The reconstruction of background noise from an error signal of an adaptive filter is a key issue for developing Variable Step-Size Normalized Least Mean Square (VSS-NLMS) algorithm in the context of Echo Cancellation ...The reconstruction of background noise from an error signal of an adaptive filter is a key issue for developing Variable Step-Size Normalized Least Mean Square (VSS-NLMS) algorithm in the context of Echo Cancellation (EC). The core parameter in this algorithm is the Background Noise Power (BNP); in the estimation of BNP, the power difference between the desired signal and the filter output, statistically equaling to the error signal power, has been widely used in a rough manner. In this study, a precise BNP estimate is implemented by multiplying the rough estimate with a corrective factor, taking into consideration the fact that the error signal consists of background noise and misalignment noise. This corrective factor is obtained by subtracting half of the latest VSS value from 1 after analyzing the ratio of BNP to the misalignment noise. Based on the precise BNP estimate, the PVSS-NLMS algorithm suitable for the EC system is eventually proposed. In practice, the proposed algorithm exhibits a significant advantage of easier controllability application, as prior knowledge of the EC environment can be neglected. The simulation results support the preciseness of the BNP estimation and the effectiveness of the proposed algorithm.展开更多
The adaptive algorithm used for echo cancellation(EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm(APA) is a better alternative than normalized least mean ...The adaptive algorithm used for echo cancellation(EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm(APA) is a better alternative than normalized least mean square(NLMS) algorithm in EC applications where the input signal is highly correlated. Since the APA with a constant step-size has to make compromise between the performance criteria 1) and 2), a variable step-size APA(VSS-APA) provides a more reliable solution. A nonparametric VSS-APA(NPVSS-APA) is proposed by recovering the background noise within the error signal instead of cancelling the a posteriori errors. The most problematic term of its variable step-size formula is the value of background noise power(BNP). The power difference between the desired signal and output signal, which equals the power of error signal statistically, has been considered the BNP estimate in a rough manner. Considering that the error signal consists of background noise and misalignment noise, a precise BNP estimate is achieved by multiplying the rough estimate with a corrective factor. After the analysis on the power ratio of misalignment noise to background noise of APA, the corrective factor is formulated depending on the projection order and the latest value of variable step-size. The new algorithm which does not require any a priori knowledge of EC environment has the advantage of easier controllability in practical application. The simulation results in the EC context indicate the accuracy of the proposed BNP estimate and the more effective behavior of the proposed algorithm compared with other versions of APA class.展开更多
Lightning is a typical example of an instantaneous random point source target. It has close connection with severe convective phenomena such as a thunderstorm, whose distribution, variation, position and forecasting c...Lightning is a typical example of an instantaneous random point source target. It has close connection with severe convective phenomena such as a thunderstorm, whose distribution, variation, position and forecasting can be acquired through lightning observation. In this paper, we discuss the way to achieve instantaneous lightning signal intensification and detection from geostationary orbit by using the differences between the lightning signal and the slowly changing background noise such as that of cloud, land and ocean, combining three methods, spectral filtering, spatial filtering and background noise, enabling removal between frames. After six months of operation in orbit, lightning within the coverage of the Geostationary Lightning Imager was effectively detected, strongly supporting the case for shorttime and real-time early warning, forecasting and tracking of severe convective phenomena in China.展开更多
文摘The reconstruction of background noise from an error signal of an adaptive filter is a key issue for developing Variable Step-Size Normalized Least Mean Square (VSS-NLMS) algorithm in the context of Echo Cancellation (EC). The core parameter in this algorithm is the Background Noise Power (BNP); in the estimation of BNP, the power difference between the desired signal and the filter output, statistically equaling to the error signal power, has been widely used in a rough manner. In this study, a precise BNP estimate is implemented by multiplying the rough estimate with a corrective factor, taking into consideration the fact that the error signal consists of background noise and misalignment noise. This corrective factor is obtained by subtracting half of the latest VSS value from 1 after analyzing the ratio of BNP to the misalignment noise. Based on the precise BNP estimate, the PVSS-NLMS algorithm suitable for the EC system is eventually proposed. In practice, the proposed algorithm exhibits a significant advantage of easier controllability application, as prior knowledge of the EC environment can be neglected. The simulation results support the preciseness of the BNP estimation and the effectiveness of the proposed algorithm.
文摘The adaptive algorithm used for echo cancellation(EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm(APA) is a better alternative than normalized least mean square(NLMS) algorithm in EC applications where the input signal is highly correlated. Since the APA with a constant step-size has to make compromise between the performance criteria 1) and 2), a variable step-size APA(VSS-APA) provides a more reliable solution. A nonparametric VSS-APA(NPVSS-APA) is proposed by recovering the background noise within the error signal instead of cancelling the a posteriori errors. The most problematic term of its variable step-size formula is the value of background noise power(BNP). The power difference between the desired signal and output signal, which equals the power of error signal statistically, has been considered the BNP estimate in a rough manner. Considering that the error signal consists of background noise and misalignment noise, a precise BNP estimate is achieved by multiplying the rough estimate with a corrective factor. After the analysis on the power ratio of misalignment noise to background noise of APA, the corrective factor is formulated depending on the projection order and the latest value of variable step-size. The new algorithm which does not require any a priori knowledge of EC environment has the advantage of easier controllability in practical application. The simulation results in the EC context indicate the accuracy of the proposed BNP estimate and the more effective behavior of the proposed algorithm compared with other versions of APA class.
文摘Lightning is a typical example of an instantaneous random point source target. It has close connection with severe convective phenomena such as a thunderstorm, whose distribution, variation, position and forecasting can be acquired through lightning observation. In this paper, we discuss the way to achieve instantaneous lightning signal intensification and detection from geostationary orbit by using the differences between the lightning signal and the slowly changing background noise such as that of cloud, land and ocean, combining three methods, spectral filtering, spatial filtering and background noise, enabling removal between frames. After six months of operation in orbit, lightning within the coverage of the Geostationary Lightning Imager was effectively detected, strongly supporting the case for shorttime and real-time early warning, forecasting and tracking of severe convective phenomena in China.
