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论文:2019,Vol:37,Issue(4):697-703 |
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引用本文： |
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陈克安, 胥健, 王磊, 周兵. 基于声场分解和稀疏正则化的二维空间次级声源布局优化[J]. 西北工业大学学报 |
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CHEN Kean, XU Jian, WANG Lei, ZHOU Bing. Optimization of Secondary Sources Configuration in Two-Dimensional Space Based on Sound Field Decomposition and Sparsity-Inducing Regularization[J]. Northwestern polytechnical university |
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| 基于声场分解和稀疏正则化的二维空间次级声源布局优化 |
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| 陈克安1,2,3, 胥健2,3, 王磊2,3, 周兵1 |
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1. 中国电力科学研究院有限公司 电网环境保护国家重点实验室, 湖北 武汉 430074;
2. 西北工业大学 航海学院, 陕西 西安 710072;
3. 海洋声学信息感知工信部重点实验室(西北工业大学), 陕西 西安 710072 |
| 摘要: |
| 在有源噪声控制系统电声器件布局设计中，目前的优化方法需要预先确定误差传感器的布局，这样大大增加了后续次级声源布局优化的工作量。利用高阶传声器拾取声场信息，在波域构造有源控制代价函数，从而解除了对误差传感器布局信息的需求。在此基础上，根据初级声场的稀疏特性，引入稀疏正则化方法，通过求解欠定方程的稀疏近似解，实现了次级声源布局（个数和空间位置）优化。讨论了备选次级声源个数对优化结果的影响，并与均匀布局和遗传算法优化布局结果进行了比较。结果表明，所提方法在不依赖于误差传感器布局信息的情况下可以有效地优化次级声源布局，降噪效果与遗传算法布局结果相近，其他系统评价指标明显更优，有利于有源系统的稳定运行。 |
| 关键词:
有源噪声控制
次级声源布局
声场分解
稀疏正则化
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| Optimization of Secondary Sources Configuration in Two-Dimensional Space Based on Sound Field Decomposition and Sparsity-Inducing Regularization |
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| CHEN Kean1,2,3, XU Jian2,3, WANG Lei2,3, ZHOU Bing1 |
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1. State Key Laboratory of Power Grid Environmental Protection, China Electric Power Research Institute, Wuhan 430074, China;
2. School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China;
3. Key Laboratory of Ocean Acoustics and Sensing(Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi'an 710072, China |
| Abstract: |
| During the design of transducers configuration for an active noise control system, current optimization methods need to predetermine the error sensors configuration, which significantly increases the workload of later optimization of the secondary sources configuration. In this study, a new method free from specific error sensors configuration information is presented that higher order microphones are used to capture the sound field so as to formulate the cost function in wave domain. In addition, according to sparsity characteristics of the primary sound field, sparsity-inducing regularization is introduced to optimize the secondary sources configuration, including the number and positions, by calculating a sparse approximate solution to underdetermined equations. Effects of the number of candidate secondary sources are discussed, and the comparison with the uniform configuration and the optimized configuration using the genetic algorithm is performed. Results show that the proposed method can optimize the secondary sources configuration effectively independent of the error sensors configuration information. The noise reduction of the proposed method is close to that by the genetic algorithm, while other evaluation metrics for the system are much better, which would benefit the stability of active noise control system. |
| Key words:
active noise control
secondary sources configuration
optimization
sound field decomposition
sparsity-inducing regularization
algorithms
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| 收稿日期: 2018-09-03
修回日期:
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| DOI: 10.1051/jnwpu/20193740697 |
| 基金项目: 电网环境保护国家重点实验室开放基金和中央财政专项基金（MJ-2015-F-044）资助 |
| 通讯作者:
Email： |
| 作者简介: 陈克安(1965-),西北工业大学教授,主要从事噪声控制研究。
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| 作者相关文章 |
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| 陈克安 在本刊中的所有文章 |
| 胥健 在本刊中的所有文章 |
| 王磊 在本刊中的所有文章 |
| 周兵 在本刊中的所有文章 |
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参考文献: |
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