Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk

This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.

Jamming Recognition Based on Feature Fusion and Convolutional Neural Network

The complicated electromagnetic environment of the BeiDou satellites introduces vari-ous types of external jamming to communication links,in which recognition of jamming signals with uncertainties is essential.In this work,the jamming recognition framework proposed consists of fea-ture fusion and a convolutional neural network(CNN).Firstly,the recognition inputs are obtained by prepossessing procedure,in which the 1-D power spectrum and 2-D time-frequency image are ac-cessed through the Welch algorithm and short-time Fourier transform(STFT),respectively.Then,the 1D-CNN and residual neural network(ResNet)are introduced to extract the deep features of the two prepossessing inputs,respectively.Finally,the two deep features are concatenated for the following three fully connected layers and output the jamming signal classification results through the softmax layer.Results show the proposed method could reduce the impacts of potential feature loss,therefore improving the generalization ability on dealing with uncertainties.

Spatiotemporal emotion recognition based on 3D time-frequency domain feature matrix

The research of emotion recognition based on electroencephalogram(EEG)signals often ignores the related information between the brain electrode channels and the contextual emotional information existing in EEG signals,which may contain important characteristics related to emotional states.Aiming at the above defects,a spatiotemporal emotion recognition method based on a 3-dimensional(3 D)time-frequency domain feature matrix was proposed.Specifically,the extracted time-frequency domain EEG features are first expressed as a 3 D matrix format according to the actual position of the cerebral cortex.Then,the input 3 D matrix is processed successively by multivariate convolutional neural network(MVCNN)and long short-term memory(LSTM)to classify the emotional state.Spatiotemporal emotion recognition method is evaluated on the DEAP data set,and achieved accuracy of 87.58%and 88.50%on arousal and valence dimensions respectively in binary classification tasks,as well as obtained accuracy of 84.58%in four class classification tasks.The experimental results show that 3 D matrix representation can represent emotional information more reasonably than two-dimensional(2 D).In addition,MVCNN and LSTM can utilize the spatial information of the electrode channels and the temporal context information of the EEG signal respectively.

Underground Pipeline Surveillance with an Algorithm Based on Statistical Time-Frequency Acoustic Features

Underground pipeline networks suffer from severe damage by earth-moving devices due to rapid urbanization.Thus,designing a round-the-clock intelligent surveillance system has become crucial and urgent.In this study,we develop an acoustic signal-based excavation device recognition system for underground pipeline protection.The front-end hardware system is equipped with an acoustic sensor array,an Analog-to-Digital Converter(ADC)module(ADS1274),and an industrial processor Advanced RISC Machine(ARM)cortex-A8 for signal collection and algorithm implementation.Then,a novel Statistical Time-Frequency acoustic Feature(STFF)is proposed,and a fast Extreme Learning Machine(ELM)is adopted as the classifier.Experiments on real recorded data show that the proposed STFF achieves better discriminative capability than the conventional acoustic cepstrum features.In addition,the surveillance platform is applicable for encountering big data owing to the fast learning speed of ELM.

基于多特征融合的视频火焰检测技术研究

基于彩色监控视频图像,利用火焰影像的时间运动特性,采用混合高斯背景建模方法从监控视频图像序列中提取出运动前景像素;在RGB色彩空间模型中通过火焰颜色的加权判别算法提取出具有火焰颜色的运动像素区域;最后,提出一种基于统计频率计数的火焰频闪特征识别方法,用于将视频图像中真实的火焰区域从具有火焰像素颜色的运动区域中区分出来。Matlab仿真结果表明,该算法具有很高的有效性和鲁棒性,能够应用实际的视觉火灾检测系统之中。

基于闪噪谱方法及加权滤波器组共空间模式的运动想象脑电信号识别

针对运动想象脑电信号复杂度高、受试者个体差异大、传统识别模型精度欠佳的问题,本文提出了基于闪噪谱方法及加权滤波器组共空间模式(wFBCSP)的运动想象脑电信号识别模型。首先,采用闪噪谱方法对运动想象脑电信号进行解析,以二阶差矩为结构函数,采用滑窗策略生成前兆时间序列,以发掘过渡阶段的隐匿动态变化。其次,从信号频带特点出发,利用wFBCSP分别对过渡阶段前兆时间序列及反应阶段序列进行特征提取,生成表征过渡阶段及反应阶段的特征向量。进一步,利用最小冗余最大相关算法对特征向量进行局部筛选,使所选特征能自适应于受试者的个体差异,具有更好的泛化性。最后,以支持向量机为分类器进行分类判别。实验结果表明,本文所提方法在运动想象脑电信号识别中取得了86.34%平均分类准确率,较对照方法性能更优,为运动想象脑电信号解码研究提供了新思路。