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基于简正波分解的不同阵列匹配场定位性能分析
引用本文:贾雨晴,苏林,郭圣明,马力.基于简正波分解的不同阵列匹配场定位性能分析[J].物理学报,2018,67(17):174302-174302.
作者姓名:贾雨晴  苏林  郭圣明  马力
作者单位:1. 中国科学院水声环境特性重点实验室, 北京 100190;2. 中国科学院声学研究所, 北京 100190;3. 中国科学院大学, 北京 100049
基金项目:国家自然科学基金(批准号:Y11704396)和中国科学院声学研究所青年基金(批准号:CXJJ-16S057)资助的课题.
摘    要:针对利用不同阵列对浅海环境中水下目标的定位问题,基于简正波分解方法,对组合阵的目标声源定位性能进行了研究,着力解决在实际实验环境下定位性能不够高的问题,并降低实验设备布放难度.在浅海环境下,基于匹配场理论的声接收阵可实现目标的定位,但定位性能受阵形、阵元数目等影响.通过研究不同声接收阵的简正波分解矩阵,可以有效辨别不同阵形定位性能的优劣.仿真实验表明,当某一子阵简正波分解效果较差时,会降低组合阵的定位性能.基于实际实验的需求,在对短垂直阵和组合阵性能的研究中发现,由于水平阵对接收声场的定位模糊度函数中的旁瓣有抑制效果,从而造成模糊度函数表面上旁瓣较低,定位目标的主旁瓣比有所提升的现象.仿真实验表明,不同组合阵形的定位准确度均在90%以上,基于实际应用的考虑,组合阵无疑是对定位性能和实验复杂度的折中选择.

关 键 词:简正波分解  匹配场  组合阵形
收稿时间:2018-01-17

Performance analysis of matched field processing localization with various line array configurations based on normal mode decomposition
Jia Yu-Qing,Su Lin,Guo Sheng-Ming,Ma Li.Performance analysis of matched field processing localization with various line array configurations based on normal mode decomposition[J].Acta Physica Sinica,2018,67(17):174302-174302.
Authors:Jia Yu-Qing  Su Lin  Guo Sheng-Ming  Ma Li
Institution:1. Key Laboratory of Underwater Environment, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China;2. Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China;3. University of Chinese Academy of Science, Beijing 100049, China
Abstract:Various line array configurations are evaluated for the source localization performance based on the analysis of mode decomposition matrix in this paper. The guideline of array shape design focuses on improving the localization performance of matched filed processing, meanwhile reducing the difficulty of deploying equipment in practical experiments. In the shallow water environment, when the environment is well known, the source localization result can be obtained by matched field processing algorithms effectively, but the source localization performance is affected by the array parameters, such as array length, the number of sensors, and the configurations of various horizontal and vertical line arrays. The modal decomposition method provides a useful insight into the questions of how many modes are needed and how to design the array to resolve the modes. Therefore, the method of utilizing a normal mode acoustic propagation model to decompose mode is proposed by vertical line array, horizontal line array and combined array respectively. Then we can evaluate the source localization performance of various line array configurations by studying the characteristic of normal mode decomposition matrix, thus establishing a qualitative or even quantitative relationship between each other. The more the normal mode decomposition matrix tends to be diagonalized, the better performance of line array localization will be obtained. Simulation results show that the localization performance of matched field processing with the combined arrays will be severely degraded when the mode amplitudes cannot be accurately deduced by one of the sub-arrays. Considering the requirements for the practical experiments and various environments, the source localization performance of short vertical line array and combined array are mainly discussed in this paper. The combined array can increase the azimuth and depth information of the source and realize three-dimensional target detection while the vertical array provides range-depth information and the horizontal array provides bearing information. Simulation result indicates that the design guidelines based on the normal mode decomposition are appropriate for arrays employed for matched filed processing. Meanwhile, the combined arrays perform better than the short vertical array, which is benefited by the horizontal array's suppressing the side lobes, which leads the ratio of peak to sidelobe to increase, and thus improving the location accuracy. The values of localization accuracy of combined arrays are all above 90% according to the simulation experiment. Take the practical application into account, the combined array is undoubtedly a compromise choice for the localization performance and the test complexity.
Keywords:normal mode decomposition  matched field processing  multi-array
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