基于三阶累积量的水声信号时延估计研究
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摘要
针对海洋探测接收信号信噪比低导致水声信号时延估计困难的问题,提出了将三阶累积量的一维切片作为高阶统计量进行时延估计的新方法。该方法通过希尔伯特变换,对相关峰进行锐化处理,提高了时延估计精度。与直接相关法相比,此方法不仅可以在较低信噪比下获得良好的时延估计效果,而且可以抑制相关背景噪声和非高斯噪声干扰的影响,计算机仿真和水池实验验证了此方法在较低信噪比和背景噪声为相关噪声及非高斯色噪声情况下的有效性。此方法为海洋探测中微弱回波信号的时延估计提供了一种新的技术途径。
In order to solve the problem of time delay estimation caused by low signal-to-noise ratio(SNR) of received signals in ocean exploration, a new method of time delay estimation is proposed by using one-dimensional slices of third-order cumulants as high-order statistics. This method improves the accuracy of time delay estimation by combining Hilbert transform to sharpen the correlation peaks and compared with direct correlation method. The proposed method can not only obtain good time delay estimation effect at low SNR, but also restrain the influence of correlated background noise and non-Gaussian noise. Computer simulation and water pool experiments verify the effectiveness of the proposed method with low signal-to-noise ratio and correlated background noise. This method provides a new idea of time delay estimation for detecting weak echo signals in ocean detection.
In order to solve the problem of time delay estimation caused by low signal-to-noise ratio(SNR) of received signals in ocean exploration, a new method of time delay estimation is proposed by using one-dimensional slices of third-order cumulants as high-order statistics. This method improves the accuracy of time delay estimation by combining Hilbert transform to sharpen the correlation peaks and compared with direct correlation method. The proposed method can not only obtain good time delay estimation effect at low SNR, but also restrain the influence of correlated background noise and non-Gaussian noise. Computer simulation and water pool experiments verify the effectiveness of the proposed method with low signal-to-noise ratio and correlated background noise. This method provides a new idea of time delay estimation for detecting weak echo signals in ocean detection.
引文
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[23]HUSSAIN Z M,BOASHASH B.Hilbert transformer and time delay statistical in the presence of Gaussian noise [J].IEEE Transactions on Signal Processing,2002,50(3):501-508.
[2]张亚斌,孙磊,马晶鑫,等.一种基于环境噪声的被动测深方法 [J].海洋测绘,2017,37(6):35-38.
[3]刘伯胜,雷家煜.水声学原理 [M].哈尔滨:哈尔滨工程大学出版社,1993:23.
[4]KNAPP C,CARTER G.The generalized correlation method for estimation of time delay [J].IEEE Transactions on Acoustics,Speech,and Signal Processing,1976,24(4):320-327.
[5]姜彦玲.时延误差的高精度估计与补偿方法研究 [D].西安:长安大学,2017.
[6]景源,姜雪莹.广义二次相关时延估计算法改进 [J].辽宁大学学报,2018,45(2):97-103.
[7]景思源,冯西安,张亚辉.广义互相关时延估计声定位算法研究 [J].声学技术,2014,33(5):464-468.
[8]金留念.基于二次相关的时延估计方法研究 [J].电子信息对抗技术,2011,26(1):39-41.
[9]刘敏,曾毓敏,张铭,等.基于二次相关的语音信号时延估计改进算法 [J].应用声学,2016,35(3):255-264.
[10]程方晓,刘璐,姚清华,等.基于改进时延估计的声源定位算法 [J].吉林大学学报 (理学版),2018,56(35):681-687.
[11]林力新.高精度时延估计算法的研究及其在水声定位系统中的应用 [D].哈尔滨:哈尔滨工程大学,2016.
[12]徐宏宇,孙强,屈乐乐,等.基于改进空间平滑技术的ESPRIT探地雷达时延估计算法 [J].科学技术与工程,2013,13(25):7343-7346.
[13]苏静茹.基于联合角度时延估计的MUSIC算法研究 [D].昆明:昆明理工大学,2014.
[14]陈灿芬.基于声阵列的水下目标定位技术研究 [D].广州:华南理工大学,2011.
[15]蒋忠进.弹性波Chirp信号检测与时延估计研究 [D].长春:吉林大学,2004.
[16]吕婧一.高阶累积量分析及其应用研究 [D].北京:北京邮电大学,2014.
[17]王猛.基于高阶累积量的声矢量阵列信号DOA估计算法研究 [D].长春:吉林大学,2017.
[18]栾风虎,李玉峰,于学明,等.基于高阶累积量的时延估计研究 [J].黑龙江大学自然科学学报,2010,27(2):260-263.
[19]温旋旋.基于高阶累积量的舰船辐射噪声包络特征提取 [D].哈尔滨:哈尔滨工程大学,2017.
[20]陈祖斌,刘昕,孙峰,等.基于三阶累积量一维切片的微地震信号时延估计 [J].湖南大学学报,2016,43(8):120-127.
[21]张端金,张中华,郭建军,等.基于四阶累积量的时延估计新方法 [J].郑州大学学报,2010,31(1):103-106.
[22]余文晶,何琳,崔立林,等.声源定位中的时延估计方法研究进展 [J].声学技术,2016,35(5):232-241.
[23]HUSSAIN Z M,BOASHASH B.Hilbert transformer and time delay statistical in the presence of Gaussian noise [J].IEEE Transactions on Signal Processing,2002,50(3):501-508.