Full Text:   <1168>

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CLC number: TN95

On-line Access: 2023-08-29

Received: 2022-06-17

Revision Accepted: 2023-01-12

Crosschecked: 2023-08-29

Cited: 0

Clicked: 1075

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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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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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

Reference

[1]Amoozegar F, Sundareshan MK, 1994. Target tracking by neural network maneuver modeling. Proc IEEE Int Conf on Neural Networks, p.3932-3937.

[2]Bengio Y, Simard P, Frasconi P, 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neur Netw, 5(2):157-166.

[3]Bengtsson T, Bickel P, Li B, 2008. Curse-of-dimensionality revisited: collapse of the particle filter in very large scale systems. In: Nolan D, Speed T (Eds.), Probability and Statistics: Essays in Honor of David A. Freedman, Vol. 2. Institute of Mathematical Statistics, Beachwood, USA, p.316-334.

[4]Chin L, 1994. Application of neural networks in target tracking data fusion. IEEE Trans Aerosp Electron Syst, 30(1):281-287.

[5]Choi S, Crouse DF, Willett P, et al., 2014. Approaches to Cartesian data association passive radar tracking in a DAB/DVB network. IEEE Trans Aerosp Electron Syst, 50(1):‍649-663.

[6]Ding J, Chen B, Liu HW, et al., 2016. Convolutional neural network with data augmentation for SAR target recognition. IEEE Geosc Remote Sens Lett, 13(3):364-368.

[7]Doucet A, de Freitas N, Gordon N, 2001. An introduction to sequential Monte Carlo methods. In: Doucet A, Freitas N, Gordon N (Eds.), Sequential Monte Carlo Methods in Practice. Springer-Verlag, New York, USA, p.3-14.

[8]Fatseas K, Bekooij MJG, 2019. Neural network based multiple object tracking for automotive FMCW radar. Int Conf on Radar, p.1-5.

[9]Gao C, Liu HW, Zhou SH, et al., 2018. Maneuvering target tracking with recurrent neural networks for radar application. Int Conf on Radar, p.1-5.

[10]Goodfellow I, Bengio Y, Courville A, et al., 2016. Deep learning architectures. In: Deep Learning, Vol. 1. MIT Press, Cambridge, USA, p.16-21.

[11]Gordon NJ, Salmond DJ, Smith AFM, 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc F-Radar Signal Process, 140(2):107-113.

[12]Griffiths HD, Baker CJ, 2005. Passive coherent location radar systems. Part 1: performance prediction. IEEE Proc-Radar Sonar Navig, 152(3):153-159.

[13]Griffiths HD, Long NRW, 1986. Television-based bistatic radar. IEE Proc F-Commun Radar Signal Process, 133(7):649-657.

[14]Gu JC, Wang HC, Ding GR, et al., 2020. UAV-enabled mobile radiation source tracking with deep reinforcement learning. Int Conf on Wireless Communications and Signal Processing, p.672-678.

[15]Higuchi T, 1997. Monte Carlo filter using the genetic algorithm operators. J Stat Comput Simul, 59(1):1-23.

[16]Hornik K, 1991. Approximation capabilities of multilayer feedforward networks. Neur Netw, 4(2):251-257.

[17]Hornik K, Stinchcombe M, White H, 1989. Multilayer feedforward networks are universal approximators. Neur Netw, 2(5):359-366.

[18]Jiang L, Singh SS, Yıldırım S, 2015. Bayesian tracking and parameter learning for non-linear multiple target tracking models. IEEE Trans Signal Process, 63(21):5733-5745.

[19]Kuschel H, 2013. Approaching 80 years of passive radar. Int Conf on Radar, p.213-217.

[20]Li XR, Jilkov VP, 2003. Survey of maneuvering target tracking. Part I: dynamic models. IEEE Trans Aerosp Electron Syst, 39(4):1333-1364.

[21]Liu H, Liu Z, Liu S, et al., 2019. A nonlinear regression application via machine learning techniques for geomagnetic data reconstruction processing. IEEE Trans Geosci Remote Sens, 57(1):128-140.

[22]Liu JX, Wang ZL, Xu M, 2020. DeepMTT: a deep learning maneuvering target-tracking algorithm based on bidirectional LSTM network. Inform Fus, 53:289-304.

[23]Ma NN, Zhang XY, Zheng HT, et al., 2018. ShuffleNet V2: practical guidelines for efficient CNN architecture design. Proc 15th European Conf on Computer Vision, p.‍122-138.

[24]Malanowski M, Kulpa K, 2012. Two methods for target localization in multistatic passive radar. IEEE Trans Aerosp Electron Syst, 48(1):572-580.

[25]Mazuelas S, Shen Y, Win MZ, 2013. Belief condensation filtering. IEEE Trans Signal Process, 61(18):4403-4415.

[26]Ning XL, Wang F, Fang JC, 2017. An implicit UKF for satellite stellar refraction navigation system. IEEE Trans Aerosp Electron Syst, 53(3):1489-1503.

[27]Oong TH, Isa NAM, 2011. Adaptive evolutionary artificial neural networks for pattern classification. IEEE Trans Neur Netw, 22(11):1823-1836.

[28]Palmer JE, Harms HA, Searle SJ, et al., 2013. DVB-T passive radar signal processing. IEEE Trans Signal Process, 61(8):2116-2126.

[29]Radmard M, Karbasi SM, Nayebi MM, 2013. Data fusion in MIMO DVB-T-based passive coherent location. IEEE Trans Aerosp Electron Syst, 49(3):1725-1737.

[30]Rassalna P, Mishra T, 2020. Target detection, tracking and threat evaluation in multi sensor system using machine learning. 3rd Int Conf on Intelligent Sustainable Systems, p.837-842.

[31]Rumelhart DE, Hinton GE, Williams RJ, 1986. Learning representations by back-propagating errors. Nature, 323(6088):533-536.

[32]Saha M, Ghosh R, Goswami B, 2014. Robustness and sensitivity metrics for tuning the extended Kalman filter. IEEE Trans Instrum Meas, 63(4):964-971.

[33]Schön T, Gustafsson F, Nordlund PJ, 2005. Marginalized particle filters for mixed linear/nonlinear state-space models. IEEE Trans Signal Process, 53(7):2279-2289.

[34]Singer H, 2008. Generalized Gauss‍–‍Hermite filtering. AStA Adv Stat Anal, 92(2):179-195.

[35]Singer RA, 1970. Estimating optimal tracking filter performance for manned maneuvering targets. IEEE Trans Aerosp Electron Syst, AES-6(4):473-483.

[36]Smidl V, Quinn A, 2008. Variational Bayesian filtering. IEEE Trans Signal Process, 56(10):5020-5030.

[37]Tichavsky P, Muravchik CH, Nehorai A, 1998. Posterior Cramer-Rao bounds for discrete-time nonlinear filtering. IEEE Trans Signal Process, 46(5):1386-1396.

[38]Wang XZ, Musicki D, Ellem R, et al., 2009. Efficient and enhanced multi-target tracking with Doppler measurements. IEEE Trans Aerosp Electron Syst, 45(4):1400-1417.

[39]Yi JX, Wan XR, Cheng F, et al., 2015. Deghosting for target tracking in single frequency network based passive radar. IEEE Trans Aerosp Electron Syst, 51(4):2655-2668.

[40]Yin S, Zhu XP, 2015. Intelligent particle filter and its application to fault detection of nonlinear system. IEEE Trans Ind Electron, 62(6):3852-3861.

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