Since the CPU of embed system has some limitation in operating speed, a new filter was put forward which implemented mountain template convolution by performing rectangle template convolution two times. It can obtain ...Since the CPU of embed system has some limitation in operating speed, a new filter was put forward which implemented mountain template convolution by performing rectangle template convolution two times. It can obtain time and frequency localization with computational complexity greatly reduced. This algorithm was applied to lightning waveforms (include chopped waveforms) parameter calculation. It simplifies the computation and the results pretreated by this algorithm are in accord with IEC1083-2 completely. It was applied in embed system successfully. Its capability in frequency restraining was researched. The validity of the algorithm was proved in theory when processing lightning waves. The standard sources and the processing results are consistent completely.展开更多
A closed-form approximate maximum likelihood（AML） algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival（TDOA） and frequency difference of arr...A closed-form approximate maximum likelihood（AML） algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival（TDOA） and frequency difference of arrival（FDOA） measurements of a signal received at a number of receivers.The maximum likelihood（ML） technique is a powerful tool to solve this problem.But a direct approach that uses the ML estimator to solve the localization problem is exhaustive search in the solution space,and it is very computationally expensive,and prohibits real-time processing.On the basis of ML function,a closed-form approximate solution to the ML equations can be obtained,which can allow real-time implementation as well as global convergence.Simulation results show that the proposed estimator achieves better performance than the two-step weighted least squares（WLS） approach,which makes it possible to attain the Cramér-Rao lower bound（CRLB） at a sufficiently high noise level before the threshold effect occurs.展开更多
文摘Since the CPU of embed system has some limitation in operating speed, a new filter was put forward which implemented mountain template convolution by performing rectangle template convolution two times. It can obtain time and frequency localization with computational complexity greatly reduced. This algorithm was applied to lightning waveforms (include chopped waveforms) parameter calculation. It simplifies the computation and the results pretreated by this algorithm are in accord with IEC1083-2 completely. It was applied in embed system successfully. Its capability in frequency restraining was researched. The validity of the algorithm was proved in theory when processing lightning waves. The standard sources and the processing results are consistent completely.
基金National High-tech Research and Development Program of China (2010AA7010422,2011AA7014061)
文摘A closed-form approximate maximum likelihood（AML） algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival（TDOA） and frequency difference of arrival（FDOA） measurements of a signal received at a number of receivers.The maximum likelihood（ML） technique is a powerful tool to solve this problem.But a direct approach that uses the ML estimator to solve the localization problem is exhaustive search in the solution space,and it is very computationally expensive,and prohibits real-time processing.On the basis of ML function,a closed-form approximate solution to the ML equations can be obtained,which can allow real-time implementation as well as global convergence.Simulation results show that the proposed estimator achieves better performance than the two-step weighted least squares（WLS） approach,which makes it possible to attain the Cramér-Rao lower bound（CRLB） at a sufficiently high noise level before the threshold effect occurs.
