基于改进的LMS-CSP算法管道泄漏点定位方法研究
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摘要
针对变步长最小均方(LMS)自适应滤波算法的步长因子在算法收敛程度加深时快速变小的问题,提出在步长因子迭代过程中引入历史误差平方的遗忘加权和补偿项,并加入滑动窗,提高算法的收敛速度,同时减小了稳态失调。利用互功率谱相位(CSP)方法对信号进行时延估计,采用对互功率谱函数进行多帧加权平滑的方式来提高算法的抗噪能力。实验仿真结果表明,改进后的算法在信噪比较低的条件下,也能获取到有效的时延估计,证明新方法具有很好的鲁棒性,是有效的。
According to the issue that step factor of variable step length least mean square( LMS) adaptive filtering algorithm reduces quickly when the algorithm convergence degree deepen,this paper introduced the sum of forgetting weighted for historical error square and sliding window to improve the convergence rate of algorithm and reduce the steady-state misadjustment in the iterative process of step factor. It used the cross-power spectrum phase( CSP) method for signal time delay estimation,adopted the method of multi-frame weighted smoothing for cross-power spectrum function to improve algorithm's anti-noise ability. Simulation results show that the improved algorithm can also get the effective time delay estimation at low SNR conditions,proved that the new method has good robustness and is effective.
According to the issue that step factor of variable step length least mean square( LMS) adaptive filtering algorithm reduces quickly when the algorithm convergence degree deepen,this paper introduced the sum of forgetting weighted for historical error square and sliding window to improve the convergence rate of algorithm and reduce the steady-state misadjustment in the iterative process of step factor. It used the cross-power spectrum phase( CSP) method for signal time delay estimation,adopted the method of multi-frame weighted smoothing for cross-power spectrum function to improve algorithm's anti-noise ability. Simulation results show that the improved algorithm can also get the effective time delay estimation at low SNR conditions,proved that the new method has good robustness and is effective.
引文
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[12]彭继慎,刘爽,安丽.低信噪比下基于新型变步长LMS的自适应滤波算法[J].传感技术学报,2013,26(8):1116-1120.
[13]田福庆,罗荣,李克玉,等.基于改进的双曲正切函数变步长LMS算法[J].系统工程与电子技术,2012,34(9):1758-1763.
[14]耿攀飞,夏双志.基于互功率谱法的多天线时延估计研究[J].电子技术与软件工程,2014(1):193-194.
[15]陈颖,吴长奇.基于麦克风阵列的声源定位算法研究及DSP实现[J].燕山大学学报,2013,37(1):63-67,74.
[2]潘碧霞,徐长航,曹国梁,等.管道泄漏声发射信号的传播特性[J].油气储运,2013(10):1141-1145.
[3]吴晓,靳世久,李一博,等.基于麦克风阵列声音信号定位方法的研究[J].传感技术学报,2010,23(5):682-686.
[4]金中薇,姜明顺,隋青美,等.基于广义互相关时延估计算法的声发射定位技术[J].传感技术学报,2013,26(11):1513-1518.
[5]Belina F,Irving J,Ernst J,et al.Analysis of an iterative deconvolution approach for estimating the source wavelet during waveform inversion of crosshole georadar data[J].Journal of Applied Geophysics,2011,78:20.
[6]杨荣根,任明武,杨静宇.泄漏信号定位中的时延估计算法[J].信息与控制,2011,40(1):8-13.
[7]任自钊,徐建城,闫永鹏.一种改进的变步长LMS自适应滤波算法及性能分析[J].计算机应用研究,2011,28(3):954-956.
[8]Gao Y,Brennan M J,Joseph P F.On the effects of reflections on time delay estimation for leak detection in buried plastic water pipes[J].Journal of Sound and Vibration,2009,325(3):649-663.
[9]Mayyas K,Momani F.An LMS adaptive algorithm with a new stepsize control equation[J].Journal of the Franklin Institute,2011,348(4):589-605.
[10]Costa M H,Bermudez J C M.A noise resilient variable step-size LMS algorithm[J].Signal Processing,2007,88(3):733-748.
[11]付学志,刘忠,胡生亮,等.低信噪比下的变步长最小均方自适应算法及其在时延估计中的应用[J].中南大学学报:自然科学版,2012,43(3):1010-1019.
[12]彭继慎,刘爽,安丽.低信噪比下基于新型变步长LMS的自适应滤波算法[J].传感技术学报,2013,26(8):1116-1120.
[13]田福庆,罗荣,李克玉,等.基于改进的双曲正切函数变步长LMS算法[J].系统工程与电子技术,2012,34(9):1758-1763.
[14]耿攀飞,夏双志.基于互功率谱法的多天线时延估计研究[J].电子技术与软件工程,2014(1):193-194.
[15]陈颖,吴长奇.基于麦克风阵列的声源定位算法研究及DSP实现[J].燕山大学学报,2013,37(1):63-67,74.