Submit Manuscript
Peer Review
Editor Work
| Citation: | GAO Chang, GU Fengdeng, YAN Junkun, et al. Fusion detection for networked radar aided by Doppler information[J]. Journal of Radars, 2023, 12(3): 500–515. doi: 10.12000/JR22220
|
Fusion Detection for Networked Radar Aided by Doppler Information
doi: 10.12000/JR22220
More Information-
Abstract
Compared with single-radar systems, spatially separated networked radar usually has better detection performance due to its advantages of spatial and frequency diversities. Most of the current fusion detection methods based on networked radar systems only rely on the echo amplitude information of the target without considering the Doppler information that a coherent radar system can obtain to assist detection of targets. Intuitively, the spatial position and radial velocity of a target observed by different radar devices in the networked radar systems should meet certain physical constraints under which the target and false target can be substantially distinguished. Based on this consideration, fusion detection for the networked radar aided by a Doppler information algorithm is proposed in this paper. First, a set of inequalities is constructed based on the coupling between the observation of the same target’s azimuth and Doppler velocity by multiple radar stations. Then, a two-phase method, an algorithm in operational research, is used to judge whether the inequalities have a feasible solution, based on which a judgment is made on whether the target exists. Finally, some simulations are conducted, which show that the proposed algorithm can effectively improve the detection performance of the networked radar system fusion detection. Additionally, the influence of radar station location and target position on the fusion detection performance of the proposed algorithm is analyzed. -
-
References
[1] 何友, 关键, 孟祥伟. 雷达目标检测与恒虚警处理[M]. 2版. 北京: 清华大学出版社, 2011: 5–10.HE You, GUAN Jian, and MENG Xiangwei. Radar Target Detection and CFAR Processing[M]. 2nd ed. Beijing: Tsinghua University Press, 2011: 5–10.[2] RICHARD M A. Fundamentals of Radar Signal Processing[M]. 2nd ed. New York: McGraw-Hill Education, 2014.[3] SPAGNOLINI U and RAMPA V. Multitarget detection/tracking for monostatic ground penetrating radar: Application to pavement profiling[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(1): 383–394. doi: 10.1109/36.739074[4] CHEN Xiaolong, SU Ningyuan, HUANG Yong, et al. False-alarm-controllable radar detection for marine target based on multi-features fusion via CNNs[J]. IEEE Sensors Journal, 2021, 21(7): 9099–9111. doi: 10.1109/jsen.2021.3054744[5] 刘宏伟, 严峻坤, 周生华. 网络化雷达协同探测技术[J]. 现代雷达, 2020, 42(12): 7–12. doi: 10.16592/j.cnki.1004-7859.2020.12.002LIU Hongwei, YAN Junkun, and ZHOU Shenghua. Collaborative detection technology of netted radar[J]. Modern Radar, 2020, 42(12): 7–12. doi: 10.16592/j.cnki.1004-7859.2020.12.002[6] 周生华, 刘宏伟. MIMO雷达目标空间和频率散射多样性研究[J]. 电子与信息学报, 2010, 32(1): 38–42. doi: 10.3724/sp.j.1146.2008.01536ZHOU Shenghua and LIU Hongwei. Scattering properties of spatial and frequency diversity of MIMO radar targets[J]. Journal of Electronics &Information Technology, 2010, 32(1): 38–42. doi: 10.3724/sp.j.1146.2008.01536[7] 王经鹤, 易伟, 孔令讲. 组网雷达多帧检测前跟踪算法研究[J]. 雷达学报, 2019, 8(4): 490–500. doi: 10.12000/JR18092WANG Jinghe, YI Wei, and KONG Lingjiang. Multi-frame track before detect method for the netted radar system[J]. Journal of Radars, 2019, 8(4): 490–500. doi: 10.12000/JR18092[8] ZHANG Weiwei, SHI Chenguang, SALOUS S, et al. Convex optimization-based power allocation strategies for target localization in distributed hybrid non-coherent active-passive radar networks[J]. IEEE Transactions on Signal Processing, 2022, 70: 2476–2488. doi: 10.1109/tsp.2022.3173756[9] 时晨光, 王奕杰, 代向荣, 等. 面向目标跟踪的机载组网雷达辐射参数与航迹规划联合优化算法[J]. 雷达学报, 2022, 11(5): 778–793. doi: 10.12000/JR22005SHI Chenguang, WANG Yijie, DAI Xiangrong, et al. Joint transmit resources and trajectory planning for target tracking in airborne radar networks[J]. Journal of Radars, 2022, 11(5): 778–793. doi: 10.12000/JR22005[10] XIE Mingchi, YI Wei, KIRUBARAJAN T, et al. Joint node selection and power allocation strategy for multitarget tracking in decentralized radar networks[J]. IEEE Transactions on Signal Processing, 2018, 66(3): 729–743. doi: 10.1109/tsp.2017.2777394[11] BARKAT M and VARSHNEY P K. Decentralized CFAR signal detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 1989, 25(2): 141–149. doi: 10.1109/7.18676[12] ELIAS-FUSTE A, BROQUETAS-IBARS A, ANTEQUERA J, et al. CFAR data fusion center with inhomogeneous receivers[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 276–285. doi: 10.1109/7.135453[13] CONTE E, D’ADDIO E, FARINA A, et al. Multistatic radar detection: Synthesis and comparison of optimum and suboptimum receivers[J]. IEE Proceedings F (Communications, Radar and Signal Processing), 1983, 130(6): 484–494. doi: 10.1049/ip-f-1.1983.0078[14] D’ADDIO E and FARINA A. Overview of detection theory in multistatic radar[J]. IEE Proceedings F (Communications, Radar and Signal Processing), 1986, 133(7): 613–623. doi: 10.1049/ip-f-1.1986.0098[15] THOMOPOULOS S C A, VISWANATHAN R, and BOUGOULIAS D C. Optimal decision fusion in multiple sensor systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 1987, AES-23(5): 644–653. doi: 10.1109/taes.1987.310858[16] MATHUR A and WILLETT P. Local SNR considerations in decentralized CFAR detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(1): 13–22. doi: 10.1109/7.640257[17] GROSSI E, LOPS M, and VENTURINO L. A novel dynamic programming algorithm for track-before-detect in radar systems[J]. IEEE Transactions on Signal Processing, 2013, 61(10): 2608–2619. doi: 10.1109/tsp.2013.2251338[18] GAO Chang, YAN Junkun, PENG Xiaojun, et al. Signal structure information-based target detection with a fully convolutional network[J]. Information Science, 2021, 576: 345–354. doi: 10.1016/j.ins.2021.06.066[19] GAO Chang, YAN Junkun, PENG Xiaojun, et al. Intelligent multiframe detection aided by Doppler information and a deep neural network[J]. Information Science, 2022, 593: 432–448.[20] HU Zhijian, LEUNG H, and BLANCHETTE M. Statistical performance analysis of track initiation techniques[J]. IEEE Transactions on Signal Processing, 1997(2): 445–456. doi: 10.1109/78.554308[21] 靳标, 李聪, 张贞凯. 回波幅度信息辅助的群目标航迹起始方法[J]. 雷达学报, 2020, 9(4): 723–729. doi: 10.12000/JR19088JIN Biao, LI Cong, and ZHANG Zhenkai. Group target track initiation method aided by echo amplitude information[J]. Journal of Radars, 2020, 9(4): 723–729. doi: 10.12000/JR19088[22] SUNDARESAN A, VARSHNEY P K, and RAO N S V. Copula-based fusion of correlated decisions[J]. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(1): 454–471. doi: 10.1109/taes.2011.5705686[23] ZHANG Shan, KHANDURI P, and VARSHNEY P K. Distributed sequential detection: Dependent observations and imperfect communication[J]. IEEE Transactions on Signal Processing, 2020, 68: 830–842. doi: 10.1109/tsp.2019.2955830[24] BREARLEY A L, MITRA G, and WILLIAMS H P. Analysis of mathematical programming problems prior to applying the simplex algorithm[J]. Mathematical Programming, 1975, 8(1): 54–83. doi: 10.1007/bf01580428[25] ARSHAM H. Initialization of the simplex algorithm: An artificial-free approach[J]. SIAM Review, 1997, 39(4): 736–744. doi: 10.1137/s0036144596304722[26] KALL P. Computational methods for solving two-stage stochastic linear programming problems[J]. Zeitschrift für angewandte Mathematik und Physik ZAMP, 1979, 30(2): 261–271. doi: 10.1007/bf01601939[27] LUENBERGER D G and YE Y. Linear and Nonlinear Programming[M]. Springer, 2016.[28] WITTMANN-HOHLBEIN M and PISTIKOPOULOS E N. A two-stage method for the approximate solution of general multiparametric mixed-integer linear programming problems[J]. Industrial & Engineering Chemistry Research, 2012, 51(23): 8095–8107. doi: 10.1021/ie201408p[29] BORGWARDT K H. The average number of pivot steps required by the simplex-method is polynomial[J]. Zeitschrift für Operations Research, 1982, 26(1): 157–177. doi: 10.1007/bf01917108[30] 丁鹭飞, 耿富录, 陈建春. 雷达原理[M]. 5版. 北京: 电子工业出版社, 2014: 20–23.DING Lufei, GENG Fulu, and CHEN Jianchun. Principle of Radar[M]. 5th ed. Beijing: Publishing House of Electronics Industry, 2014: 20–23.[31] HOBALLAH I Y and VARSHNEY P K. Distributed Bayesian signal detection[J]. IEEE Transactions on Information Theory, 1989, 35(5): 995–1000. doi: 10.1109/18.42208 -
Proportional views
- Publishing Ethics
- Journal Insights
- Abstracting & Indexing
- Peer Review Policies
- Guide for Authors
- ISSN 2095-283X (Print)ISSN 2097-339X (Online)
- CN 10-1030/TN
- CODEN LXEUAO
About Journal
- Sponsor: China Radio Detection and Ranging Industry Association (CRIA)
- Phone: 010-58887062
- Email：radars@aircas.ac.cn
- Publisher: Leida Xuebao Bianjibu (Editorial office of the Journal of Radars)
Contacts Us
京ICP备20021838号-8
Supported by: Beijing Renhe Information Technology Co. Ltd
Export File
Citation
Format
Content
DownLoad:
- Figure 1. Networked radar fusion detection system
- Figure 2. Position relation between lines in linear inequalities based on single radar station
-
Figure 3. Corresponding feasible region
${R_i}$ - Figure 4. Three-radar station system
- Figure 5. Feasible region R of S1 and S2
- Figure 6. The position of L3 of S1 and S2
-
Figure 7. Corresponding feasible region
${R_i}$ - Figure 8. Feasible region R of S1 and S2
- Figure 9. Possible region of L3 of S3
- Figure 10. Radar network common view area
- Figure 11. Detection performance of various traditional fusion detectors before and after the introduction of Doppler information
- Figure 12. Feasible region R based on dual-radar station system
- Figure 13. Feasible region R based on three-radar station system
- Figure 14. Location of radar stations
- Figure 15. Detection performance of different algorithms applied for scenes with different number of radar stations
- Figure 16. Locations of 3 radar stations with different top angles
-
Figure 17. Distribution of
$\{ {R_i}\} _{i = 1}^3$ - Figure 18. Detection performance of algorithm applied in radar systems with different distributions of radars
- Figure 19. Location of targets
-
Figure 20. Distribution of
$\{ {R_i}\} _{i = 1}^3$ - Figure 21. Detection performance of algorithm applied in radar systems with different locations of targets