Neural Network-based Information Fusion Technique for Distributed Passive Sensor

doi:10.3969/j.issn.1000-1093.2020.01.011

Abstract

Abstract: The information correlation (IC) of mult-sensor and the target estimation (TE) of single sensor are difficult in distributed passive sensor (DPS) information fusion (IF). For example, the information from different sensors cannot be registered in time and space, and the false targets cannot be eliminated due to the interdependence and mutual restriction of TE. Therefore a hybrid ordered delaminated information fusion structure (HODIFS) is introduced to avoid the multiple combinations of multi-sensor information. A united optimization model (UOM) based on 2-sensor IC and TE is established, which uses an optimization Hopfield neural network (HNN) algorithm and avoids the complex combination computation of correlation. Simulated results indicate that the HODIFS with UOM based on HNN is effective in the DPSIF, in which HNN is easily realized and the performance of DPSIF can be improved. Key

Key words: distributedpassivesensor, informationfusion, correlation, neuralnetwork

CLC Number:

TP212.6

LI Hongrui. Neural Network-based Information Fusion Technique for Distributed Passive Sensor[J]. Acta Armamentarii, 2020, 41(1): 95-101.

References

［1］董志荣.舰船信息融合与目标运动分析［M］. 北京:国防工业出版社,2016:114-269.
DONG Z R. Information fusion and target motion analysis for naval vessels［M］. Beijing: National Defense Industry Press, 2016: 114-269. (in Chinese)
［2］李洪瑞, 盛安东.连续纯方位系统的可观测性分析［J］. 兵工学报, 2009, 30(11):1446-1450.
LI H R,SHENG A D. Analysis on the observability for continuous bearings-only system［J］. Acta Armamentarii, 2009, 30(11):1446-1450.(in Chinese)
［3］ NARDONE S C, AIDALA V J. Observability criteria for bearing-only target motion analysis［J］. IEEE Transactions on Aerospace and Electronic Systems, 1981, 17(2):162-166.
［4］ AIDALA V J, HAMMEL S E. Utilization of modified polar coordinates for bearings-only tracking［J］. IEEE Transactions on Automatic Control, 1983, 28(3):283-294.
［5］段广全, 孙书利. 带未知模型参数和衰减观测率系统自校正分布式融合估计［J/OL］. 自动化学报, 2019［2019-03-20］. http:∥kns.cnki.net/kcms/detail/11.2109.TP.20190219.1552.007.html.
DUAN G Q, SUN S L. Self-tuning distributed fusion estimation for systems with unknown model parameters and fading measurement rates［J/OL］. Acta Automatica Sinica, 2019［2019-03-20］. http:∥kns.cnki.net/kcms/detail/11.2109.TP.20190219.1552.007.html.(in Chinese)
［6］ XIE Y F, SONG T L. Bearings-only multi-target tracking using an improved labeled multi-Bernoulli filter［J］. Signal Processing, 2018, 151:32-44.
［7］ LIN H L, SUN S L. Globally optimal sequential and distributed fusion state estimation for multi-sensor systems with cross-correlated noises［J］. Automatica, 2019, 101:128-137.
［8］李文超, 邹焕新, 雷琳, 等.目标数据关联技术综述［J］. 计算机仿真, 2014, 31(3):1-5,10.
LI W C,ZOU H X,LEI L,et al.A survey of target data association［J］. Computer Simulation, 2014, 31(3):1-5,10.(in Chinese)
［9］ SENGUPTA D, ILTIS R A.Neural solution to the multitarget tracking data association problem［J］.IEEE Transactions on Aerospace and Electronic Systems, 1989,25(1):96-108.

［10］吕红芳, 顾幸生. 基于蚁群神经网络的两级信息融合算法［J］. 上海交通大学学报, 2016, 50(8):1323-1330.
L H F, GU X S. A two level information fusion algorithm based on ant colony neural network［J］. Journal of Shanghai Jiao Tong University, 2016, 50(8):1323-1330.(in Chinese)
［11］田腾, 尹力, 黄海宁, 等. 基于Hough变换的纯方位目标航迹关联算法［J］. 网络新媒体技术, 2018, 7(2):37-41.
TIAN T,YIN L,HUANG H N, et al. Track correlation algorithm of bearings-only targets based on Hough transform［J］. Microcomputer Applications,2018,7(2):37-41.(in Chinese)
［12］ RONALD P S M. 多源多目标统计信息融合［M］.范红旗, 卢大成, 刘本源, 等，译. 北京:国防工业出版社, 2013:208-217.
RONALD P S M. Statistical multisource-multitarget information fusion［M］. FAN H Q,LU D C,LIU B Y, et al,translated. Beijing: National Defense Industry Press, 2013: 208-217. (in Chinese)
［13］宋强, 周万宁. 基于最近航迹迭代的目标航迹对准关联算法［J］. 舰船电子工程, 2018, 38(10):69-73.
SONG Q, ZHOU W N. Target track alignment-correlation algorithm based on iterative closet track ［J］. Ship Electronic Engineering, 2018, 38(10):69-73.(in Chinese)
［14］衣晓, 张怀巍, 曹昕莹, 等. 基于区间灰数的分布式多目标航迹关联算法［J］.航空学报, 2013, 34(2):352-360.
YI X, ZHANG H W,CAO X Y,et al. A track association algorithm for distributed muti-target system based on gray interval numbers［J］. Acta Aeronautica et Astronautica Sinica, 2013, 34(2): 352-360.(in Chinese)
［15］李启虎, 卫翀华, 薛山花. 声呐多传感器观测资料数据融合的一种深度学习算法［J］. 中国科学:信息科学, 2018, 48(12): 1614-1621.
LI Q H, WEI C H, XUE S H. A deep learning algorithm for multiple observation data fusion in sonar system［J］.Scientia Sinica: Informationis, 2018, 48(12): 1614-1621. (in Chinese)
［16］李洪瑞, 王超, 肖凯. 纯方位目标运动分析四维状态的最小二乘模型及分析［J］. 指挥控制与仿真, 2007, 29(5):10-14.
LI H R, WANG C, XIAO K. Least square models with 4-dimesional states and analysis for bearings-only TMA［J］. Command Control & Simulation, 2007, 29(5):10-14. (in Chinese)
［17］ MATHENDRA M,VIKRAM K,BA-NGU V. 目标跟踪、分类与传感器管理理论及应用［M］. 乔向东, 梁彦, 杨峰, 等, 译. 北京:国防工业出版社, 2017:11-19.
MAHENDRA M,VIKRAM K,BA-NGU V. Integrated tracking, classification and sensor management: theory and application［M］. QIAO X D,LIANG Y,YANG F,et al, translated. Beijing: National Defense Industry Press, 2017: 11-19. (in Chinese)

第41卷第1期
2020 年1月兵工学报ACTA
ARMAMENTARIIVol.41No.1Jan.2020

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