基于一种滤波-误差算法的主动隔振技术

针对船载动力机械隔振降噪问题,本文将多通道的滤波-误差信号主动控制算法应用于主动隔振系统,采用子结构导纳方法建立基于导纳矩阵的双层隔振系统主动控制的次级通道模型。在一种改进的滤波-误差信号算法的基础上,利用实验得到的双层隔振系统的次级通道辨识数据构建出新的误差滤波器,并进行主动控制算法仿真。仿真结果表明,通过合理的构建误差滤波器,采用改进的滤波-误差信号主动控制算法的主动隔振系统对于周期激励具有较好的隔振效果。

Tests and error analysis of a self-positioning shearer operating at a manless working face

Self-positioning of a shearer is the key technology for mining with a man-less working face. In an underground coal mine all radio navigation; satellite positioning or celestial navigation methods have their limitations. We analyzed an inertial navi-gation system intended to guide the movement a shearer and designed a self-positioning device for the shearer. Simulation tests were also performed on the system. We analyzed the errors observed in these tests to show that the main reason for the low preci-sion of the self-positioning system is accumulated error in the inertial sensor. A Kalman filtering algorithm used in combination with the shearer motion model effectively reduces the measurement errors of the self-positioning system by compensating for gyroscopic drift. Finally, we built an error compensation model to reduce accumulated errors using continuous correction to provide self-positioning of the shearer within a certain range of accuracy.

Chaotic pulse position modulation ultra-wideband system based on particle filtering

Traditional chaotic pulse position modulation(CPPM)system has many drawbacks.It introduces delay into the feedback loop,which will lead to divergence of chaotic map easily.The wrong decision of data will cause error propagation.Mismatch of parameters and synchronization error between the receiver and transmitter will arouse high bit error rate.To solve these problems,a demodulation algorithm of CPPM based on particle filtering is proposed.According to the mathematical model of the system,it tracks the real signal by online separation in demodulation.Simulation results show that the proposed method can track the true signal better than the traditional CPPM scheme.What's more,it has good synchronization robustness,reduced error propagation by wrong decision and low bit error rate.

Wireless location algorithm using digital broadcasting signals based on neural network

In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.

Precise Background Noise Power Estimate for 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.

WLS filter for reducing atmospheric effects in spaceborne SAR tomography

A new approach was presented to eliminate the atmosphere-induced phase error utilizing only the single look complex(SLC) synthetic aperture radar(SAR) image set. This method exploited the space-invariance characteristic of phase error components contained in image pixels and estimates the phase error using the weighted least-squares(WLS) filter. Actually, this sort of method can be classified as autofocus algorithm which was generally applied in airborne SAR 2-D imaging to compensate the phase error introduced by airplane's nonideal motion. Real data processing, which is relevant to Honda center and Angel stadium of Anaheim test-sites and acquired by Envisat-ASAR during the period from June 2004 to October 2007, was carried out to evaluate this WLS estimation algorithm. Experimental results show that the phase error estimated from WLS filter is very accurate and the focusing quality along NSR dimension is improved prominently via phase correction, which verifies the practicability of this new method.

Raman spectroscopy de-noising based on EEMD combined with VS-LMS algorithm

This paper proposes a novel de-noising algorithm based on ensemble empirical mode decomposition(EEMD) and the variable step size least mean square(VS-LMS) adaptive filter.The noise of the high frequency part of spectrum will be removed through EEMD,and then the VS-LMS algorithm is utilized for overall de-noising.The EEMD combined with VS-LMS algorithm can not only preserve the detail and envelope of the effective signal,but also improve the system stability.When the method is used on pure R6G,the signal-to-noise ratio(SNR) of Raman spectrum is lower than 10dB.The de-noising superiority of the proposed method in Raman spectrum can be verified by three evaluation standards of SNR,root mean square error(RMSE) and the correlation coefficient ρ.