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CLC number: TN95

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2023-08-29

Cited: 0

Clicked: 1850

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jianxin YI

https://orcid.org/0000-0003-0585-0445

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.8 P.1214-1230

http://doi.org/10.1631/FITEE.2200260


High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network


Author(s):  Baoxiong XU, Jianxin YI, Feng CHENG, Ziping GONG, Xianrong WAN

Affiliation(s):  Electronic Information School, Wuhan University, Wuhan 430072, China

Corresponding email(s):   jessiexu@whu.edu.cn, jxyi@whu.edu.cn, cwing@whu.edu.cn

Key Words:  Deep feedforward neural network, Filter layer, Passive radar, Target tracking, Tracking accuracy


Baoxiong XU, Jianxin YI, Feng CHENG, Ziping GONG, Xianrong WAN. High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(8): 1214-1230.

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author="Baoxiong XU, Jianxin YI, Feng CHENG, Ziping GONG, Xianrong WAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
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pages="1214-1230",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200260"
}

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%T High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network
%A Baoxiong XU
%A Jianxin YI
%A Feng CHENG
%A Ziping GONG
%A Xianrong WAN
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T1 - High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network
A1 - Baoxiong XU
A1 - Jianxin YI
A1 - Feng CHENG
A1 - Ziping GONG
A1 - Xianrong WAN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
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EP - 1230
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200260


Abstract: 
In radar systems, target tracking errors are mainly from motion models and nonlinear measurements. When we evaluate a tracking algorithm, its tracking accuracy is the main criterion. To improve the tracking accuracy, in this paper we formulate the tracking problem into a regression model from measurements to target states. A tracking algorithm based on a modified deep feedforward neural network (MDFNN) is then proposed. In MDFNN, a filter layer is introduced to describe the temporal sequence relationship of the input measurement sequence, and the optimal measurement sequence size is analyzed. Simulations and field experimental data of the passive radar show that the accuracy of the proposed algorithm is better than those of extended Kalman filter (EKF), unscented Kalman filter (UKF), and recurrent neural network (RNN) based tracking methods under the considered scenarios.

基于深度前馈神经网络的多基地外辐射源雷达高精度目标跟踪

徐宝兄,易建新,程丰,龚子平,万显荣
武汉大学电子信息学院,中国武汉市,430072
摘要:在雷达系统中,目标跟踪误差主要来自运动模型和非线性量测。在评估跟踪算法时,其跟踪精度是主要衡量准则。为提高跟踪精度,本文将跟踪问题表述为从量测到目标状态的回归模型,提出一种基于改进深度前馈神经网络(MDFNN)的跟踪算法。所提MDFNN跟踪算法引入一种滤波层来描述输入量测序列的时序关系,并分析了最优量测序列长度。仿真和实测的外辐射源雷达数据测试表明,在所考虑的场景下,所提算法跟踪精度优于基于扩展卡尔曼滤波器(EKF)、无迹卡尔曼滤波器(UKF)和递归神经网络(RNN)的跟踪方法。

关键词:深度前馈神经网络;滤波层;外辐射源雷达;目标跟踪;跟踪精度

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

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