Full Text:   <225>

Summary:  <28>

CLC number: TN953

On-line Access: 2024-06-04

Received: 2023-07-09

Revision Accepted: 2024-06-04

Crosschecked: 2023-11-24

Cited: 0

Clicked: 326

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhenkai ZHANG

https://orcid.org/0000-0003-2439-0923

Xiaoke SHANG

https://orcid.org/0009-0002-5137-2835

Yue XIAO

https://orcid.org/0009-0008-9343-5367

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.5 P.742-754

http://doi.org/10.1631/FITEE.2300462


Target parameter estimation for OTFS-integrated radar and communications based on sparse reconstruction preprocessing


Author(s):  Zhenkai ZHANG, Xiaoke SHANG, Yue XIAO

Affiliation(s):  Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Corresponding email(s):   zhangzhenkai@just.edu.cn

Key Words:  Integrated radar and communications system, Orthogonal time–, frequency space, Target parameter estimation, Sparse reconstruction, Weighted subspace fitting


Zhenkai ZHANG, Xiaoke SHANG, Yue XIAO. Target parameter estimation for OTFS-integrated radar and communications based on sparse reconstruction preprocessing[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(5): 742-754.

@article{title="Target parameter estimation for OTFS-integrated radar and communications based on sparse reconstruction preprocessing",
author="Zhenkai ZHANG, Xiaoke SHANG, Yue XIAO",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="25",
number="5",
pages="742-754",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300462"
}

%0 Journal Article
%T Target parameter estimation for OTFS-integrated radar and communications based on sparse reconstruction preprocessing
%A Zhenkai ZHANG
%A Xiaoke SHANG
%A Yue XIAO
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 5
%P 742-754
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300462

TY - JOUR
T1 - Target parameter estimation for OTFS-integrated radar and communications based on sparse reconstruction preprocessing
A1 - Zhenkai ZHANG
A1 - Xiaoke SHANG
A1 - Yue XIAO
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 5
SP - 742
EP - 754
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300462


Abstract: 
orthogonal time–;frequency space (OTFS) is a new modulation technique proposed in recent years for high Doppler wireless scenes. To solve the parameter estimation problem of the OTFS-integrated radar and communications system, we propose a parameter estimation method based on sparse reconstruction preprocessing to reduce the computational effort of the traditional weighted subspace fitting (WSF) algorithm. First, an OTFS-integrated echo signal model is constructed. Then, the echo signal is transformed to the time domain to separate the target angle from the range, and the range and angle of the detected target are coarsely estimated by using the sparse reconstruction algorithm. Finally, the WSF algorithm is used to refine the search with the coarse estimate at the center to obtain an accurate estimate. The simulations demonstrate the effectiveness and superiority of the proposed parameter estimation algorithm.

基于稀疏重构预处理的OTFS雷达通信一体化目标参数估计算法

张贞凯,商晓可,肖悦
江苏科技大学海洋学院,中国镇江市,212003
摘要:正交时频空间(orthogonal time-frequency space, OTFS)是近年来针对高多普勒无线场景提出的一种新的调制技术。针对OTFS雷达通信一体化系统的参数估计问题,本文提出一种基于稀疏重构预处理的参数估计方法,以降低传统加权子空间拟合(weighted subspace fitting,WSF)算法的计算量。首先,构建了OTFS一体化回波信号模型。然后,对回波信号进行时域变换,将目标角度与距离分离,利用稀疏重建算法对检测目标的距离和角度进行粗估计。最后,利用WSF算法以粗估计为中心对搜索进行细化,得到准确的估计。仿真实验证明了所提参数估计算法的有效性和优越性。

关键词:雷达通信一体化系统;正交时频空;目标参数估计;稀疏重构;加权子空间拟合

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Dokhanchi SH, Mysore BS, Mishra KV, et al., 2019. A mmWave automotive joint radar-communications system. IEEE Trans Aerosp Electron Syst, 55(3):1241-1260.

[2]Farhang A, RezazadehReyhani A, Doyle LE, et al., 2018. Low complexity modem structure for OFDM-based orthogonal time frequency space modulation. IEEE Wirel Commun Lett, 7(3):344-347.

[3]Franken GEA, Nikookar H, van Genderen P, 2006. Doppler tolerance of OFDM-coded radar signals. European Radar Conf, p.108-111.

[4]Gaudio L, Kobayashi M, Caire G, et al., 2020a. On the effectiveness of OTFS for joint radar parameter estimation and communication. IEEE Trans Wirel Commun, 19(9):5951-5965.

[5]Gaudio L, Kobayashi M, Caire G, et al., 2020b. Joint radar target detection and parameter estimation with MIMO OTFS. IEEE Radar Conf, p.1-6.

[6]Hadani R, Rakib S, Tsatsanis M, et al., 2017a. Orthogonal time frequency space modulation. IEEE Wireless Communications and Networking Conf, p.1-6.

[7]Hadani R, Rakib S, Molisch AF, et al., 2017b. Orthogonal time frequency space (OTFS) modulation for millimeter-wave communications systems. IEEE MTT-S Int Microwave Symp, p.681-683.

[8]Hakobyan G, Yang B, 2018. A novel intercarrier-interference free signal processing scheme for OFDM radar. IEEE Trans Veh Technol, 67(6):5158-5167.

[9]Hassanien A, Amin MG, Aboutanios E, et al., 2019. Dual-function radar communication systems: a solution to the spectrum congestion problem. IEEE Signal Process Mag, 36(5):115-126.

[10]Keskin MF, Wymeersch H, Alvarado A, 2021. Radar sensing with OTFS: embracing ISI and ICI to surpass the ambiguity barrier. IEEE Int Conf on Communications Workshops, p.1-6.

[11]Li SY, Yuan WJ, Liu C, et al., 2022. A novel ISAC transmission framework based on spatially-spread orthogonal time frequency space modulation. IEEE J Sel Areas Commun, 40(6):1854-1872.

[12]Li YC, Wang XD, Ding ZG, 2020. Multi-target position and velocity estimation using OFDM communication signals. IEEE Trans Commun, 68(2):1160-1174.

[13]Liu CW, Liu SH, Mao ZH, et al., 2021. Low-complexity parameter learning for OTFS modulation based automotive radar. IEEE Int Conf on Acoustics, Speech and Signal Processing, p.8208-8212.

[14]Ottersten B, Viberg M, Kailath T, 1992. Analysis of subspace fitting and ML techniques for parameter estimation from sensor array data. IEEE Trans Signal Process, 40(3):590-600.

[15]Ottersten B, Viberg M, Stoica P, et al., 1993. Exact and large sample maximum likelihood techniques for parameter estimation and detection in array processing. In: Haykin S, Litva J, Shepherd TJ (Eds.), Radar Array Processing. Springer, Berlin, p.99-151.

[16]Patole SM, Torlak M, Wang D, et al., 2017. Automotive radars: a review of signal processing techniques. IEEE Signal Process Mag, 34(2):22-35.

[17]Rahman ML, Zhang JA, Huang XJ, et al., 2020. Joint communication and radar sensing in 5G mobile network by compressive sensing. IET Commun, 14(22):3977-3988.

[18]Raviteja P, Phan KT, Hong Y, et al., 2019a. Orthogonal time frequency space (OTFS) modulation based radar system. IEEE Radar Conf, p.1-6.

[19]Raviteja P, Hong Y, Viterbo E, et al., 2019b. Practical pulse-shaping waveforms for reduced-cyclic-prefix OTFS. IEEE Trans Veh Technol, 68(1):957-961.

[20]Sanson JB, Tomé PM, Castanheira D, et al., 2020. High-resolution delay-Doppler estimation using received communication signals for OFDM radar-communication system. IEEE Trans Veh Technol, 69(11):13112-13123.

[21]Shi WT, Zhang QF, He CB, et al., 2019. Taylor expansion MUSIC method for joint DOD and DOA estimation in a bistatic MIMO array. Front Inform Technol Electron Eng, 20(6):842-848.

[22]Surabhi GD, Ramachandran MK, Chockalingam A, 2019a. OTFS modulation with phase noise in mmWave communications. IEEE 89th Vehicular Technology Conf, p.1-5.

[23]Surabhi GD, Augustine RM, Chockalingam A, 2019b. Peak-to-average power ratio of OTFS modulation. IEEE Commun Lett, 23(6):999-1002.

[24]Wang XJ, Zhang ZK, Najafabadi HE, 2021. Joint range and velocity estimation for integration of radar and communication based on multi-symbol OFDM radar pulses. IET Radar Sonar Navig, 15(5):533-545.

[25]Xue JR, Wang D, Du SY, et al., 2017. A vision-centered multi-sensor fusing approach to self-localization and obstacle perception for robotic cars. Front Inform Technol Electron Eng, 18(1):122-138.

[26]Yan JK, Pu WQ, Zhou SH, et al., 2020. Collaborative detection and power allocation framework for target tracking in multiple radar system. Inform Fus, 55:173-183.

[27]Zhang FQ, Zhang ZH, Yu WX, et al., 2020. Joint range and velocity estimation with intrapulse and intersubcarrier Doppler effects for OFDM-based RadCom systems. IEEE Trans Signal Process, 68:662-675.

[28]Zhang ZK, Najafabadi HE, Jin B, 2021. Transmit array resource allocation for radar and communication integration system. Measurement, 173:108595.

[29]Zheng L, Wang XD, 2017. Super-resolution delay-Doppler estimation for OFDM passive radar. IEEE Trans Signal Process, 65(9):2197-2210.

[30]Zheng L, Lops M, Eldar YC, et al., 2019. Radar and communication coexistence: an overview: a review of recent methods. IEEE Signal Process Mag, 36(5):85-99.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE