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基于软件无线电与神经网络的频谱监测识别系统 被引量:1
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作者 覃远年 谢旭锋 刘申 《无线电通信技术》 2020年第2期239-245,共7页
为了解决目前城市频谱监测工作依赖于固定的监测站、持续性监测能力较差和频谱异常判定人工依赖性高等问题,提出了一种利用软件无线电搭配人工智能的新型频谱监测识别系统。首先利用GNURadio软件无线电平台,实现对某一频段的实时监测,... 为了解决目前城市频谱监测工作依赖于固定的监测站、持续性监测能力较差和频谱异常判定人工依赖性高等问题,提出了一种利用软件无线电搭配人工智能的新型频谱监测识别系统。首先利用GNURadio软件无线电平台,实现对某一频段的实时监测,获得所需要的频域数据;再利用一系列预处理手段,优化数据样本;最后,在前馈(BP)神经网络中,对频域状态波形进行识别,确定其信号数量、类型及信号所处信道,可以实现持续性频谱监测和智能频谱状态识别判定,其神经网络识别准确率高达96.1%。该系统可以嵌入手持频谱监测设备,并结合云端服务器持续智能地监测区域频谱环境。 展开更多
关键词 GNURadio 频谱监控 频谱识别 BP神经网络 主成分分析
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Data-Driven Joint Estimation for Blind Signal Based on GA-PSO Algorithm
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作者 LIU Shen qin yuannian +2 位作者 LI Xiaofan ZHAO Yubin XU Chengzhong 《ZTE Communications》 2019年第3期63-70,共8页
Without any prior information about related wireless transmitting nodes,joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task.Measuring the ... Without any prior information about related wireless transmitting nodes,joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task.Measuring the signal related data based on a group distributed sensor is an efficient way to infer the various characteristics of the signal sources.In this paper,we propose a particle swarm optimization to estimate multiple co-frequency"blind"source nodes,which is based on the received power data measured by the sensors.To distract the mix signals precisely,a genetic algorithm is applied,and it further improves the estimation performance of the system.The simulation results show the efficiency of the proposed algorithm. 展开更多
关键词 PARTICLE SWARM Optimization(PSO) GENETIC Algorithm(GA) spatially distributed sensor BLIND signal detection
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