諧振區(qū)雷達(dá)目標(biāo)識別的模塊化神經(jīng)網(wǎng)絡(luò)方法
RADAR TARGET RECOGNITION BASED ON MODULAR NEURAL NETWORKS
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摘要: 本文研究了基于模塊化神經(jīng)網(wǎng)絡(luò)的諧振區(qū)雷達(dá)目標(biāo)識別方法,該方法先用BP網(wǎng)絡(luò)進(jìn)行波形預(yù)測,再用最大后驗概率準(zhǔn)則或修正最小平方誤差準(zhǔn)則進(jìn)行分類。通過計算機模擬,證實了該方法有較好的識別性能,且對信號留數(shù)變化,即目標(biāo)的姿態(tài)變化不敏感。另外,該方法還具有實現(xiàn)簡單,結(jié)構(gòu)擴展方便等優(yōu)點。Abstract: A new method for radar target recognition based on modular neural networks is reported in this paper.In this method,the response from an unknown target is first sent to several waveform predicators that the some BP neural networks trained by responses from known targets respectively.Then the predicator erros are inputted to a classfier using the rule of maximum a posteriori or the rule of modified minimum squared errorrs.The simulation of PC computer shows that the new method has a good performance on radar target recognition.The method also has other advantages such as easy realization,clear structure and easy expansion.
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