The local wave method is a very good time-frequency method for nonstationaryvibration signal analysis. But the interfering noise has a big influence on the accuracy oftime-frequency analysis. The wavelet packet de-noi...The local wave method is a very good time-frequency method for nonstationaryvibration signal analysis. But the interfering noise has a big influence on the accuracy oftime-frequency analysis. The wavelet packet de-noising method can eliminate the interference ofnoise and improve the signal-noise-ratio. This paper uses the local wave method to decompose thede-noising signal and perform a time-frequency analysis. We can get better characteristics. Finally,an example of wavelet packet de-noising and a local wave time-frequency spectrum application ofdiesel engine surface vibration signal is put forward.展开更多
Passive localization by a single moving observer using Time of Arrival(TOA)only with an unknown Signal Repetition Interval(SRI)is investigated in this paper.Observability analysis is performed first.The observability ...Passive localization by a single moving observer using Time of Arrival(TOA)only with an unknown Signal Repetition Interval(SRI)is investigated in this paper.Observability analysis is performed first.The observability condition for uniquely determining the emitter position and SRI is derived.The conditional Cramer-Rao Lower Bound(CRLB)is also analyzed.It is found that the ambiguity of the SRI integer of the first TOA does not affect the theoretical estimation precision of the emitter position and SRI.A Reference-Fixed Differential TOA(RFDTOA)-based Iterative Maximum Likelihood Estimator(IMLE)is proposed,which only needs O(M)computational operations.Theoretical analysis and simulation results show that the Mean Square Error(MSE)of the proposed algorithm could attain the CRLB with moderate Gaussian measurement noise.展开更多
The sparse nature of location finding in the spatial domain makes it possible to exploit the Compressive Sensing (CS) theory for wireless location.CS-based location algorithm can largely reduce the number of online me...The sparse nature of location finding in the spatial domain makes it possible to exploit the Compressive Sensing (CS) theory for wireless location.CS-based location algorithm can largely reduce the number of online measurements while achieving a high level of localization accuracy,which makes the CS-based solution very attractive for indoor positioning.However,CS theory offers exact deterministic recovery of the sparse or compressible signals under two basic restriction conditions of sparsity and incoherence.In order to achieve a good recovery performance of sparse signals,CS-based solution needs to construct an efficient CS model.The model must satisfy the practical application requirements as well as following theoretical restrictions.In this paper,we propose two novel CS-based location solutions based on two different points of view:the CS-based algorithm with raising-dimension pre-processing and the CS-based algorithm with Minor Component Analysis (MCA).Analytical studies and simulations indicate that the proposed novel schemes achieve much higher localization accuracy.展开更多
针对如何快速、准确地提取生物体触电故障暂态信号中的电力参数问题,提出了一种基于局部均值分解(local mean decomposition,LMD)的生物体触电时总泄漏电流信号瞬时参数提取方法,该方法首先利用局部均值分解将生物体触电时的总泄漏电流...针对如何快速、准确地提取生物体触电故障暂态信号中的电力参数问题,提出了一种基于局部均值分解(local mean decomposition,LMD)的生物体触电时总泄漏电流信号瞬时参数提取方法,该方法首先利用局部均值分解将生物体触电时的总泄漏电流信号分解为一组乘积函数分量之和,每个乘积函数(product function,PF)分量可以表示为一个调幅信号和一个调频信号的乘积,然后由调幅信号和调频信号分别计算得到信号的瞬时幅值和瞬时频率。与采用希尔伯特黄变换方法相比,LMD具有瞬时频率曲线波动小和瞬时幅值函数端部失真小等优点。仿真信号分析结果表明:对测试信号进行LMD和经验模态分解(empirical mode decomposition,EMD)分解分别得到3个PF分量和5个IMF(intrinsic mode function)分量,分解前后信号的能量变化值分别为0.2851、0.5633,且LMD比EMD所需分解时间短0.0743s,与Hilbert变换相比,该文方法计算的瞬时幅值和瞬时频率更为平滑,在一定程度上避免了Hilbert变换计算过程中的负频率和端点效应现象。试验信号分析结果表明:对消噪后的总泄漏电流信号进行LMD和EMD分解,分别得到5和6个分量,分解前后信号的能量变化值各为0.5574、0.8896,所用分解时间分别为0.0835、0.2479 s;在求取瞬时频率方面,LMD方法求取的主导分量瞬时频率可判定生物体触电时刻,而经Hilbert变换求取的瞬时频率不仅无法判定生物体触电时刻,还出现了负的频率值,无法解释其物理意义;在求取瞬时幅值方面,该文方法与Hilbert变换求取的触电前总泄漏电流信号的瞬时幅值的平均值分别为11.3240、12.3728 m A,与原生物体无触电时总泄漏电流的幅值11.3538 m A的绝对误差分别为0.0298、1.0190 m A,另外,2种方法求取的生物体触电后总泄漏电流信号的瞬时幅值与原生物体触电后总泄漏电流的幅值的绝对误差分别为0.4340、0.6643 m A。因此,仿真信号和试验信号分析结果均证明所提方法是有效和可行的。展开更多
文摘The local wave method is a very good time-frequency method for nonstationaryvibration signal analysis. But the interfering noise has a big influence on the accuracy oftime-frequency analysis. The wavelet packet de-noising method can eliminate the interference ofnoise and improve the signal-noise-ratio. This paper uses the local wave method to decompose thede-noising signal and perform a time-frequency analysis. We can get better characteristics. Finally,an example of wavelet packet de-noising and a local wave time-frequency spectrum application ofdiesel engine surface vibration signal is put forward.
基金supported by the National Natural Science Foundation of China(No.61901494).
文摘Passive localization by a single moving observer using Time of Arrival(TOA)only with an unknown Signal Repetition Interval(SRI)is investigated in this paper.Observability analysis is performed first.The observability condition for uniquely determining the emitter position and SRI is derived.The conditional Cramer-Rao Lower Bound(CRLB)is also analyzed.It is found that the ambiguity of the SRI integer of the first TOA does not affect the theoretical estimation precision of the emitter position and SRI.A Reference-Fixed Differential TOA(RFDTOA)-based Iterative Maximum Likelihood Estimator(IMLE)is proposed,which only needs O(M)computational operations.Theoretical analysis and simulation results show that the Mean Square Error(MSE)of the proposed algorithm could attain the CRLB with moderate Gaussian measurement noise.
基金supported by the National Natural Science Foundation of China under Grant No.61001119the Fund for Creative Research Groups of China under Grant No.61121001
文摘The sparse nature of location finding in the spatial domain makes it possible to exploit the Compressive Sensing (CS) theory for wireless location.CS-based location algorithm can largely reduce the number of online measurements while achieving a high level of localization accuracy,which makes the CS-based solution very attractive for indoor positioning.However,CS theory offers exact deterministic recovery of the sparse or compressible signals under two basic restriction conditions of sparsity and incoherence.In order to achieve a good recovery performance of sparse signals,CS-based solution needs to construct an efficient CS model.The model must satisfy the practical application requirements as well as following theoretical restrictions.In this paper,we propose two novel CS-based location solutions based on two different points of view:the CS-based algorithm with raising-dimension pre-processing and the CS-based algorithm with Minor Component Analysis (MCA).Analytical studies and simulations indicate that the proposed novel schemes achieve much higher localization accuracy.
文摘针对如何快速、准确地提取生物体触电故障暂态信号中的电力参数问题,提出了一种基于局部均值分解(local mean decomposition,LMD)的生物体触电时总泄漏电流信号瞬时参数提取方法,该方法首先利用局部均值分解将生物体触电时的总泄漏电流信号分解为一组乘积函数分量之和,每个乘积函数(product function,PF)分量可以表示为一个调幅信号和一个调频信号的乘积,然后由调幅信号和调频信号分别计算得到信号的瞬时幅值和瞬时频率。与采用希尔伯特黄变换方法相比,LMD具有瞬时频率曲线波动小和瞬时幅值函数端部失真小等优点。仿真信号分析结果表明:对测试信号进行LMD和经验模态分解(empirical mode decomposition,EMD)分解分别得到3个PF分量和5个IMF(intrinsic mode function)分量,分解前后信号的能量变化值分别为0.2851、0.5633,且LMD比EMD所需分解时间短0.0743s,与Hilbert变换相比,该文方法计算的瞬时幅值和瞬时频率更为平滑,在一定程度上避免了Hilbert变换计算过程中的负频率和端点效应现象。试验信号分析结果表明:对消噪后的总泄漏电流信号进行LMD和EMD分解,分别得到5和6个分量,分解前后信号的能量变化值各为0.5574、0.8896,所用分解时间分别为0.0835、0.2479 s;在求取瞬时频率方面,LMD方法求取的主导分量瞬时频率可判定生物体触电时刻,而经Hilbert变换求取的瞬时频率不仅无法判定生物体触电时刻,还出现了负的频率值,无法解释其物理意义;在求取瞬时幅值方面,该文方法与Hilbert变换求取的触电前总泄漏电流信号的瞬时幅值的平均值分别为11.3240、12.3728 m A,与原生物体无触电时总泄漏电流的幅值11.3538 m A的绝对误差分别为0.0298、1.0190 m A,另外,2种方法求取的生物体触电后总泄漏电流信号的瞬时幅值与原生物体触电后总泄漏电流的幅值的绝对误差分别为0.4340、0.6643 m A。因此,仿真信号和试验信号分析结果均证明所提方法是有效和可行的。
