基于神经网络的分布式被动传感器信息融合技术

doi:10.3969/j.issn.1000-1093.2020.01.011

摘要/Abstract

摘要： 在分布式被动传感器信息融合中，存在多传感器信息关联和单传感器目标估计困难，二者相互依赖和制约，造成相对于不同传感器的信息难于进行时空对准、虚假目标不能消除。为此，应用一种混合式有序分层信息融合结构，避免多传感器信息的多重组合问题，建立了基于两个传感器的信息关联与目标估计联合优化模型，并采取一种优化神经网络算法，避免关联中的组合计算。仿真计算结果表明，这种信息融合结构、优化模型和模拟神经网络的应用是解决被动信息融合系统中关联和估计问题的一种有效方法，所采用的Hopfield型神经网络易于实现，可以提高信息融合的性能。

关键词: 分布式被动传感器, 信息融合, 关联, 神经网络

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

中图分类号:

TP212.6

李洪瑞. 基于神经网络的分布式被动传感器信息融合技术[J]. 兵工学报, 2020, 41(1): 95-101.

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

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

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