基于MEMS矢量水听器的信号处理与DOA估计

发布时间：2019-04-28 10:04

【摘要】：MEMS矢量水听器的优点在于它体积小、一致性好、成本低等优势,且能够同时、共点地获得声场中的标量声压信息和矢量振速信息,而更丰富的输出信号也为声源目标的信号处理与波达方向估计提供了更多的处理手段,并且具备抵消各向同性噪声干扰的能力。近年来,矢量水听器技术在水下目标探测、潜艇隐身及声源定向定位等领域受到了各海洋大国的普遍关注,并已用于水声领域的工程应用阶段,而相关的信号处理问题也成为各国研究的前沿热点。本文对自适应算法和DOA估计算法在水声信号处理中的应用进行了比较系统的研究,主要研究内容如下:(1)根据水声信号与噪声信号特征的差异性,针对MEMS水听器采集的数据“淹没”在强噪声场中的问题,提出采用LMS自适应噪声对消与Fourier变换滤波相结合的改进算法进行信噪分离,对提取后的信号与理想信号做MATLAB仿真实验对比,并从均方误差(RMSE)、信噪比(SNR)、信噪比增益(GSNR)、相似度(R)等方面进行了去噪性能评价分析。仿真结果表明,该改进算法突破了传统自适应噪声对消法在低信噪比(低于0dB)和非平稳噪声环境下去噪效果奇差的局限,在信噪比降至-15dB以下时,仍有很好的去噪能力。(2)利用改进去噪算法得到预处理信号,分别运用反切算法,平均声强法,互谱算法及最小二乘法进行了单矢量水听器的声源的波达方向(DOA,Direction Of Arrival)估计,与设定的方位角进行均方误差与成功率分析,评价改进算法与中心滤波算法的在不同快拍数和信噪比环境下的优劣性能,为工程应用提供实验依据。(3)利用改进算法结合多重信号分类(Multiple Signal Classification,MUSIC)算法进行矢量水听器阵列的DOA估计,评价改进MUSIC算法与原MUSIC算法的在不同快拍数和信噪比环境下的优劣性能,分别比较分析了方位角估计的均方误差与预测成功率,显示出改进算法在低信噪比下的情况下仍有一定的工程应用性。仿真结果表明:该改进去噪算法极大的提高了声源方位角估计精度,其中平均声强法、互谱法在小快拍数和低信噪比下也具有良好的单矢量水听器DOA估计性能精度;改进MUSIC算法在小快拍数和低信噪比下要比原MUSIC算法要具有更高的预测成功率和更小的方位角均方误差值。(4)最后,进行了汾河二库实验。基于汾河湖试数据的实验处理结果表明:经过数据预处理的定位定向结果显示良好,性能良好,计算简单高效,为工程实际应用提供满意的效果。
[Abstract]:The advantages of MEMS vector hydrophone are its small size, good consistency, low cost, and the ability to obtain the scalar sound pressure information and vector vibration velocity information in the sound field simultaneously and at the same time, the vector hydrophone has the advantages of small size, good consistency and low cost. The richer output signal also provides more processing means for signal processing and DOA estimation of sound source target and has the ability to counteract isotropic noise interference. In recent years, vector hydrophone technology in underwater target detection, submarine stealth and sound source orientation and other fields has been widely concerned by the major marine countries, and has been used in the field of underwater acoustic engineering application stage. And the related signal processing problem has also become a hot spot in the research of various countries. In this paper, the application of adaptive algorithm and DOA estimation algorithm in underwater acoustic signal processing is studied systematically. The main contents are as follows: (1) according to the difference of characteristics between underwater acoustic signal and noise signal, the main research contents are as follows: (1) according to the difference between underwater acoustic signal and noise signal, In order to solve the problem of "submerged" data collected by MEMS hydrophone in strong noise field, an improved algorithm combining LMS adaptive noise cancellation with Fourier transform filtering is proposed to separate the signal-to-noise (SNR). The extracted signal is compared with the ideal signal by MATLAB simulation, and the de-noising performance of the extracted signal is evaluated from the aspects of mean square error (RMSE), signal to noise ratio (SNR), signal to noise gain (GSNR), similarity (R) and so on. The simulation results show that the improved algorithm breaks through the limitation of the traditional adaptive noise cancellation method in low signal-to-noise ratio (lower than 0dB) and non-stationary noise. When the signal-to-noise ratio is lower than-15dB, the noise-to-noise ratio is lower than-noise. There is still good denoising ability. (2) pre-processing signal is obtained by using improved denoising algorithm. The direction of arrival (DOA,) of the source of single vector hydrophone is carried out by using anti-tangent algorithm, average sound intensity method, cross-spectral algorithm and least square method respectively. Direction Of Arrival) estimation, mean square error and success rate analysis of azimuth were performed to evaluate the performance of the improved algorithm and the center filtering algorithm in different fast beat number and signal-to-noise ratio (SNR) environment. It provides experimental basis for engineering application. (3) DOA estimation of vector hydrophone array is carried out by using improved algorithm combined with multiple signal classification (Multiple Signal Classification,MUSIC (Multi-signal Classification) algorithm. The advantages and disadvantages of the improved MUSIC algorithm and the original MUSIC algorithm under different snapshot number and signal-to-noise ratio (SNR) are evaluated. The mean square error and prediction success rate of azimuth estimation are compared and analyzed, respectively. It is shown that the improved algorithm still has some engineering application under the condition of low signal-to-noise ratio. The simulation results show that the improved de-noising algorithm greatly improves the accuracy of azimuth estimation of the sound source. The average sound intensity method and cross-spectrum method also have good performance accuracy for DOA estimation of single vector hydrophone under the condition of small snapshot number and low signal-to-noise ratio. The improved MUSIC algorithm has higher prediction success rate and smaller azimuth mean square error than the original MUSIC algorithm under the condition of small snapshot number and low signal-to-noise ratio. (4) at last, the Fenhe No. 2 reservoir experiment is carried out. The experimental results based on the experimental data of Fenhe Lake show that the orientation result after data preprocessing is good, the performance is good, the calculation is simple and efficient, and it provides a satisfactory effect for the practical application of the project.
【学位授予单位】：中北大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TB565.1

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