoint Mapping FMCW[J]. Journal of University of Electronic Science and Technology of China, 2024, 53(2): 210-218. doi: 10.12178/1001-0548.2023171
Citation: XU Rui, WEN Ruiming, HUANG Zihan, LI Gang, WEN Guangjun. Design of Dual-Function Radar Communication System Based on Delay Breakpoint Mapping FMCW[J]. Journal of University of Electronic Science and Technology of China, 2024, 53(2): 210-218. doi: 10.12178/1001-0548.2023171

Design of Dual-Function Radar Communication System Based on Delay Breakpoint Mapping FMCW

doi: 10.12178/1001-0548.2023171
  • Received Date: 2023-06-14
  • Rev Recd Date: 2023-07-21
  • Available Online: 2024-04-01
  • Publish Date: 2024-03-30
  • Dual-Functional Radar-Communication (DFRC) system based on Frequency Modulated Continuous Wave (FMCW) has the characteristics of high time-frequency utilization, strong anti-Doppler performance and low range sidelobe. The existing FMCW-DFRC waveform designs generate dual-function deterioration and increase the link complexity of communication receivers. A DFRC system based on Delayed Breakpoint Mapping (DBM)-FMCW is designed to modulate data by mapping the location of the delayed breakpoint in the chirp period and the phase between the divided clusters. After carrier mixing and undersampling, dechirping, chip alignment and data demodulation are realized in the digital domain. In the radar processing link, a Breakpoint Area Deletion and Splicing (BADS) scheme is designed to make the radar performance of DFRC consistent with that of unmodulated FMCW. The simulation results show that, compared with the uncompensated scheme, the BADS scheme can reduce the sidelobe amplitude of the image by about 37 dB and is not affected by the modulation data. Compared with the communication performance of existing DFRC schemes based on chirped waveform, DBM-FMCW reduces the symbol error rate.
  • [1] 梁兴东, 李强, 王杰, 等. 雷达通信一体化技术研究综述[J]. 信号处理, 2020, 254(10): 5-17.

    LIANG X D, LI Q, WANG J, et al. Joint wireless communication and radar sensing: Review and future prospects[J]. Journal of Signal Processing, 2020, 254(10): 5-17.
    [2] LIU F, MASOUROS C, PETROPULU A P, et al. Joint radar and communication design: Applications, state-of-the-art, and the road ahead[J]. IEEE Transactions on Communications, 2020, 68(6): 3834-3862. doi:  10.1109/TCOMM.2020.2973976
    [3] FENG Z, FANG Z, WEI Z, et al. Joint radar and communication: A survey[J]. China Communications, 2020, 17(1): 1-27.
    [4] CHENG Z, HE Z, LIAO B. Hybrid beamforming design for OFDM dual-function radar-communication system[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(6): 1455-1467.
    [5] HAN L, WU K. Multifunctional transceiver for future intelligent transportation systems[J]. IEEE Transactions on Microwave Theory and Techniques, 2011, 59(7): 1879-1892. doi:  10.1109/TMTT.2011.2138156
    [6] 叶启彬, 胡泽林, 黄驿轩, 等. 5G NR通信雷达一体化系统雷达性能分析[J]. 电子科技大学学报, 2022, 51(6): 847-855.

    YE Q B, HU Z L, HUANG Y X, et al. Radar performance analysis of 5G NR RadCom system[J]. Journal of University of Electronic Science and Technology of China, 2022, 51(6): 847-855.
    [7] WANG C, ZHU X. A novel receiver design based on frft for frequency diversity array radar[C]//2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP). [S.l.]: IEEE, 2021: 611-614.
    [8] REICHARDT L, STURM C, GRUNHAUPT F, et al. Demonstrating the use of the IEEE 802.11 p car-to-car communication standard for automotive radar[C]//2012 6th European Conference on Antennas and Propagation (EUCAP). Prague: IEEE, 2012: 1576-1580.
    [9] ZHENG X, YANG G, QIAN Y, et al. PAPR reduction with Compressive Sensing for Joint Radar and Communication System[C]//2022 7th International Conference on Signal and Image Processing (ICSIP). Suzhou: IEEE, 2022: 696-700.
    [10] SARAC U B, GUVENSEN G M. On the effects of range-doppler processing for target detection in OFDM-based RadCom[J]. IEEE Communications Letters, 2023, 27(7): 1864-1868.
    [11] 张翼. 基于OFDM的雷达通信一体化波形设计[D]. 成都: 电子科技大学, 2018.

    ZHANG Y. Integrated waveform design of radar communication based on OFDM[D]. Chengdu: University of Electronic Science and Technology of China, 2018.
    [12] 胡泽林, 叶启彬, 黄驿轩, 等. 基于频分多址的多用户OFDM通信雷达一体化方案[J]. 电子科技大学学报, 2023, 52(2): 196-202.

    HU Z L, YE Q B, HUANG Y X, et al. Multiuser OFDM Radcom scheme based on FDMA[J]. Journal of University of Electronic Science and Technology of China, 2023, 52(2): 196-202.
    [13] GU Y, ZHANG L, ZHOU Y, et al. Embedding communication symbols into radar waveform with orthogonal FM scheme[J]. IEEE Sensors Journal, 2018, 18(21): 8709-8719. doi:  10.1109/JSEN.2018.2868542
    [14] MA D, SHLEZINGER N, HUANG T, et al. FRaC: FMCW-based joint radar-communications system via index modulation[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(6): 1348-1364. doi:  10.1109/JSTSP.2021.3118219
    [15] ZHANG J A, LIU F, 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
    [16] LIU A, HUANG Z, LI M, et al. A survey on fundamental limits of integrated sensing and communication[J]. IEEE Communications Surveys & Tutorials, 2022, 24(2): 994-1034.
    [17] YE X, LI Y, DU Y, et al. Design of data transmission system for speed measurement radar between ARM and FPGA based on embedded Linux[C]//2016 IEEE International Conference on Signal and Image Processing (ICSIP). Beijing: IEEE, 2016: 343-346.
    [18] HUANG T, SHLEZINGER N, XU X, et al. MAJoRCom: A dual-function radar communication system using index modulation[J]. IEEE Transactions on Signal Processing, 2020, 68: 3423-3438. doi:  10.1109/TSP.2020.2994394
    [19] GOU W, WANG L, LIU Y, et al. Generation of phase-coded LFM signals based on Fourier domain mode-locked optoelectronic oscillator[J]. Journal of Lightwave Technology, 2023, 41(19): 6142-6148.
    [20] 章琴, 刘以安. 一种新的雷达辐射源信号识别方法[J]. 微型机与应用, 2015(12): 48-50.

    ZHANG Q, LIU Y A. A new radar emitter signal recognition method[J]. Microcomputer & Its Applications, 2015(12): 48-50.
    [21] 闵博. 基于小波变换的雷达脉内调制特征分选识别[D]. 成都: 电子科技大学, 2020.

    MIN B. Radar in-pulse modulation feature sorting and recognition based on wavelet transform[D]. Chengdu: University of Electronic Science and Technology of China, 2020.
    [22] GU M X, LEE M C, LIU Y S, et al. Design and analysis of frequency hopping-aided FMCW-based integrated radar and communication systems[J]. IEEE Transactions on Communications, 2022, 70(12): 8416-8432. doi:  10.1109/TCOMM.2022.3220333
    [23] DOU Z. Radar-communication integration based on MSK-LFM spread spectrum signal[J]. International Journal of Communications, Network and System Sciences, 2017, 10(8): 108-117. doi:  10.4236/ijcns.2017.108B012
    [24] NI Y, WANG Z, HUANG Q, et al. High throughput rate-shift integrated system for joint radar-communications[J]. IEEE Access, 2019, 7: 78228-78238. doi:  10.1109/ACCESS.2019.2920003
    [25] ZHANG Y, LI Q, HUANG L, et al. A modified waveform design for radar-communication integration based on LFM-CPM[C]//2017 IEEE 85th Vehicular Technology Conference. Sydney: IEEE, 2017: 1-5.
    [26] GU Y, ZHANG L, ZHOU Y, et al. Waveform design for integrated radar and communication system with orthogonal frequency modulation[J]. Digital Signal Processing, 2018, 83: 129-138. doi:  10.1016/j.dsp.2018.08.014
    [27] SHI Q, ZHANG T, YU X, et al. Waveform designs for joint radar-communication systems with OQAM-OFDM[J]. Signal Processing, 2022, 195: 108462. doi:  10.1016/j.sigpro.2022.108462
    [28] ZHAO Z, JIANG D. A novel integrated radar and communication waveform based on LFM signal[C]//2015 IEEE 5th International Conference on Electronics Information and Emergency Communication. Beijing: IEEE, 2015: 219-223.
    [29] NOWAK M, WICKS M, ZHANG Z, et al. Co-designed radar-communication using linear frequency modulation waveform[J]. IEEE Aerospace and Electronic Systems Magazine, 2016, 31(10): 28-35. doi:  10.1109/MAES.2016.150236
    [30] MU J, OUYANG W, JING Z, et al. Energy-efficient interference cancellation in integrated sensing and communication scenarios[J]. IEEE Transactions on Green Communications and Networking, 2023, 7(1): 370-378.
    [31] JAMIL M, ZEPERNICK H J, PETTERSSON M I. On integrated radar and communication systems using Oppermann sequences[C]//MILCOM 2008-2008 IEEE Military Communications Conference. San Diego: IEEE, 2008: 1-6.
    [32] JIANG M, LIAO G, YANG Z, et al. Tunable filter design for integrated radar and communication waveforms[J]. IEEE Communications Letters, 2020, 25(2): 570-573.
    [33] TEMIZ M, HORNE C, PETERS N J, et al. An experimental study of radar-centric transmission for integrated sensing and communications[J]. IEEE Transactions on Microwave Theory and Techniques, 2023, 71(7): 3203-3216.
    [34] CHEN X, WANG X, XU S, et al. A novel radar waveform compatible with communication[C]//2011 International Conference on Computational Problem-Solving (ICCP). Chengdu: IEEE, 2011: 177-181.
    [35] 张华亮, 酒乐. 一种基于2FSK的调制解调系统设计[J]. 电子技术与软件工程, 2016(16): 45.

    ZHANG H L, JIU L. Design of a modulation and demodulation system based on 2FSK[J]. Electronic Technology and Software Engineering, 2016(16): 45.
    [36] 黄驿轩, 胡苏, 叶启彬, 等. 基于连续波的通信雷达一体化距离处理分析[J]. 电子科技大学学报, 2022, 51(5): 688-693.

    HUANG Y X, HU S, YE Q B, et al. Range processing analysis for RadCom based on continuous-wave[J]. Journal of University of Electronic Science and Technology of China, 2022, 51(5): 688-693.
    [37] SWERLING P. Probability of detection for fluctuating targets[J]. IRE Transactions on Information Theory, 1960, 6(2): 269-308. doi:  10.1109/TIT.1960.1057561
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Design of Dual-Function Radar Communication System Based on Delay Breakpoint Mapping FMCW

doi: 10.12178/1001-0548.2023171

Abstract: Dual-Functional Radar-Communication (DFRC) system based on Frequency Modulated Continuous Wave (FMCW) has the characteristics of high time-frequency utilization, strong anti-Doppler performance and low range sidelobe. The existing FMCW-DFRC waveform designs generate dual-function deterioration and increase the link complexity of communication receivers. A DFRC system based on Delayed Breakpoint Mapping (DBM)-FMCW is designed to modulate data by mapping the location of the delayed breakpoint in the chirp period and the phase between the divided clusters. After carrier mixing and undersampling, dechirping, chip alignment and data demodulation are realized in the digital domain. In the radar processing link, a Breakpoint Area Deletion and Splicing (BADS) scheme is designed to make the radar performance of DFRC consistent with that of unmodulated FMCW. The simulation results show that, compared with the uncompensated scheme, the BADS scheme can reduce the sidelobe amplitude of the image by about 37 dB and is not affected by the modulation data. Compared with the communication performance of existing DFRC schemes based on chirped waveform, DBM-FMCW reduces the symbol error rate.

XU Rui, WEN Ruiming, HUANG Zihan, LI Gang, WEN Guangjun. Design of Dual-Function Radar Communication System Based on Delay Breakpoint Mapping FMCW[J]. Journal of University of Electronic Science and Technology of China, 2024, 53(2): 210-218. doi: 10.12178/1001-0548.2023171
Citation: XU Rui, WEN Ruiming, HUANG Zihan, LI Gang, WEN Guangjun. Design of Dual-Function Radar Communication System Based on Delay Breakpoint Mapping FMCW[J]. Journal of University of Electronic Science and Technology of China, 2024, 53(2): 210-218. doi: 10.12178/1001-0548.2023171
  • 双功能雷达通信(Dual-Functional Radar- Communication, DFRC)旨在通过同一硬件收发链路,同时完成雷达探测功能与通信功能[1-2]。相比于传统单一的雷达或者通信设备,其更易集成化和高效利用频谱,并已成为研究热点[3-7]。根据波形类型,DFRC主要分为两类。第一类利用通信信号波形完成雷达功能[8],正交频分多路复用(Orthogonal Frequency Division Multiplex, OFDM)波形是其中的主要研究方向,可实现高速率通信性能。然而,OFDM存在高峰值平均比问题,且循环前缀会导致高旁瓣的雷达波形[9-13]。第二类将通信信息按照特定的调制方式嵌入现有雷达波形中,在保证雷达性能的同时实现通信功能[14-16]。调频连续波(Frequency Modulated Continuous Wave, FMCW)雷达系统中使用的啁啾波形,由于其具有良好的频谱利用率和抗多普勒能力以及低旁瓣特性,成为DFRC的主要研究方向。

    基于FMCW的DFRC通信系统,采用宽带啁啾(Chirp)信号以实现更高的雷达测距精度[17-21]。通信接收端采用高采样速率的模数转换器(Analog to Digital Converter, ADC)[22]或产生同步的啁啾相干信号进行信号解调[23-29]。文献[22]将FMCW与FH序列映射和相移键控法(Phase Shift Keying, PSK)相结合,采用优化算法抑制对波形的雷达性能和通信性能的恶化。但跳频序列的随机性,使通信接收端的ADC采样率需要达到FMCW信号带宽的2倍。基于最小频移键控[23-24]、连续相位调制[25]和频域正交函数[26]的多种调频方案与啁啾信号结合,在原波形上产生小幅频率变化,可以通过同步的无调制啁啾相干信号进行解调。基于正交调幅[27]或相位调制[28-29]的一体化波形同样可以采用啁啾相干信号解调。然而,高采样速率的ADC或宽频啁啾信号的生成,极大增加了通信接收端的链路复杂度和硬件成本,使接收设备难以实现低功耗、低成本和小型化。

    在雷达性能方面,距离域图像是重要的指标之一。由于通信数据调制,DFRC将产生高峰值旁瓣电平(Peak Sidelobe Level, PSL)[30-31]和距离旁瓣调制(Range Sidelobe Modulation, RSM)[32]。前者使雷达反射信号中强目标的旁瓣掩盖弱目标,导致漏检;后者使FMCW周期间距离域输出的幅值和相位被不同跳频序列改变,导致相干积累的恶化。文献[13, 29, 33-34]通过设置加权系数来控制调制产生的变化幅度,减少数据调制对雷达性能的影响。但这些方案都不能使旁瓣幅值与无调制FMCW方案一致,也不能完全消除RSM。同时,通过削弱通信调制系数来实现抑制,会加剧通信的误符号率(Symbol Error Rate, SER)。

    本研究针对现有DFRC系统中通信接收链路复杂度高和雷达性能受数据调制恶化的问题,提出了一种延迟断点映射(Delayed Breakpoint Mapping, DBM)FMCW波形,并基于该波形设计了基于欠采样的通信解调链路和基于断点区域删除与拼接(Breakpoint Area Deletion and Splicing, BADS)的雷达处理链路,以实现通信链路的简化和雷达性能的优化。DBM-FMCW波形在特定点产生频率延迟跳变,通过断点位置的映射和被分割的簇间的相位进行数据调制。

    • 啁啾信号又称为线性调频信号,是指频率随时间而线性改变的信号。设FMCW的每个啁啾周期为$ T $,带宽为$ B $。DBM-FMCW波形发送链路图与波形时频参数原理图如图1图2所示。

      首先,将$ R $比特的原始数据进行串并转换,并分解为$ {R_D} $$ {R_P} $比特。同时,将一个原始啁啾周期在时域上划分为$ Q $个码片。单码片的周期为$ {T_c} $,带宽为$ {B_c} $。然后,设置$ b[x] = 1,2,\cdots,Q $$ x = 1,2,\cdots,X $为跳变位置调制序列,由$ {R_D} $比特映射产生,其中$ X $为跳变点总数量,且$ b[x] $互不相等。跳变点所在码片和其之后码片的瞬时频率,在原有啁啾信号的基础上产生$ - {B_c}/2 $偏移。此时,原始啁啾周期时域被拓展至$ {T}^{\prime }=(Q+X/2){T}_{c} $,且被$ X $个跳变点划分为$ (X + 1) $个簇。最后,根据$ {R_P} $比特的调制数据对每个簇进行多元phase shift keying (PSK)调制。跳变点位置的总集合内的元素总和为组合运算$ {\text{C}}_Q^X $。随着跳变点数量的增加,元素总和的增量将显著减少,因此本文以$ X = 2 $为例进行波形设计介绍。按照前述规则,设置各簇的频率偏移为:

      $ s(q,t) $为第$ q $个码片的时域波形,其表示为:

      式中,$ {f_0} $为射频啁啾起始频率,即信号频段的最小频率;$ \mu = B/T $为调频斜率;$ \varOmega [q] = {{\rm{e}}^{{\rm{j}}\theta [q]}} $为第$ q $个码片的相位调制项。当信号采用$ A $元相位调制,$ \theta [q] = 0,2{\text π}/A,\cdots,2{\text π}(A - 1)/A $为对应的相位调制参数,且同一簇内的$ \varOmega [q] $保持不变。将所有码片合并,发射信号波形表示为:

      式中,$ u(t) = 1,{\text{ }}0 \leqslant t \leqslant 1 $为矩形窗函数。采用$ {\mathbb{N}_ + } $表示正整数集合,若$ \varOmega [q] $恒为1且啁啾的单码片时宽带宽积参数满足:

      波形在包括跳变点在内的任意$ q{T_c} $时刻相位连续。根据DBM-FMCW波形设计原理,每个啁啾的调制比特数$ R $为:

      式中,$ \left\lfloor \cdot \right\rfloor $为向下取整运算。

    • 本文设计了一种基于DBM-FMCW波形的欠采样通信解调链路,该方案的通信接收解调链路图与时频参数原理图如图3图4所示。

      图4图2的波形为输入,且其中的数字域的离散时序用对应时长的连续时序表示。首先,使用单频载波对信号进行混频,并以$ {B_c} $为采样速率进行采样。然后以$ {T_c} $为周期产生啁啾数字相干信号,与采样后的信号进行相干。接着,对码片进行短时傅里叶变换(Short-Time Fourier Transform, STFT),得到数字相干时延估计。基于该时延对数字相干信号进行调整后,再次对信号进行相干和STFT。最后,通过获得的跳变点位置信息和相位参数信息,还原通信信号。设置通信端天线处的接收信号为:

      式中,$ \alpha $为信号强度;$ l $为收发端相对时延。使用频率为$ {f_0} $的载波对信号进行混频。然后对中频信号以$ {B_c} $为采样速率进行采样。此时,单码片的采样点数为$ {H_c} = {B_c}{T_c} = {r_{TB}} = 2\eta ,\eta \in {\mathbb{N}_ + } $,采样后的信号为:

      式中,$ h = 1,2,\cdots,(Q + 1){H_c} $为采样序列;$ L = l/{B_c} $是离散相干时延,为方便计算取与该值最接近的整数;$ \phi $为上下变频信号相位差。设置数字相干信号为:

      该相干信号具有以下特性:

      即在本文设置的$ {r_{TB}} $和通信采样频率条件下,数字相干波形相位连续,且以$ {H_c} $为周期循环。由于数字相干信号持续产生且具有周期性,可设接收信号与相干信号的离散时延$ L < {H_c} $$ P[h] $进行数字相干后,输出为:

      容易证明,当2个相位连续的信号进行相干处理,其输出信号相位连续。因此,若$ \varOmega [k] $恒为1,$ O[h] $保持相位连续。以每个码片的$ {H_c} $个采样点为周期,对信号进行STFT。根据式(1)中频率偏移项$ {f_c} $,STFT得到的信号能量集中在$ L $$ {H_c}/2 + L $的离散频率处。在首次数字相干处理后,通过将接收数字信号$ P[h] $的时域移动$ L $,实现数字码片对齐。再次进行数字相干处理,更新输出为:

      $ w[q] $$ O'[q,h] $中第$ q $个码片FFT处理后峰值所在离散频率位置,根据式(1),得到:

      信号能量集中在0和$ {H_c}/2 $处的2个离散频率。通过频率变化检测跳变码片位置,完成$ {R_D} $比特的映射调制解调。通过对簇间相位调制项$ \varOmega [q] $的检测,完成$ {R_P} $比特相位调制解调。最后合并解调数据,通过映射表还原发送数据。设低通滤波器输出的加性高斯噪声的方差为$ \sigma $,此时信噪比$ {\rm{SNR}} = {a^2}/2{\sigma ^2} $。二阶频移键控(Binary Frequency Shift Keying, BFSK)的误码率[35]为:

      $ A $元PSK的误码率为:

      在多调制方式结合的应用中,联合调制解调误码率往往受误码率较大的调制解调方案影响。本文将在后续的仿真章节中给出PSK的调制参数选择。

      表1 对比了多方案通信解调链路对模拟相干信号生成和ADC采样速率的性能需求。其中$ A' $为正整数,与对应方案的频率调制设置有关。与文献[22]中的采样率$ 2B $相比,本文方案的采样率为$ B/K $。与文献[23-29]中啁啾的相干信号相比,本文的相干信号采用了单频载波。因此,基于DBM-FMCW波形的欠采样通信解调链路,极大降低了产生相干信号和ADC的性能要求。

      方案模拟相干信号ADC采样速率/sps
      本文载波B/K
      文献[22]载波2B
      文献[23-26]同步啁啾A'B/K
      文献[27-29]同步啁啾2B/K
    • 本章基于DBM-FMCW波形,设计了对应的雷达处理链路方案,该雷达信号处理链路图和BADS方案时频参数原理图分别如图5图6所示。

      图6同样以图2的波形为输入,且其中的数字域的离散时序用对应时长的连续时序表示。与常规FMCW处理相比,链路增加了跳变区域删除与拼接模块,即BADS。目标反射信号与发射信号混频后进行采样。然后,在断点处删除$ {T_c}/2 $对应的采样点,将该区域前后信号进行拼接。最后,对信号进行2D-DFT处理,得到距离−速度域目标探测信息。雷达系统采用由$ Z $个啁啾周期构成的FMCW帧进行探测。根据雷达原理,设置第$ z $个啁啾周期内目标反射信号为:

      式中,$ \xi (t) $为雷达信号中的加性高斯噪声;$ \varphi [k] $$ \varepsilon [k] $分别为目标$k $的相位偏移系数和衰减系数;$ \psi [k] = 2V[k]{f_0}/c $为速度$V[k] $引起的目标多普勒频移;$ \tau [k] = 2G[k]/c $为距离$G[k] $引起的往返时延,$ c $表示光速。假设中心频率相对于工作带宽足够大,即$ {f_0} \gg B $。然后,假设目标在帧持续时间内的移动距离远小于雷达与目标之间的距离。最后,假设目标时延相对于啁啾周期较小,即$ \tau [k] < {T_c} $,且啁啾斜率满足$ \mu \tau [k] \gg \psi [k] $,因此在啁啾周期内可以忽略多普勒效应。上述条件在FMCW雷达系统中是普遍存在的[22]。首先,将接收信号与发送信号进行混频。由于信号存在频率跳变,需要分簇对信号进行分析。当$ 0 \leqslant t < ({b_1} - 1){T_c} $,混频后的输出信号为:

      式中,$ \varLambda [k] $是目标参数导致的固定相位;$ \varGamma [z,k] $是多普勒引起的相位偏移,与$ z $耦合。对相干信号输出进行射频的低通滤波,通带设置为后续的采样速率$ {f_s} $的1/2。之后,以$ {f_s} $为采样率进行采样。特别地,对$ {f_s} $的约束如下:

      采样后,时序$ 0 \leqslant t < ({b_1} - 1){T_c} $对应的采样点区间为$ 0 < i \leqslant ({b_1} - 1){I_s} $。此时,数字信号为:

      在第二个簇中,当$ ({b_1} - 1){I_s} + \delta < i \leqslant ({b_2} - 1){I_s} $,数字信号为:

      对比式(18)与式(19),两者产生相偏,且该相偏与目标距离耦合。将$ [({b_1} - 1){I_s} + 1,({b_1} - 1){I_s} + \delta ] $内的$ \delta $个采样点删除,并拼接前后的数字信号,可实现前后数据的相位连续。然后,对第三个簇中$ [({b_2} - 1){I_s} + 1,({b_2} - 1){I_s} + \delta ] $的采样点做相同处理。由于使用的时刻均以雷达发射信号为基准,因此可以假设雷达系统已知断点所在的时刻。信号采样点经过删减和拼接后,表示为:

      式中,$ i' = 1,2,\cdots,K{I_s} $为删除和拼接处理后的采样点序列。此时,雷达信号在快时间域还原为周期为$ T $且带宽为$ B $的无调制FMCW波形。对快时间采样点$ i' $进行FFT处理,并带入目标距离参数$ \tau [k] = 2H[k]/c $,得到距离-DFT:

      式中,$ n = 1,2,\cdots,Q{I_s} $为距离门。之后对慢时间$ z $进行后续的2D-DFT,并带入目标距离参数$ \psi [q] = 2V[q]{f_0}/c $,得到速度-DFT:

      式中,$ m = 1,2,\cdots,Z $为速度门。通过恒虚警检测(Constant False Alarm Detection, CFAR),获得第$ k $个目标对应峰值所在距离门序号$ N[k] $与速度门序号$ M[k] $,进而对目标距离与速度的进行测算:

      通过链路分析,在雷达信号处理过程中,虽然删除了1个$ {T_c} $对应的数字信号,最终的目标探测结果与常规FMCW一致[36]。即BADC处理后的DJM-FMCW信号完全消除了通信信号调制带来的影响。

    • 本文对提出的DBM-FMCW波形系统进行仿真分析,并与现有研究在雷达性能和通信性能方面进行比较。利用MATLAB平台,对具有加性白噪声的雷达和通信仿真场景进行了评估。

    • 参考现有研究,假设雷达与通信接收机之间的无线信道是理想的莱斯衰落信道。将雷达和通信接收系统模拟带通滤波器后信号的信噪比设为SNR[22]。仿真参数设置如表2所示[13, 29]

      参数数值
      中心频率f0/GHz20
      单帧啁啾数Z128
      单啁啾断点数X2
      码片数Q16
      码片带宽BC/MHz8
      码片周期TC/us8
      调频斜率μ/MHz·us−11
      码片时宽带宽积rTB64
      啁啾周期T/us128
      通信采样率fS/Ms·s−18
      雷达采样率$f'_S $/Ms·s−12
    • 以单个目标的距离域图像为指标,验证基于DBM-FMCW波形的BADS方案的雷达探测性能。假设雷达反射场景服从Swerling-I[37]。目标位置设为100 m,信号幅度归一化。

      首先,验证BADS方案对距离域图像的改善,选择基于DBM-FMCW波形的无补偿方案和未调制FMCW波形方案的距离域图像进行比较。同时设置断点位置为{2, 10}和{7, 12}的调制数据为条件1和条件2。为区别完全重叠的2个方案的曲线,在接收端信号加入适量的噪声。图7 a显示了无加窗处理的3种方案的图像。可以看出,基于DBM-FMCW波形的BADS方案的距离曲线与未调制FMCW方案完全相同。与无补偿方案相比,旁瓣幅度减小约4 dB,且不受调制数据的影响。图7 b显示了经过汉宁窗处理的3种方案的图像。BADS方案的输出旁瓣幅值降至−60 dB,比其他两种方案的输出副瓣幅值低约37 dB。仿真结果表明,BADS方案改善了数据调制下的雷达性能,其输出与无调制FMCW方案一致。

      然后,验证基于DBM-FMCW的BADS方案与现有的基于FMCW的方案的雷达性能比较。图8为BADS方案和FH-FMCW-PSK[22]方案在无噪声条件下的距离域图像。BADS方案不会产生文献[22]方案中周期性的小峰值旁瓣,进而降低了PSL。仿真结果同样验证了文献[22]方案中的主瓣和周期小峰副瓣会受到跳频序列值的影响,而BADS方案始终保持相同的波形,避免了RSM。

      最后,验证基于DBM-FMCW的BADS方案距离−多普勒成像性能。设置2个目标的距离和速度分别为{100 m, 0 m/s}和{50 m, 1 m/s}。使用随机通信数值下的DBM-FMCW波形,对FMCW帧内的$ Z $个啁啾信号进行2D-DFT处理,得到距离−多普勒2D热力图如图9所示。通过BADS方案处理的DBM-FMCW波形可以进行几乎无损的相干累积,与同样参数下无调制FMCW波形成像性能一致。

    • DBM-FMCW波形采用数据映射,因此使用SER作为通信性能的评价指标,进行100 000次蒙特卡罗仿真,验证不同波形在相同通信场景下的通信性能。

      首先在DBM-FMCW进行单调制,并对比通信接收端的SER。图10 a显示了在对FMCW进行无调相的BFSK和无频率断点的BPSK、QPSK、8PSK这4种调制方案下,通信链路的SER曲线。可看到,BFSK的SER介于QPSK和8PSK之间。综合考虑SER和方案实现难度,本文使用QPSK作为DBM-FMCW调制中簇间的相位调制方案,在不显著影响误码率的前提下,提升通信速率。

      为了与DBM-FMCW波形的抗噪性能进行比较,选择CE-OFDM-chirp调制解调方案[13]和PSK-chirp调制解调方案[29]进行比较。上述两种参考文献均采用减少通信调制权值的方法,使DFRC波形在雷达性能恶化与SER之间获得折中性能。文献[13]采用调频比为0.015,文献[29]采用相位调制为10度的PSK-chirp。图10 b为不同信噪比条件下不同通信方案的信噪比对比。当SER为$ {10^{ - 5}} $时,基于DBM-FMCW的方案比CE-OFDM-chirp方案低约3 dB,比PSK-chirp方案低约2 dB。

      综合表1和仿真结果,DBM-FMCW波形系统在通信接收端实现了较低的链路复杂度,而且在雷达性能和通信SER性能上都优于现有的方案[13, 29]

    • 本文提出了一种DBM-FMCW波形系统,以解决现有的基于FMCW的DFRC系统不能抑制双系统性能恶化和通信接收链路复杂度高的问题。首先,DBM-FMCW波形通过断点位置的映射和被分割的簇间的相位进行数据调制。然后,通信端采用载波解调和欠采样,并在数字域实现去啁啾,降低了接收端相干信号生成和ADC的性能需求,实现了较低的链路复杂度。随后,在雷达处理链路中,相干和采样处理后,在时域上通过BADS方案优化了雷达性能。最后,通过仿真验证,基于DBM-FMCW波形的BADS方案的距离域图像与无调制的FMCW一致,且不受调制数据的影响。与无补偿的方案相比,BADS方案距离图像的旁瓣幅度减小约37 dB。此外,与现有基于啁啾波形的DFRC方案相比,DBM-FMCW实现了3 dB通信SER优化。本文所提供的DBM-FMCW波形系统的理论研究和仿真结果,在多种DFRC应用场景中具有较大的指导意义。

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