基于線性?xún)?nèi)插神經(jīng)網(wǎng)絡(luò)的雷達(dá)目標(biāo)一維距離像識(shí)別
1-D RANGE PROFILE IDENTIFICATION OF RADAR TARGETS BASED ON LINEAR INTERPOLATION NEURAL NETWORK
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摘要: 本文提出一種新穎的神經(jīng)網(wǎng)絡(luò)模型線性?xún)?nèi)插神經(jīng)網(wǎng)絡(luò)(Linear InterpolationNeural Network,LINN)用于雷達(dá)目標(biāo)一維距離像識(shí)別。它可避開(kāi)提取不變特征的難點(diǎn),利用目標(biāo)一維距離像特征隨姿態(tài)變化的信息來(lái)提高目標(biāo)識(shí)別性能。實(shí)驗(yàn)結(jié)果表明,采用LINN很好地解決了在大的姿態(tài)角范圍內(nèi)識(shí)別目標(biāo)時(shí)所存在的計(jì)算量與識(shí)別率的矛盾,提高了雷達(dá)對(duì)任意姿態(tài)目標(biāo)的識(shí)別性能。Abstract: A novel neural network model---Linear Interpolation Neural Network(LINN) has been presented, which is used for radar target identification. And the 1-D range profiles of targets are used as identification feature. It is well known that the 1-D range profile reflects the precise geometric structure feature of a target, but it varies with the pose of the target. The LINN utilizes just the variation information of the 1-D range profile with the pose to improve the identification performance of targets in any posture.
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