Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributio...Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.展开更多
The novel closed-form expressions for the average channel capacity of dual selection diversity is presented, as well as, the bit-error rate （BER） of several coherent and noncoherent digital modulation schemes in the...The novel closed-form expressions for the average channel capacity of dual selection diversity is presented, as well as, the bit-error rate （BER） of several coherent and noncoherent digital modulation schemes in the correlated Weibull fading channels with nonidentical statisticS. The results are expressed in terms of Meijer＇s Gfunction, which can be easily evaluated numerically. The simulation results are presented to validate the proposed theoretical analysis and to examine the effects of the fading severity on the concerned quantities.展开更多
Adaptive digital self-interference cancellation(ADSIC)is a significant method to suppress self-interference and improve the performance of the linear frequency modulated continuous wave(LFMCW)radar.Due to efficient im...Adaptive digital self-interference cancellation(ADSIC)is a significant method to suppress self-interference and improve the performance of the linear frequency modulated continuous wave(LFMCW)radar.Due to efficient implementation structure,the conventional method based on least mean square(LMS)is widely used,but its performance is not sufficient for LFMCW radar.To achieve a better self-interference cancellation(SIC)result and more optimal radar performance,we present an ADSIC method based on fractional order LMS(FOLMS),which utilizes the multi-path cancellation structure and adaptively updates the weight coefficients of the cancellation system.First,we derive the iterative expression of the weight coefficients by using the fractional order derivative and short-term memory principle.Then,to solve the problem that it is difficult to select the parameters of the proposed method due to the non-stationary characteristics of radar transmitted signals,we construct the performance evaluation model of LFMCW radar,and analyze the relationship between the mean square deviation and the parameters of FOLMS.Finally,the theoretical analysis and simulation results show that the proposed method has a better SIC performance than the conventional methods.展开更多
设计了一种基于1 bit Sigma-Delta调制技术的数字磁通门磁强计,并利用Matlab Simulink工具对其进行仿真建模与分析,获得了系统在噪声、线性度、动态响应速度以及频率响应等方面的性能参数.在±1000 nT量程范围内,该磁强计系统在1 H...设计了一种基于1 bit Sigma-Delta调制技术的数字磁通门磁强计,并利用Matlab Simulink工具对其进行仿真建模与分析,获得了系统在噪声、线性度、动态响应速度以及频率响应等方面的性能参数.在±1000 nT量程范围内,该磁强计系统在1 Hz处的噪声为0.17 pT·Hz-1/2,最大线性误差为1.04 pT,动态响应速度为1.07×10^(3) nT·s^(-1),频率响应带宽超过30 Hz.仿真结果证实,采用1 bit Sigma-Delta调制技术显著提高了数模转换器(Digital-to-Analog Converter,DAC)的转换精度,有效降低了数字磁强计系统的本底噪声和非线性误差,显著提升了数字磁强计的性能.基于1 bit Sigma-Delta调制技术的数字磁通门磁强计性能指标能够满足高精度磁场探测任务的要求,为空间磁场探测领域提供了一种高精度、高可靠性的探测手段,在深空探测及空间科学领域具有广泛的应用前景.展开更多
针对退役动力电池规模大、单体筛选复杂、重组后动态特性差异大以及寿命损耗加剧等问题,该文考虑电池模组的功能状态(state of function,SOF)特性,提出基于数字孪生技术的退役电池模组筛选方法。首先,通过电压、电流、荷电状态(state of...针对退役动力电池规模大、单体筛选复杂、重组后动态特性差异大以及寿命损耗加剧等问题,该文考虑电池模组的功能状态(state of function,SOF)特性,提出基于数字孪生技术的退役电池模组筛选方法。首先,通过电压、电流、荷电状态(state of charge,SOC)及健康状态(state of health,SOH)等参量表征SOF特性,估计梯次利用过程中SOF动态安全裕度;其次,搭建耦合物理模型、信息流及数字孪生映射体的电池模组筛选架构,提出基于生成对抗网络(generative adversarial networks,GAN)与长短期记忆网络(long short-term memory,LSTM)的电池数据缺失及偏移预测方法,优化退役动力电池模组表征SOF的多性能参量;最后,采用k-means算法对综合考虑SOH及SOF特性的退役电池模组进行聚类筛选。仿真结果表明:所提筛选方法可以提高退役动力电池动态一致性,并延长梯次利用过程中电池的运行寿命。展开更多
文摘Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.
基金the National High-Tech Research and Development Program (2002AA123032)the Innovative Research Team Program of UESTC, China.
文摘The novel closed-form expressions for the average channel capacity of dual selection diversity is presented, as well as, the bit-error rate （BER） of several coherent and noncoherent digital modulation schemes in the correlated Weibull fading channels with nonidentical statisticS. The results are expressed in terms of Meijer＇s Gfunction, which can be easily evaluated numerically. The simulation results are presented to validate the proposed theoretical analysis and to examine the effects of the fading severity on the concerned quantities.
文摘Adaptive digital self-interference cancellation(ADSIC)is a significant method to suppress self-interference and improve the performance of the linear frequency modulated continuous wave(LFMCW)radar.Due to efficient implementation structure,the conventional method based on least mean square(LMS)is widely used,but its performance is not sufficient for LFMCW radar.To achieve a better self-interference cancellation(SIC)result and more optimal radar performance,we present an ADSIC method based on fractional order LMS(FOLMS),which utilizes the multi-path cancellation structure and adaptively updates the weight coefficients of the cancellation system.First,we derive the iterative expression of the weight coefficients by using the fractional order derivative and short-term memory principle.Then,to solve the problem that it is difficult to select the parameters of the proposed method due to the non-stationary characteristics of radar transmitted signals,we construct the performance evaluation model of LFMCW radar,and analyze the relationship between the mean square deviation and the parameters of FOLMS.Finally,the theoretical analysis and simulation results show that the proposed method has a better SIC performance than the conventional methods.
文摘设计了一种基于1 bit Sigma-Delta调制技术的数字磁通门磁强计,并利用Matlab Simulink工具对其进行仿真建模与分析,获得了系统在噪声、线性度、动态响应速度以及频率响应等方面的性能参数.在±1000 nT量程范围内,该磁强计系统在1 Hz处的噪声为0.17 pT·Hz-1/2,最大线性误差为1.04 pT,动态响应速度为1.07×10^(3) nT·s^(-1),频率响应带宽超过30 Hz.仿真结果证实,采用1 bit Sigma-Delta调制技术显著提高了数模转换器(Digital-to-Analog Converter,DAC)的转换精度,有效降低了数字磁强计系统的本底噪声和非线性误差,显著提升了数字磁强计的性能.基于1 bit Sigma-Delta调制技术的数字磁通门磁强计性能指标能够满足高精度磁场探测任务的要求,为空间磁场探测领域提供了一种高精度、高可靠性的探测手段,在深空探测及空间科学领域具有广泛的应用前景.
文摘针对退役动力电池规模大、单体筛选复杂、重组后动态特性差异大以及寿命损耗加剧等问题,该文考虑电池模组的功能状态(state of function,SOF)特性,提出基于数字孪生技术的退役电池模组筛选方法。首先,通过电压、电流、荷电状态(state of charge,SOC)及健康状态(state of health,SOH)等参量表征SOF特性,估计梯次利用过程中SOF动态安全裕度;其次,搭建耦合物理模型、信息流及数字孪生映射体的电池模组筛选架构,提出基于生成对抗网络(generative adversarial networks,GAN)与长短期记忆网络(long short-term memory,LSTM)的电池数据缺失及偏移预测方法,优化退役动力电池模组表征SOF的多性能参量;最后,采用k-means算法对综合考虑SOH及SOF特性的退役电池模组进行聚类筛选。仿真结果表明:所提筛选方法可以提高退役动力电池动态一致性,并延长梯次利用过程中电池的运行寿命。
