在运动传感器网络中，应用直接定位（Direct Position Determination，DPD）算法可大幅改善系统的定位性能，但同时也带来了计算量高、算法稳定性差等问题。对此，本文提出了一种基于时频差的分布式快速DPD算法。利用互模糊函数与矩阵...在运动传感器网络中，应用直接定位（Direct Position Determination，DPD）算法可大幅改善系统的定位性能，但同时也带来了计算量高、算法稳定性差等问题。对此，本文提出了一种基于时频差的分布式快速DPD算法。利用互模糊函数与矩阵相容范数计算分别替代了原DPD算法中复杂的大规模矩阵运算和最大特征值求解，并据此设计出了一种有效的分布式部署计算方法，充分利用了传感器网络的计算资源。仿真表明，该方法在定位性能与原DPD算法相当的情况下，大幅简化了计算量、提高了算法稳定性，具有工程应用价值。展开更多
In the long distance GIL under certain conditions, this paper researches and realizes detection of PD characters and accurate fault localization through UHF coupling sensors at different positions of the GIL pipeline....In the long distance GIL under certain conditions, this paper researches and realizes detection of PD characters and accurate fault localization through UHF coupling sensors at different positions of the GIL pipeline. The main methods for the detection are UHF signal amplitude difference （DOA） and time difference （TOF）. We analyze the localization error by using TE and TEM component and high order TE mode component in electromagnetic coaxial wave guide theory. Research and field test prove the DOA detection error can meet the requirements of real-time online diagnosis and for history tracking analysis. The error of TOF detection method can be controlled within 3% and can be applied to the site.展开更多
Passive source localization via a maximum likelihood （ML） estimator can achieve a high accuracy but involves high calculation burdens, especially when based on time-of-arrival and frequency-of-arrival measurements f...Passive source localization via a maximum likelihood （ML） estimator can achieve a high accuracy but involves high calculation burdens, especially when based on time-of-arrival and frequency-of-arrival measurements for its internal nonlinearity and nonconvex nature. In this paper, we use the Pincus theorem and Monte Carlo importance sampling （MCIS） to achieve an approximate global solution to the ML problem in a computationally efficient manner. The main contribution is that we construct a probability density function （PDF） of Gaussian distribution, which is called an important function for efficient sampling, to approximate the ML estimation related to complicated distributions. The improved performance of the proposed method is at- tributed to the optimal selection of the important function and also the guaranteed convergence to a global maximum. This process greatly reduces the amount of calculation, but an initial solution estimation is required resulting from Taylor series expansion. However, the MCIS method is robust to this prior knowledge for point sampling and correction of importance weights. Simulation results show that the proposed method can achieve the Cram6r-Rao lower bound at a moderate Gaussian noise level and outper- forms the existing methods.展开更多
文摘在运动传感器网络中，应用直接定位（Direct Position Determination，DPD）算法可大幅改善系统的定位性能，但同时也带来了计算量高、算法稳定性差等问题。对此，本文提出了一种基于时频差的分布式快速DPD算法。利用互模糊函数与矩阵相容范数计算分别替代了原DPD算法中复杂的大规模矩阵运算和最大特征值求解，并据此设计出了一种有效的分布式部署计算方法，充分利用了传感器网络的计算资源。仿真表明，该方法在定位性能与原DPD算法相当的情况下，大幅简化了计算量、提高了算法稳定性，具有工程应用价值。
文摘In the long distance GIL under certain conditions, this paper researches and realizes detection of PD characters and accurate fault localization through UHF coupling sensors at different positions of the GIL pipeline. The main methods for the detection are UHF signal amplitude difference （DOA） and time difference （TOF）. We analyze the localization error by using TE and TEM component and high order TE mode component in electromagnetic coaxial wave guide theory. Research and field test prove the DOA detection error can meet the requirements of real-time online diagnosis and for history tracking analysis. The error of TOF detection method can be controlled within 3% and can be applied to the site.
基金Project supported by the National Natural Science Foundation of China （No. 61201381 ） and the China Postdoctoral Science Foundation （No. 2016M592989）
文摘Passive source localization via a maximum likelihood （ML） estimator can achieve a high accuracy but involves high calculation burdens, especially when based on time-of-arrival and frequency-of-arrival measurements for its internal nonlinearity and nonconvex nature. In this paper, we use the Pincus theorem and Monte Carlo importance sampling （MCIS） to achieve an approximate global solution to the ML problem in a computationally efficient manner. The main contribution is that we construct a probability density function （PDF） of Gaussian distribution, which is called an important function for efficient sampling, to approximate the ML estimation related to complicated distributions. The improved performance of the proposed method is at- tributed to the optimal selection of the important function and also the guaranteed convergence to a global maximum. This process greatly reduces the amount of calculation, but an initial solution estimation is required resulting from Taylor series expansion. However, the MCIS method is robust to this prior knowledge for point sampling and correction of importance weights. Simulation results show that the proposed method can achieve the Cram6r-Rao lower bound at a moderate Gaussian noise level and outper- forms the existing methods.
