The problem of increasing computation in pace with the growth of dimension is discussed for arbitrary dimensional frequency estimation of complex sinusoid signals. The conception of matrix core, the form of which do...The problem of increasing computation in pace with the growth of dimension is discussed for arbitrary dimensional frequency estimation of complex sinusoid signals. The conception of matrix core, the form of which doesnt change with dimension, is put forward. The deduced estimation formula shows that a N dimensional frequency estimation could be obtained by N one dimensional calculations. Obviously, while dimension increases, this method could reduce much computation.展开更多
A new fast and accurate method for estimating the frequency of a complex sinusoid in complex white Gaussian environments is proposed. The new estimator comprises of applications of low-pass filtering, decimation, and ...A new fast and accurate method for estimating the frequency of a complex sinusoid in complex white Gaussian environments is proposed. The new estimator comprises of applications of low-pass filtering, decimation, and frequency estimation by linear prediction. It is computationally efficient yet obtains the Crazner-Rao bound at moderate signal-to-noise ratios. And it is well suited for real time applications requiring precise frequency estimation. Simulation results are included to demonstrate the performance of the proposed method.展开更多
文摘The problem of increasing computation in pace with the growth of dimension is discussed for arbitrary dimensional frequency estimation of complex sinusoid signals. The conception of matrix core, the form of which doesnt change with dimension, is put forward. The deduced estimation formula shows that a N dimensional frequency estimation could be obtained by N one dimensional calculations. Obviously, while dimension increases, this method could reduce much computation.
文摘A new fast and accurate method for estimating the frequency of a complex sinusoid in complex white Gaussian environments is proposed. The new estimator comprises of applications of low-pass filtering, decimation, and frequency estimation by linear prediction. It is computationally efficient yet obtains the Crazner-Rao bound at moderate signal-to-noise ratios. And it is well suited for real time applications requiring precise frequency estimation. Simulation results are included to demonstrate the performance of the proposed method.
