| 基于稀疏信号重构的阵元位置误差校正方法 |
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| 引用本文： | 梁国龙, 邱龙皓, 邹男. 基于稀疏信号重构的阵元位置误差校正方法[J]. 声学学报, 2017, 42(6): 677-684. DOI: 10.15949/j.cnki.0371-0025.2017.06.005 |
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| 作者姓名： | 梁国龙 邱龙皓 邹男 |
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| 作者单位： | 1. 哈尔滨工程大学 水声技术重点实验室 哈尔滨 150001; |
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| 基金项目： | 国家自然科学基金项目(51279043,11504064)和水声技术国家重点实验室基金项目(9140C2000501150C200)资助 |
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| 摘 要： | 阵元位置误差的存在会严重影响水听器阵列的测向性能,为此在使用阵列之前需对该误差进行校正。针对这一需求,提出了一种对任意阵型适用的高精度阵元位置有源校正方法。结合远场阵列模型以及位置误差
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| 关 键 词： | 压缩感知 阵元位置误差 稀疏表示 有源校正 |
| 收稿时间： | 2016-09-14 |
| 修稿时间： | 2016-10-01 |
A sparse signal reconstruction perspective for hydrophone array shape calibration |
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| LIANG Guolong, QIU Longhao, ZOU Nan. A sparse signal reconstruction perspective for hydrophone array shape calibration[J]. ACTA ACUSTICA, 2017, 42(6): 677-684. DOI: 10.15949/j.cnki.0371-0025.2017.06.005 |
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| Authors: | LIANG Guolong QIU Longhao ZOU Nan |
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| Affiliation: | 1. Science and Technology on Underwater Acoustic Laboratory, Harbin Engineering University Harbin 150001;2. College of Underwater Acoustic Engineering, Harbin Engineering University Harbin 150001 |
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| Abstract: | The performance of array processing algorithms critically depends on the precise knowledge of the array shape. Calibration of array shape error must be obtained in advance. A high precision array shape calibration method using sources in known directions which can be applied to arbitrary array geometries is proposed. With the prior knowledge of the bound of senor location uncertainty, the problem of array shape estimation is transformed into the process of sparse signal reconstruction from multiple time measurements with overcomplete basis. A geometry error model combined with compressed sensing method is established. The convex objective function penalized by l1-norm aimed to enforce sparsity is efficiently solved in second-order cone(SOC) programming framework. The l1 SVD algorithm is used to summarize multiple time samples. The physical interpretation and algorithm implementation steps are also explained. Computer simulations indicate the effectiveness of the proposed method by comparing the estimator RMSE to the Cramer-Rao lower bound(CRLB). Other advantages include high calibration precision, robustness to direction of calibration sources and not requiring accurate initialization. |
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| Keywords: | Compressed sensing array shape error sparse signalreconstruction active calibration |
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