海底沉积物分类以及低空目标定位中的声信号处理算法研究
文献类型：学位论文
| 作者 | 江峰 |
| 学位类别 | 博士 |
| 答辩日期 | 2001 |
| 授予单位 | 中国科学院声学研究所 |
| 授予地点 | 中国科学院声学研究所 |
| 关键词 | 沉积物分类 神经网络 被动定位 信号处理算法 窄带处理方法 |
| 其他题名 | Research on acoustic signal processing algorithm in seabed sediment classification and low-altitude target localization |
| 中文摘要 | 该文研究内容包括三个部分.第一,沉积物声学分类中的信号处理算法及其实验研究.第二,低空目标声学定位中的信号处理算法研究以及演示系统的建立.文中将重点介绍这两方面取得的有效实验结果.第三,海底声学探测技术中储备性新技术研究方向的思考,文中简单讨论子波分析、自适应滤波和声矢量传感器的应用前景.在低空目标声学定位技术的研究中,该文主要结果:建立了窄带处理的演示系统,现场试验结果给出满意的目标轨迹测量结果和目标运动参数估计精度.该文主要结论:(1)在低宽目标声学定位技术中发展双基地布阵和窄带处理方法是必要的和可行的.(2)该文建议的基于窄带处理的定位方法具有基阵布放灵活,良好的抗噪声干扰能力,软件实现简单,并且适合处理运动目标等优点. |
| 英文摘要 | The research work presented in this report is composed of three parts. The first part is the research on signal processing algorithm and its experimental research in acoustic sediment classification. The second part is the research on acoustic signal processing algorithm and the establishment of demonstration system in low altitude target localization. Effective experimental results about these two aspects will be given concentration in this paper. The third part is some discussion about new research direction for acoustic seabed exploration technology. Prospects for the application of adaptive filter, wavelet transform and vector sensor will be given simple discussion. Main results for the application of echo sounder to make research into sediment classification include the following. The laboratory simulation system for sediment classification is established. Real-time classification is conducted with total percent accuracy. Experimental data recorded in trial at the China-South-Sea regions are analyzed in detail, achieving satisfactory classification result which reaches a accuracy rate of more than 90%. As regards the research on the signal processing algorithm in acoustic sediment classification, conclusions can be summarized as follows. -Principal Component Extraction Neural Network converts higher-dimensional information into intuitionistic diagram with two dimensions. It is helpful for decision-making in acoustic sediment classification system. -With features in simple structure, rapid convergence speed and strong generalization capability, Pi-Sigma Higher Order Neural Network is an effective method to fulfill real-time calculation of sediment classification. In the research of low altitude target localization, a demonstration system based on narrow-band processing method is established which gives satisfactory results for track measurement of moving target in the out-field experiment. Main conclusions are reached in this report that include the following. -It is necessary and feasible to develop narrow-band processing method and double-base array arrangement in acoustic target localization. - The method proposed in the paper is characteristic of such advantages as flexibility for sensor array arrangement, excellent capability to resist noise interference, simplicity in software realization, fitting for processing moving target. |
| 语种 | 中文 |
| 公开日期 | 2011-05-07 |
| 页码 | 57 |
| 源URL | [http://159.226.59.140/handle/311008/810]  |
| 专题 | 声学研究所_声学所博硕士学位论文_1981-2009博硕士学位论文 |
| 推荐引用方式 GB/T 7714 | 江峰. 海底沉积物分类以及低空目标定位中的声信号处理算法研究[D]. 中国科学院声学研究所. 中国科学院声学研究所. 2001. |
入库方式： OAI收割
来源：声学研究所
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