Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior perfo...Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.展开更多
针对固定翼飞行器在可达区域内最佳应急着陆场选择问题,通过构建普适性的指标体系,将上述问题转化为多属性评价问题,并提出一种熵权灰色理想解逼近(technique for order preference by similarity to an ideal solution, TOPSIS)法对问...针对固定翼飞行器在可达区域内最佳应急着陆场选择问题,通过构建普适性的指标体系,将上述问题转化为多属性评价问题,并提出一种熵权灰色理想解逼近(technique for order preference by similarity to an ideal solution, TOPSIS)法对问题加以解决。从天气情况、着陆场特性、空域拥堵程度和地面保障能力4个方面进行分析,细化为13个评价指标,构建了应急着陆场评价指标体系。在传统TOPSIS的基础上,分别使用熵权法和灰色关联度对权重计算和距离测度进行改进,解决了传统方法依赖评价专家的主观赋权和采用欧式距离导致的无法评判优劣的问题。通过仿真对比,体现了所提方法在权重确定方面的客观性和距离测度方面的准确性,能够为飞行器应急着陆场的选择提供依据。展开更多
基金supported by the Beijing Natural Science Foundation (L202003)National Natural Science Foundation of China (No. 31700479)。
文摘Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.
文摘针对固定翼飞行器在可达区域内最佳应急着陆场选择问题,通过构建普适性的指标体系,将上述问题转化为多属性评价问题,并提出一种熵权灰色理想解逼近(technique for order preference by similarity to an ideal solution, TOPSIS)法对问题加以解决。从天气情况、着陆场特性、空域拥堵程度和地面保障能力4个方面进行分析,细化为13个评价指标,构建了应急着陆场评价指标体系。在传统TOPSIS的基础上,分别使用熵权法和灰色关联度对权重计算和距离测度进行改进,解决了传统方法依赖评价专家的主观赋权和采用欧式距离导致的无法评判优劣的问题。通过仿真对比,体现了所提方法在权重确定方面的客观性和距离测度方面的准确性,能够为飞行器应急着陆场的选择提供依据。
