In a flank array on an unmanned underwater vehicle （UUV）, self-generated noise which has broadband and colored spectrum property in frequency and spatial domain is the main factor affecting the performance of weak s...In a flank array on an unmanned underwater vehicle （UUV）, self-generated noise which has broadband and colored spectrum property in frequency and spatial domain is the main factor affecting the performance of weak signal detection, so the technique of adaptive noise cancellation （ANC） as well as physical denoising and active noise cancellation are often used in practice. Because ANC is based on correlations, improvements in performance come from better correlation between reference signals and primary signals. Taking full advantage of the characteristics of flank arrays and the characteristics of information obtained from hydrophones, a new method for reference signal acquisition for adaptive noise cancellation is proposed, in which the multi-channel reference signals are obtained by accurate delaying for a given direction of arrival （DOA） and differencing between adjacent outputs of array elements. The validity of the proposed method was verified through system modeling simulations and lake experiments which showed good performance with little additional computational burden.展开更多
We develop an improved design of thin gap chamber （TGC） simulation signal source. To further simulate the feature of TGC detector, a novel thought is proposed. The TGC source has 256 channels. Every channel can rand...We develop an improved design of thin gap chamber （TGC） simulation signal source. To further simulate the feature of TGC detector, a novel thought is proposed. The TGC source has 256 channels. Every channel can randomly output the signal in 25 ns. The design is based on true random number generator （TRNG）. Considering the electrical connection between the TGC source and the developing trigger electronics, the GFZ connector is used. The experimental results show that the improved TGC simulation signal source can uniformly output the random signal in every channel. The output noise is less than 3 mVrms.展开更多
To explore the influence of the fusion of different features on recognition,this paper took the electromyography(EMG)signals of rectus femoris under different motions(walk,step,ramp,squat,and sitting)as samples,linear...To explore the influence of the fusion of different features on recognition,this paper took the electromyography(EMG)signals of rectus femoris under different motions(walk,step,ramp,squat,and sitting)as samples,linear features(time-domain features(variance(VAR)and root mean square(RMS)),frequency-domain features(mean frequency(MF)and mean power frequency(MPF)),and nonlinear features(empirical mode decomposition(EMD))of the samples were extracted.Two feature fusion algorithms,the series splicing method and complex vector method,were designed,which were verified by a double hidden layer(BP)error back propagation neural network.Results show that with the increase of the types and complexity of feature fusions,the recognition rate of the EMG signal to actions is gradually improved.When the EMG signal is used in the series splicing method,the recognition rate of time-domain+frequency-domain+empirical mode decomposition(TD+FD+EMD)splicing is the highest,and the average recognition rate is 92.32%.And this rate is raised to 96.1%by using the complex vector method,and the variance of the BP system is also reduced.展开更多
The received signals used for sparse code multiple access(SCMA)detection are usually contaminated with noise during transmission,which exposes an issue of low decoding efficiency.To address this issue,a novel detector...The received signals used for sparse code multiple access(SCMA)detection are usually contaminated with noise during transmission,which exposes an issue of low decoding efficiency.To address this issue,a novel detector based on a residual network(ResNet)perception fusion framework(RSMPA)is proposed for uplink SCMA system in this paper.Specifically,we first formulate a joint design of perception system and traditional communication module.A perception framework based on ResNet is applied to cancel the noise component and enhance the communication system performance.The ResNet model is designed and trained using the clean and noisy SCMA signal,respectively.Based on the denoised output,information iteration process is executed for multi-user detection.Simulation results indicate that the perception model achieves an excellent denoising performance for SCMA system and the proposed scheme outperforms the conventional detection algorithms in terms of SER performance.展开更多
针对小样本条件下且低信噪比时低截获概率(Low Probability of Intercept,LPI)雷达信号识别精度低的问题,本文提出了一种基于局部最大化同步压缩变换(Local Maximum Synchrosqueezing Transform,LMSST)与平滑伪维格纳维尔变换(Smoothed ...针对小样本条件下且低信噪比时低截获概率(Low Probability of Intercept,LPI)雷达信号识别精度低的问题,本文提出了一种基于局部最大化同步压缩变换(Local Maximum Synchrosqueezing Transform,LMSST)与平滑伪维格纳维尔变换(Smoothed Pseudo Wigner-Ville Distribution,SPWVD)的双通道特征融合网络模型。利用LMSST和SPWVD对仅有的小样本LPI雷达信号分别进行时频分析,获取二维时频图像;使用循环对抗生成网络对其进行扩充并送入双通道网络对其进行特征提取和特征早融合;采用Softmax分类器对融合后的特征进行分选识别。研究结果表明,在信噪比为-8 dB时,所设计的模型的整体识别率达到93.1%;相较于单通道识别模型,在小样本条件下的识别精度有效提高6%~7%。此研究为小样本时LPI雷达信号的识别提供了一种理论依据。展开更多
In order to solve the distributed detection fusion problem of underwater target detection, when the signal to noise ratio (SNR) of the acoustic channel is low, a new strategy for united detection fusion and communicat...In order to solve the distributed detection fusion problem of underwater target detection, when the signal to noise ratio (SNR) of the acoustic channel is low, a new strategy for united detection fusion and communication using multiple sensors was proposed. The performance of detection fusion was studied and compared based on the Neyman-Pearson principle when the binary phase shift keying (BPSK) and on-off keying (OOK) modes were used by the local sensors. The comparative simulation and analysis between the optimal likelihood ratio test and the proposed strategy was completed, and both the theoretical analysis and simulation indicate that using the proposed new strategy could improve the detection performance effectively. In theory, the proposed strategy of united detection fusion and communication is of great significance to the establishment of an underwater target detection system.展开更多
In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according t...In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according to the statistical variance of target position in the stationary 3D scenarios.The FFP method fuses the pedestrian dead reckoning(PDR)estimation to solve the moving target localization problem.We also introduce auxiliary parameters to estimate the target motion state.Subsequently,we can locate the static pedestrians and track the the moving target.For the case study,eight access stationary points are placed on a bookshelf and hypermarket;one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf.We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor,weighted k-nearest neighbor,pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter,unscented Kalman filter and particle fil-ter(PF).The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors,espe-cially in the 3D scenarios.Simulation results corroborate the effectiveness of our proposed approach.展开更多
The application of Global Navigation Satellite Systems(GNSSs)in the intelligent railway systems is rapidly developing all over the world.With the GNSs-based train positioning and moving state perception,the autonomy a...The application of Global Navigation Satellite Systems(GNSSs)in the intelligent railway systems is rapidly developing all over the world.With the GNSs-based train positioning and moving state perception,the autonomy and flexibility of a novel train control system can be greatly enhanced over the existing solutions relying on the track-side facilities.Considering the safety critical features of the railway signaling applications,the GNSS stand-alone mode may not be sufficient to satisfy the practical requirements.In this paper,the key technologies for applying GNSS in novel train-centric railway signaling systems are investigated,including the multi-sensor data fusion,Virtual Balise(VB)capturing and messaging,train integrity monitoring and system performance evaluation.According to the practical characteristics of the novel train control system under the moving block mode,the details of the key technologies are introduced.Field demonstration results of a novel train control system using the presented technologies under the practical railway operation conditions are presented to illustrate the achievable performance feature of autonomous train state perception using BeiDou Navigation Satellite System(BDS)and related solutions.It reveals the great potentials of these key technologies in the next generation train control system and other GNSS-based railway implementations.展开更多
基金the National Natural Science Foundation of China under Grant No.60572098
文摘In a flank array on an unmanned underwater vehicle （UUV）, self-generated noise which has broadband and colored spectrum property in frequency and spatial domain is the main factor affecting the performance of weak signal detection, so the technique of adaptive noise cancellation （ANC） as well as physical denoising and active noise cancellation are often used in practice. Because ANC is based on correlations, improvements in performance come from better correlation between reference signals and primary signals. Taking full advantage of the characteristics of flank arrays and the characteristics of information obtained from hydrophones, a new method for reference signal acquisition for adaptive noise cancellation is proposed, in which the multi-channel reference signals are obtained by accurate delaying for a given direction of arrival （DOA） and differencing between adjacent outputs of array elements. The validity of the proposed method was verified through system modeling simulations and lake experiments which showed good performance with little additional computational burden.
基金Supported by the State Key Laboratory of Particle Detection and Electronicsthe National Natural Science Foundation of China under Grant No 11375179
文摘We develop an improved design of thin gap chamber （TGC） simulation signal source. To further simulate the feature of TGC detector, a novel thought is proposed. The TGC source has 256 channels. Every channel can randomly output the signal in 25 ns. The design is based on true random number generator （TRNG）. Considering the electrical connection between the TGC source and the developing trigger electronics, the GFZ connector is used. The experimental results show that the improved TGC simulation signal source can uniformly output the random signal in every channel. The output noise is less than 3 mVrms.
基金support by the Aerospace Research Project of China under Grant No.020202。
文摘To explore the influence of the fusion of different features on recognition,this paper took the electromyography(EMG)signals of rectus femoris under different motions(walk,step,ramp,squat,and sitting)as samples,linear features(time-domain features(variance(VAR)and root mean square(RMS)),frequency-domain features(mean frequency(MF)and mean power frequency(MPF)),and nonlinear features(empirical mode decomposition(EMD))of the samples were extracted.Two feature fusion algorithms,the series splicing method and complex vector method,were designed,which were verified by a double hidden layer(BP)error back propagation neural network.Results show that with the increase of the types and complexity of feature fusions,the recognition rate of the EMG signal to actions is gradually improved.When the EMG signal is used in the series splicing method,the recognition rate of time-domain+frequency-domain+empirical mode decomposition(TD+FD+EMD)splicing is the highest,and the average recognition rate is 92.32%.And this rate is raised to 96.1%by using the complex vector method,and the variance of the BP system is also reduced.
基金This work was supported by China Postdoctoral Science Foundation(2021M702987)the Fundamental Research Funds for the Central Universities(CUC210B032).
文摘The received signals used for sparse code multiple access(SCMA)detection are usually contaminated with noise during transmission,which exposes an issue of low decoding efficiency.To address this issue,a novel detector based on a residual network(ResNet)perception fusion framework(RSMPA)is proposed for uplink SCMA system in this paper.Specifically,we first formulate a joint design of perception system and traditional communication module.A perception framework based on ResNet is applied to cancel the noise component and enhance the communication system performance.The ResNet model is designed and trained using the clean and noisy SCMA signal,respectively.Based on the denoised output,information iteration process is executed for multi-user detection.Simulation results indicate that the perception model achieves an excellent denoising performance for SCMA system and the proposed scheme outperforms the conventional detection algorithms in terms of SER performance.
文摘针对小样本条件下且低信噪比时低截获概率(Low Probability of Intercept,LPI)雷达信号识别精度低的问题,本文提出了一种基于局部最大化同步压缩变换(Local Maximum Synchrosqueezing Transform,LMSST)与平滑伪维格纳维尔变换(Smoothed Pseudo Wigner-Ville Distribution,SPWVD)的双通道特征融合网络模型。利用LMSST和SPWVD对仅有的小样本LPI雷达信号分别进行时频分析,获取二维时频图像;使用循环对抗生成网络对其进行扩充并送入双通道网络对其进行特征提取和特征早融合;采用Softmax分类器对融合后的特征进行分选识别。研究结果表明,在信噪比为-8 dB时,所设计的模型的整体识别率达到93.1%;相较于单通道识别模型,在小样本条件下的识别精度有效提高6%~7%。此研究为小样本时LPI雷达信号的识别提供了一种理论依据。
基金Supported by the National Natural Science Foundation of China under Grant No.60972152
文摘In order to solve the distributed detection fusion problem of underwater target detection, when the signal to noise ratio (SNR) of the acoustic channel is low, a new strategy for united detection fusion and communication using multiple sensors was proposed. The performance of detection fusion was studied and compared based on the Neyman-Pearson principle when the binary phase shift keying (BPSK) and on-off keying (OOK) modes were used by the local sensors. The comparative simulation and analysis between the optimal likelihood ratio test and the proposed strategy was completed, and both the theoretical analysis and simulation indicate that using the proposed new strategy could improve the detection performance effectively. In theory, the proposed strategy of united detection fusion and communication is of great significance to the establishment of an underwater target detection system.
基金partially supported by the National Natural Science Foun-dation of China(No.62071389).
文摘In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according to the statistical variance of target position in the stationary 3D scenarios.The FFP method fuses the pedestrian dead reckoning(PDR)estimation to solve the moving target localization problem.We also introduce auxiliary parameters to estimate the target motion state.Subsequently,we can locate the static pedestrians and track the the moving target.For the case study,eight access stationary points are placed on a bookshelf and hypermarket;one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf.We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor,weighted k-nearest neighbor,pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter,unscented Kalman filter and particle fil-ter(PF).The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors,espe-cially in the 3D scenarios.Simulation results corroborate the effectiveness of our proposed approach.
基金supported by National Key Research and Development Program of China(2022YFB4300501)National Natural Science Foundation of China(62027809,U2268206,T2222015).
文摘The application of Global Navigation Satellite Systems(GNSSs)in the intelligent railway systems is rapidly developing all over the world.With the GNSs-based train positioning and moving state perception,the autonomy and flexibility of a novel train control system can be greatly enhanced over the existing solutions relying on the track-side facilities.Considering the safety critical features of the railway signaling applications,the GNSS stand-alone mode may not be sufficient to satisfy the practical requirements.In this paper,the key technologies for applying GNSS in novel train-centric railway signaling systems are investigated,including the multi-sensor data fusion,Virtual Balise(VB)capturing and messaging,train integrity monitoring and system performance evaluation.According to the practical characteristics of the novel train control system under the moving block mode,the details of the key technologies are introduced.Field demonstration results of a novel train control system using the presented technologies under the practical railway operation conditions are presented to illustrate the achievable performance feature of autonomous train state perception using BeiDou Navigation Satellite System(BDS)and related solutions.It reveals the great potentials of these key technologies in the next generation train control system and other GNSS-based railway implementations.
