累积和检测方法是根据声呐目标信号出现与消失时概率密度函数(Probability Distribution Function,PDF)的变化进行有效的瞬态信号检测。以非高斯模型t分布假设替代传统的高斯分布方差变化假设作为描述瞬态信号的PDF形式,推导了累积和检...累积和检测方法是根据声呐目标信号出现与消失时概率密度函数(Probability Distribution Function,PDF)的变化进行有效的瞬态信号检测。以非高斯模型t分布假设替代传统的高斯分布方差变化假设作为描述瞬态信号的PDF形式,推导了累积和检验统计量的表达、更新量PDF求取的数值方法,利用快速傅里叶变换法计算了门限和自由度等检测参数。利用仿真的落水信号、船体加速信号和消声水池实验数据进行检验。结果表明,基于t分布假设的累积和方法对瞬态脉冲信号的检测效果优于常规累积和方法,能更快地响应信号变化,更好地抑制背景干扰。展开更多
The performance of the Ordered-Statistic Smallest Of (OSSO) Constant False Alarm Rate (CFAR) with binary integration in Weibull background with known shape parameter is analyzed, in the cases that the processor operat...The performance of the Ordered-Statistic Smallest Of (OSSO) Constant False Alarm Rate (CFAR) with binary integration in Weibull background with known shape parameter is analyzed, in the cases that the processor operates in homogeneous background and non-homogeneous situation caused by multiple targets and clutter edge. The analytical models of this scheme for the performance evaluation are given. It is shown that the OSSO-CFAR with binary integration can greatly improve the detection performance with respect to the single pulse processing case. As the clutter background becomes spiky, a high threshold S of binary integration (S/M) is required in order to obtain a good detection performance in homogeneous background. Moreover, the false alarm performance of the OSSO-CFAR with binary integration is more sensitive to the changes of shape parameter or power level of the clutter background.展开更多
Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fastresponse gas-monitoring systems.However,the conventional plasma discharge system is bulky,operates at a high temper...Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fastresponse gas-monitoring systems.However,the conventional plasma discharge system is bulky,operates at a high temperature,and inappropriate for volatile organic compounds(VOCs)concentration detection.Therefore,we report a machine learning(ML)-enhanced ion mobility analyzer with a triboelectric-based ionizer,which offers good ion mobility selectivity and VOC recognition ability with a small-sized device and non-strict operating environment.Based on the charge accumulation mechanism,a multi-switched manipulation triboelectric nanogenerator(SM-TENG)can provide a direct current(DC)bias at the order of a few hundred,which can be further leveraged as the power source to obtain a unique and repeatable discharge characteristic of different VOCs,and their mixtures,with a special tip-plate electrode configuration.Aiming to tackle the grand challenge in the detection of multiple VOCs,the ML-enhanced ion mobility analysis method was successfully demonstrated by extracting specific features automatically from ion mobility spectrometry data with ML algorithms,which significantly enhance the detection ability of the SM-TENG based VOC analyzer,showing a portable real-time VOC monitoring solution with rapid response and low power consumption for future internet of things based environmental monitoring applications.展开更多
文摘累积和检测方法是根据声呐目标信号出现与消失时概率密度函数(Probability Distribution Function,PDF)的变化进行有效的瞬态信号检测。以非高斯模型t分布假设替代传统的高斯分布方差变化假设作为描述瞬态信号的PDF形式,推导了累积和检验统计量的表达、更新量PDF求取的数值方法,利用快速傅里叶变换法计算了门限和自由度等检测参数。利用仿真的落水信号、船体加速信号和消声水池实验数据进行检验。结果表明,基于t分布假设的累积和方法对瞬态脉冲信号的检测效果优于常规累积和方法,能更快地响应信号变化,更好地抑制背景干扰。
基金Supported by the National Natural Science Foundation of China (No.61179016)
文摘The performance of the Ordered-Statistic Smallest Of (OSSO) Constant False Alarm Rate (CFAR) with binary integration in Weibull background with known shape parameter is analyzed, in the cases that the processor operates in homogeneous background and non-homogeneous situation caused by multiple targets and clutter edge. The analytical models of this scheme for the performance evaluation are given. It is shown that the OSSO-CFAR with binary integration can greatly improve the detection performance with respect to the single pulse processing case. As the clutter background becomes spiky, a high threshold S of binary integration (S/M) is required in order to obtain a good detection performance in homogeneous background. Moreover, the false alarm performance of the OSSO-CFAR with binary integration is more sensitive to the changes of shape parameter or power level of the clutter background.
基金supported by the research grant of‘‘Chip-Scale MEMS Micro-Spectrometer for Monitoring Harsh Industrial Gases”(R-263-000-C91-305)at the National University of Singapore(NUS),Singaporethe research grant of RIE Advanced Manufacturing and Engineering(AME)programmatic grant A18A4b0055‘‘Nanosystems at the Edge”at NUS,Singapore。
文摘Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fastresponse gas-monitoring systems.However,the conventional plasma discharge system is bulky,operates at a high temperature,and inappropriate for volatile organic compounds(VOCs)concentration detection.Therefore,we report a machine learning(ML)-enhanced ion mobility analyzer with a triboelectric-based ionizer,which offers good ion mobility selectivity and VOC recognition ability with a small-sized device and non-strict operating environment.Based on the charge accumulation mechanism,a multi-switched manipulation triboelectric nanogenerator(SM-TENG)can provide a direct current(DC)bias at the order of a few hundred,which can be further leveraged as the power source to obtain a unique and repeatable discharge characteristic of different VOCs,and their mixtures,with a special tip-plate electrode configuration.Aiming to tackle the grand challenge in the detection of multiple VOCs,the ML-enhanced ion mobility analysis method was successfully demonstrated by extracting specific features automatically from ion mobility spectrometry data with ML algorithms,which significantly enhance the detection ability of the SM-TENG based VOC analyzer,showing a portable real-time VOC monitoring solution with rapid response and low power consumption for future internet of things based environmental monitoring applications.
