一种基于卷积神经网络的LFM信号超低副瓣脉冲压缩方法（英文）-豆柴文库

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一种基于卷积神经网络的LFM信号超低副瓣脉冲压缩方法（英文）
Title:AConvolutionalNeuralNetwork-BasedLFMSignalPulseCompressionMethodforUltra-LowSidelobe
Abstract:
Pulsecompressiontechniquesplayacrucialroleinradarsystemstoenhancetherangeresolutionandreducesidelobelevels.Inthispaper,weproposeanovelpulsecompressionmethodbasedonConvolutionalNeuralNetwork(CNN)forLinearFrequencyModulated(LFM)signalstoachieveultra-lowsidelobelevels.TheproposedapproachtakesadvantageofthepowerfulfeatureextractioncapabilitiesofCNNstoimprovethesidelobesuppressionperformanceofLFMpulsecompression.
1.Introduction
Pulsecompressionisanimportantprocessinradarsystemstoimproverangeresolutionandreducesidelobelevelsinordertodistinguishtargetswithhighprecision.ThetraditionalapproachforLFMpulsecompressionisbasedonmatchedfilteringtechniques,whichhavelimitationsintermsofsidelobesuppressionability.Inrecentyears,deeplearningtechniques,especiallyConvolutionalNeuralNetworks(CNNs),haveachievedremarkablesuccessinvarioussignalprocessingtasks.Inthispaper,weproposeaCNN-basedpulsecompressionmethodforLFMsignals,aimingtoachieveultra-lowsidelobelevelsandimprovedtargetdetectionperformance.
2.LFMSignalPulseCompression
2.1LFMSignalBasics
WeprovideabriefoverviewofLFMsignals,includingtheirmathematicalrepresentationandproperties.
2.2TraditionalMatchedFiltering
WediscussthetraditionalmatchedfilteringtechniqueforLFMpulsecompression,highlightingitslimitationsinsidelobesuppression.
3.ConvolutionalNeuralNetworksforPulseCompression
3.1CNNArchitecture
WeintroducethearchitectureoftheCNNusedforpulsecompression,discussingitslayersandtheirfunctions.
3.2TrainingDataPreparation
Wedescribetheprocessofpreparingthetrainingdata,includingthegenerationofLFMpulsesamplesandthesimulationofdifferentnoiseandinterferenceconditions.
3.3TrainingandOptimization
WeexplainthetrainingprocessoftheCNNmodel,includingthelossfunction,trainingalgorithm,andoptimizationtechniques.
4.ExperimentalResults
4.1DatasetDescription
Wepresentthedatasetusedforevaluation,includingthesize,composition,andno