基于TDRNN的大气层外弹道式空间红外目标识别

刘涛; 吕江北; 王书宏; 卢焕章

doi:10.3724/SP.J.1146.2009.00015

基于TDRNN的大气层外弹道式空间红外目标识别

doi: 10.3724/SP.J.1146.2009.00015

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出版历程
- 收稿日期: 2009-01-07
- 修回日期: 2009-06-29
- 刊出日期: 2010-01-19

Exo-atmospheric Discrimination of Infrared Ballistic Space Target Based on the Time-Delay Recurrent Neural Network

摘要

摘要: 该文提出一种基于时延回归神经网络(TDRNN)的大气层外弹道式空间红外目标识别方法。该网络采用自适应时间延迟器和输出层回归结构，可以针对输入时间序列信号的局部时变信息自适应选择延迟步长，而且具有边跟踪边识别的记忆性动态识别功能。综合考虑弹道式空间目标热物参数和运动状态、空间环境辐射和红外传感器效应等因素，仿真了典型弹道式空间目标的动态红外辐射强度数据，进行了目标识别实验研究。仿真结果表明，提出的TDRNN网络对于弹道式空间红外目标有很强的分类能力。
- 目标识别; 空间目标; 红外辐射; 时延回归神经网络
Abstract: This paper presents a Time-Delay Recurrent Neural Network (TDRNN) for exo-atmospheric ballistic space infrared target discrimination. The TDRNN employs adaptive time delays and recurrences where the adaptive time delays make the network choose the optimal values of time delays for the temporal location of important information in the input sequence, and the recurrences enable the network to have dynamic discrimination function. The IR radiation data of space target considering the sensor effect is simulated, taking into account the synthetic factors of heat parameter and motion, the space environment radiation and the sensor effect. The target discrimination experiment is developed, based on dynamic IR radiation simulated data of four typical ballistic space target. The simulation results show that TDRNN have better discrimination ability.

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