To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results,researchers have gradually adopted deep learning...To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results,researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques.To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios,we have designed a novel modulation recognition network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing.The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with multi-scale feature fusion.The two modules are skip-connected to work together to improve the robustness of the overall network.Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods.The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.展开更多
地面核磁共振(surface nuclear magnetic resonance,SNMR)技术是一种可直接定性定量探测地下水的非侵害式地球物理方法,已广泛应用于资源勘探、地质灾害预警和环境检测等方面。但在实际应用中,复杂的环境噪声导致微弱的SNMR信号常常被淹...地面核磁共振(surface nuclear magnetic resonance,SNMR)技术是一种可直接定性定量探测地下水的非侵害式地球物理方法,已广泛应用于资源勘探、地质灾害预警和环境检测等方面。但在实际应用中,复杂的环境噪声导致微弱的SNMR信号常常被淹没,很难获取有效的SNMR信号。针对这一问题,本文提出了一种基于差分结构的SNMR数据噪声压制技术,采用两个接收线圈等距设置在发射线圈上下位置。这种分布可以实时抵消大部分环境噪声以及消除收发线圈耦合影响。理论建模和仿真结果验证了新方法能够有效压制噪声,并可靠获取到早期自由感应衰减(free induction decay,FID)信号。展开更多
Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination me...Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination method based on multi-feature extraction and classification is proposed to effectively eliminate false alarm plots.Firstly,the density based spatial clustering of applications with noise(DBSCAN)algorithm is used to cluster the radar echo data processed by constant false-alarm rate(CFAR).The multi-features including the scale features,time domain features and transform domain features are extracted.Secondly,a feature evaluation method combining pearson correlation coefficient(PCC)and entropy weight method(EWM)is proposed to evaluate interrelation among features,effective feature combination sets are selected as inputs of the classifier.Finally,False alarm plots classified as clutters are eliminated.The experimental results show that proposed method can eliminate about 90%false alarm plots with less target loss rate.展开更多
基金Project supported by the National Key R&D Program of China(No.2020YFF01015000ZL)the Fundamental Research Funds for the Central Universities,China(No.3072022CF0806)。
文摘To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results,researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques.To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios,we have designed a novel modulation recognition network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing.The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with multi-scale feature fusion.The two modules are skip-connected to work together to improve the robustness of the overall network.Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods.The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.
文摘Caused by the environment clutter,the radar false alarm plots are unavoidable.Suppressing false alarm points has always been a key issue in Radar plots procession.In this paper,a radar false alarm plots elimination method based on multi-feature extraction and classification is proposed to effectively eliminate false alarm plots.Firstly,the density based spatial clustering of applications with noise(DBSCAN)algorithm is used to cluster the radar echo data processed by constant false-alarm rate(CFAR).The multi-features including the scale features,time domain features and transform domain features are extracted.Secondly,a feature evaluation method combining pearson correlation coefficient(PCC)and entropy weight method(EWM)is proposed to evaluate interrelation among features,effective feature combination sets are selected as inputs of the classifier.Finally,False alarm plots classified as clutters are eliminated.The experimental results show that proposed method can eliminate about 90%false alarm plots with less target loss rate.
