基于LS-SVM的宽带接收前端非线性补偿算法

黄家露; 王文涛; 周莲; 李姝; 杨波; 杨阳; 刘昭涛; 高星寒; 宋海平

doi:10.12263/DZXB.20211637

您当前的位置：

首页 >

文章列表页 >

基于LS-SVM的宽带接收前端非线性补偿算法

学术论文 | 更新时间：2025-12-08

- 基于LS-SVM的宽带接收前端非线性补偿算法
- Nonlinearity Mitigation Method Based on LS-SVM for Wide-Band Receiver
- 电子学报 2023年51卷第6期页码：1500-1509
- 作者机构：
  
  中国北方车辆研究所信息与控制技术部，北京 100072
- 作者简介：
  
  [ "黄家露男，1987年生，湖北咸宁人.博士，中国北方车辆研究所工程师.主要研究方向为非线性电路理论、软件无线电接收机、坦克装甲车辆主动干扰防护技术等.E-mail： huangjialu1987@126.com" ]
  [ "王文涛男，1979年生，河南濮阳人.硕士，中国北方车辆研究所研究员.主要研究方向为坦克装甲车辆主动防护系统设计等.E-mail： wangwentao8987@163.com" ]
  [ "周莲女，1982年生，重庆人.硕士，中国北方车辆研究所研究员.主要研究方向为坦克装甲车辆主动防护系统设计等.E-mail： lotus_zl@163.com" ]
  [ "李姝女，1975年生，湖北宜昌人.硕士，中国北方车辆研究所研究员.主要研究方向为主动防护领域系统总体设计、系统流程控制实现、探测识别等.E-mail： yanyuan66@sina.com" ]
  [ "杨波女，1995年生，河北石家庄人.硕士，中国北方车辆研究所工程师.主要研究方向为坦克装甲车辆主动拦截技术等.E-mail： bobo6585801@163.com" ]
  [ "杨阳男，1989年生，山西太原人.硕士，中国北方车辆研究所高级工程师.主要研究方向为坦克装甲车辆综合防护技术等.E-mail： cashios0615@163.com" ]
  [ "刘昭涛男，1983年生，山东滕州人.硕士，中国北方车辆研究所副研究员.主要研究方向为主动拦截技术.E-mail： 13810215268@139.com" ]
  [ "高星寒女，1995年生，河南洛阳人.硕士，中国北方车辆研究所工程师助理工程师.主要研究方向为电子与通信、信号处理及其应用和嵌入式软件开发等.E-mail： yjysnxl@sina.cn" ]
  [ "宋海平（通讯作者）男，1979年生，山西人.博士，中国北方车辆研究所研究员、部门总工.主要研究方向为坦克装甲车辆主动防护系统设计等." ]
- 基金信息：
- DOI：10.12263/DZXB.20211637
  中图分类号： TN92;
- 收稿：2021-12-09，
  
  修回：2022-05-22，
  
  纸质出版：2023-06-25
- 稿件说明：
移动端阅览
黄家露,王文涛,周莲等.基于LS-SVM的宽带接收前端非线性补偿算法[J].电子学报,2023,51(06):1500-1509.

HUANG Jia-lu,WANG Wen-tao,ZHOU Lian,et al.Nonlinearity Mitigation Method Based on LS-SVM for Wide-Band Receiver[J].ACTA ELECTRONICA SINICA,2023,51(06):1500-1509.
黄家露,王文涛,周莲等.基于LS-SVM的宽带接收前端非线性补偿算法[J].电子学报,2023,51(06):1500-1509. DOI： 10.12263/DZXB.20211637.

HUANG Jia-lu,WANG Wen-tao,ZHOU Lian,et al.Nonlinearity Mitigation Method Based on LS-SVM for Wide-Band Receiver[J].ACTA ELECTRONICA SINICA,2023,51(06):1500-1509. DOI： 10.12263/DZXB.20211637.

摘要

针对目前常用的基于参数化非线性模型（Parameterized Nonlinear Model，PNM）的补偿算法存在易陷入局部最小值，导致补偿性能不稳的问题，该文提出了基于最小二乘支持向量机（Least Squares Support Vector Machine，LS-SVM）的宽带接收前端非线性补偿算法.该算法基于减谱-时频变换法（Spectrum Reduction Algorithm based on Time-Frequency Conversion，SRA-TFC）盲分离接收前端输出信号中的大功率基波信号和其他小功率信号，并以此作为LS-SVM逆模型的训练输入-输出样本对.引入最小二乘支持向量回归（Least Squares Support Vector Regression，LS-SVR）算法高精度拟合接收前端非线性逆模型.通过以宽带接收前端的输出信号为测试样本消除其非线性失真分量.仿真与实测结果表明：该算法可使宽带接收前端的无杂散失真动态范围（Spurs-Free-Dynamic-Range，SFDR）提高约20 dB，较基于PNM的补偿算法提高了约5 dB.

Abstract

To address the problem that the commonly used compensation algorithms based on parametric nonlinear model (PNM) are prone to fall into local minima

leading to unstable compensation performance

a nonlinear compensation algorithm for broadband receive front ends based on least squares support vector machine (LS-SVM) is proposed. The algorithm blindly extracts the high-power fundamental signal and other low-power signals from the receiver output signal based on the reduced-spectrum-time-frequency transform (SRA-TFC) method

and use them as the training input-output sample pairs of the LS-SVM inverse model. The inverse model is then fitted with high accuracy by least squares support vector regression (LS-SVR) algorithm. The output signal of the wideband receiver is used as the test sample to eliminate its nonlinear distortion components. The simulation and measurement results display that the algorithm can improve the spurious free dynamic range (SFDR) of the wideband receiver by about 20 dB and it is increased by 5 dB compared with those methods based on PNM.

关键词

Keywords

references

KAKKAVAS G , TSITSEKLIS K , KARYOTIS V , et al . A software defined radio cross-layer resource allocation approach for cognitive radio networks: From theory to practice [J]. IEEE Transactions on Cognitive Communications and Networking , 2020 , 6 ( 2 ): 740 - 755 .

BAZRAFSHAN A , TAHERZADEH-SANI M , NABKI F . An analog LO harmonic suppression technique for SDR receivers [J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems , 2019 , 27 ( 1 ): 182 - 192 .

PINI G , MANSTRETTA D , CASTELLO R . Analysis and design of a 260-MHz RF bandwidth 22-dBm OOB-IIP 3 mixer-first receiver with third-order current-mode filtering TIA [J]. IEEE Journal of Solid-State Circuits , 2020 , 55 ( 7 ): 1819 - 1829 .

DOGANCAY K . Blind compensation of nonlinear distortion for bandlimited signals [J]. IEEE Transactions on Circuits and Systems I: Regular Papers , 2005 , 52 ( 9 ): 1872 - 1882 .

ALLÉN M , MARTTILA J , VALKAMA M , et al . Digital full-band linearization of wideband direct-conversion receiver for radar and communications applications [C]// 2015 49th Asilomar Conference on Signals, Systems and Computers . Pacific Grove : IEEE , 2016 : 1361 - 1368 .

PENG L , MA H . Design and implementation of software-defined radio receiver based on blind nonlinear system identification and compensation [J]. IEEE Transactions on Circuits and Systems I: Regular Papers , 2011 , 58 ( 11 ): 2776 - 2789 .

LIANG P , HONG M . Blind identification and real-time calibration of memory nonlinearity based on RLS algorithm [C]// 2010 4th International Conference on Signal Processing and Communication Systems . Gold Coast : IEEE , 2011 : 1 - 9 .

LIU Y J . Adaptive blind postdistortion and equalization of system impairments for a single-channel concurrent dual-band receiver [J]. IEEE Transactions on Microwave Theory and Techniques , 2017 , 65 ( 1 ): 302 - 314 .

VALKAMA M , SHAHED HAGH GHADAM A , ANTTILA L , et al . Advanced digital signal processing techniques for compensation of nonlinear distortion in wideband multicarrier radio receivers [J]. IEEE Transactions on Microwave Theory and Techniques , 2006 , 54 ( 6 ): 2356 - 2366 .

WARD E , MULGREW B . Mitigation of cross-modulation effects in radar receivers with memory [C]// 2020 IEEE International Radar Conference (RADAR) . Washington : IEEE , 2020 : 856 - 859 .

PECCARELLI N , PECK Z , LANDON GARRY J . Analysis and mitigation of receiver induced nonlinearities on pulse-Doppler radars [C]// 2020 IEEE International Radar Conference (RADAR) . Washington : IEEE , 2020 : 333 - 338 .

SUYKENS J K , VANDEWALLE J . Least Squares support vector machine classifiers [J]. Neural Processing Letters , 1999 , 9 ( 3 ): 293 - 300 .

相征 , 张太镒 , 孙建成 . 基于最小二乘支持向量机的非线性系统建模 [J]. 系统仿真学报 , 2006 , 18 ( 9 ): 2684 - 2687 .

XIANG Z , ZHANG T Y , SUN J C . Modelling of nonlinear systems based on recurrent least squares support vector machines [J]. Journal of System Simulation , 2006 , 18 ( 9 ): 2684 - 2687 . (in Chinese)

FAZIL , KAYTEZ , . Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines [J]. International Journal of Electrical Power & Energy Systems , 2015 , 67 : 431 - 438 .

JINGANG , ZHAO , . Combination of LS-SVM algorithm and JC method for fragility analysis of deep-water high piers subjected to near-field ground motions [J]. Structures , 2020 , 24 : 282 - 295 .

KISI O , PARMAR K S . Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution [J]. Journal of Hydrology , 2016 , 534 : 104 - 112 .

ZHU B Z , WEI Y M . Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology [J]. Omega , 2013 , 41 ( 3 ): 517 - 524 .

SARBJIT S , KULWINDER S P , SIDHU J S , et al . Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries [J]. Chaos , Solitons & Fractals, 2020 , 139 : 110086 .

BAESENS B , VIAENE S , VAN GESTEL T , et al . An empirical assessment of kernel type performance for least squares support vector machine classifiers [C]// Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies . Brigllton : IEEE , 2002 : 313 - 316 .

VAN GESTEL T , SUYKENS J A K , BAESENS B , et al . Benchmarking least squares support vector machine classifiers [J]. Machine Learning , 2004 , 54 ( 1 ): 5 - 32 .

THOMAS S , PILLAI G N , PAL K . Prediction of peak ground acceleration using ?—SVR, ν-SVR and Ls-SVR algorithm [J]. Geomatics, Natural Hazards and Risk , 2017 , 8 ( 2 ): 177 - 193 .

CASTRO-GARCIA R , AGUDELO O M , SUYKENS J A K . Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification [J]. International Journal of Control , 2019 , 92 ( 4 ): 908 - 925 .

LIU Y , JI Y , LIU D , et al . A new method for runoff prediction error correction based on LS-SVM and a 4D copula joint distribution [J]. Journal of Hydrology , 2021 , 598 : 126223 .

WAN Z Y , WANG Q D , LIU D C , et al . Discovery of ester lubricants with low coefficient of friction on material surface via machine learning [J]. Chemical Physics Letters , 2021 , 773 : 138589 .

WASON H , KUMAR R , HERRMANN F J , et al . Source separation via SVD-free rank minimization in the hierarchical semi-separable representation [C]// SEG Technical Program Expanded Abstracts 2014 . Houston : SEG , 2014 : 601 - 605 .

TAN Z C , MEUCK M , DU X H , et al . A fully isolated delta-sigma ADC for shunt based current sensing [J]. IEEE Journal of Solid-State Circuits , 2016 , 51 ( 10 ): 2232 - 2240 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据