| 季建朝,张宇,赵子龙,夏露.阵列测量中的卡尔曼滤波器算法[J].声学技术,2018,37(6):601~606 |
| 阵列测量中的卡尔曼滤波器算法 |
| Kalman filter-based algorithm for acoustic array measurement |
| 投稿时间：2017-11-27 修订日期：2018-02-05 |
| DOI：10.16300/j.cnki.1000-3630.2018.06.016 |
| 中文关键词: 气动噪声|阵列信号处理|波束形成|迭代方程 |
| 英文关键词: aeroacoustics|array processing|beamforming|recursive functions |
| 基金项目:国家自然科学基金（11402305）资助项目。 |
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| 摘要点击次数: 934 |
| 全文下载次数: 663 |
| 中文摘要: |
| 针对经典波束形成算法不具备实时性、占用存储空间大、计算速度慢等缺点，提出了基于卡尔曼滤波器的算法。这种算法将信号处理领域中现有的卡尔曼滤波器理论与阵列信号处理过程相结合，在频域内对声学阵列所采集到的数据进行迭代处理，不仅能够及时发现风洞测量中存在的各种问题，而且可以实时消除由测量环境所引起的各种误差。仿真结果表明，这种算法比经典波束形成算法收敛速度更快，不仅成像效果很好，而且能够对低速运动声源进行定位。此算法具备实时性，为风洞声源的实时定位提供了重要的算法选择。 |
| 英文摘要: |
| The conventional beamforming (CB) is the most popular signal processing technique for noise identification using acoustic sensor arrays. However, CB does not have real-time performance and takes up a lot of storage space, the calculation speed is slow. A new approach called Kalman filter based beamforming method is introduced in this paper, which has a recursive form similar to Kalman filter in signal processing field. The data collected by acoustic array are processed iteratively in frequency domain, it can not only detect various problems in the wind tunnel survey, but also eliminate the errors caused by the test environment in real time. The simulation results show that the algorithm converges faster than the CB algorithm, the imaging results is very good, more importantly, it can accurately locate some low speed moving sound sources. This algorithm runs in real-time, so it is an attractive new algorithm for the real-time localization of wind tunnel sound source. |
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