In this paper, demodulation performance of first-order cyclic statistics and second-order cyclic statistics for amplitude modulation signals is introduced. By theoretical research, it is proved that cyclic mean does n...In this paper, demodulation performance of first-order cyclic statistics and second-order cyclic statistics for amplitude modulation signals is introduced. By theoretical research, it is proved that cyclic mean does not possess demodulation performance for amplitude modulation signals, but cyclic autocorrelation function can extract frequency components from amplitude modulation signals. Based on the above, both cyclic autocorrelation function and envelope demodulation based on Hilbert transform are compared. The results show that the two kinds of analysis methods have similar performance for demodulation, in the mean time it is pointed out that they also possess essential distinction, and cyclic autocorrelation function is better to demodulate amplitude modulation signals. Key words cyclic statistics - demodulation - feature extraction Project supported by the National Natural Science Foundation of China(Grant No. 50175068)展开更多
By exploiting thvorable characteristics of a uniIbrm cross-array, a passive localization algorithm of narrowband sources in the spherical coordinates （azimuth, elevation and range） is proposed. Based on the properly...By exploiting thvorable characteristics of a uniIbrm cross-array, a passive localization algorithm of narrowband sources in the spherical coordinates （azimuth, elevation and range） is proposed. Based on the properly chosen sensor outputs, we compute the third-order cyclic moment matrices, and exploit a pre-calibration technique to eliminate multiplicative noise. Then, we construct a parallel factor （PARAFAC） model, and adopt trilinear altemating least squares regression （TALS） to estimate three-dimensional （3-D） near-field parameters. The investigated algorithm is efficient in the sense that it can eliminate multiplicative noise and additive noise, provide the improved estimation accuracy, as well as avoid the parameter-pairing procedure. Simulation results are carried out to demonstrate the performance of the proposed algorithm.展开更多
文摘In this paper, demodulation performance of first-order cyclic statistics and second-order cyclic statistics for amplitude modulation signals is introduced. By theoretical research, it is proved that cyclic mean does not possess demodulation performance for amplitude modulation signals, but cyclic autocorrelation function can extract frequency components from amplitude modulation signals. Based on the above, both cyclic autocorrelation function and envelope demodulation based on Hilbert transform are compared. The results show that the two kinds of analysis methods have similar performance for demodulation, in the mean time it is pointed out that they also possess essential distinction, and cyclic autocorrelation function is better to demodulate amplitude modulation signals. Key words cyclic statistics - demodulation - feature extraction Project supported by the National Natural Science Foundation of China(Grant No. 50175068)
基金supported by the National Natural Science Foundation of China (61171137)the New Century Excellent Talents in University (NECT) of China (NECT-09-0426)
文摘By exploiting thvorable characteristics of a uniIbrm cross-array, a passive localization algorithm of narrowband sources in the spherical coordinates （azimuth, elevation and range） is proposed. Based on the properly chosen sensor outputs, we compute the third-order cyclic moment matrices, and exploit a pre-calibration technique to eliminate multiplicative noise. Then, we construct a parallel factor （PARAFAC） model, and adopt trilinear altemating least squares regression （TALS） to estimate three-dimensional （3-D） near-field parameters. The investigated algorithm is efficient in the sense that it can eliminate multiplicative noise and additive noise, provide the improved estimation accuracy, as well as avoid the parameter-pairing procedure. Simulation results are carried out to demonstrate the performance of the proposed algorithm.
