| 稀疏长时延水声信道的压缩感知估计(英文) |
| |
| 引用本文： | 伍飞云,周跃海,童峰,方世良.稀疏长时延水声信道的压缩感知估计(英文)[J].东南大学学报,2014(3):271-277. |
| |
| 作者姓名： | 伍飞云 周跃海 童峰 方世良 |
| |
| 作者单位： | 1. 厦门大学水声通信与海洋信息技术教育部重点实验室,厦门,361005
2. 东南大学水声信号处理教育部重点实验室,南京,210096 |
| |
| 基金项目： | The National Natural Science Foundation of China No．11274259 the Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education No． UASP1305． |
| |
| 摘 要： | 提出一种基于压缩感知框架下的长时延水声信道估计算法.用传统的自适应算法如最小二乘(LS)算法处理典型的长时延水声信道的估计问题时,会导致其收敛速率下降,即跟踪能力有限,而使用时延多普勒函数则加大了计算量和复杂度.通过训练序列构建一个Toeplitz矩阵作为测量矩阵,将长时延信道估计问题转为压缩感知问题,并利用信道的稀疏结构特性进行稀疏估计.与传统的l1范数或基于指数形式的近似l0范数稀疏恢复策略不同,所提出的是一种新的似l0范数稀算法(简称AL0),该算法通过融合最陡梯度和迭代投影寻优进行求解.仿真与海试数据结果验证了所提算法的优越性.
|
| 关 键 词： | 范数约束 稀疏水声信道 压缩感知 |
Compressed sensing estimation of sparse underwater acoustic channels with a large time delay spread |
| |
| Wu Feiyun,Zhou Yuehai,Tong Feng,Fang Shiliang.Compressed sensing estimation of sparse underwater acoustic channels with a large time delay spread[J].Journal of Southeast University(English Edition),2014(3):271-277. |
| |
| Authors: | Wu Feiyun Zhou Yuehai Tong Feng Fang Shiliang |
| |
| Institution: | Wu Feiyun Zhou Yuehai Tong Feng Fang Shiliang ( 1. Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of Minister of Education, Xiamen University, Xiamen 361005, China;2. Key Laboratory of Underwater Acoustic Signal Processing of Minister of Education, Southeast University, Nanjing 210096, China) |
| |
| Abstract: | The estimation of sparse underwater acoustic channels with a large time delay spread is investigated under the framework of compressed sensing. For these types of channels, the excessively long impulse response will significantly degrade the convergence rate and tracking capability of the traditional estimation algorithms such as least squares （LS）, while excluding the use of the delay-Doppler spread function due to huge computational complexity. By constructing a Toeplitz matrix with a training sequence as the measurement matrix, the estimation problem of long sparse acoustic channels is formulated into a compressed sensing problem to facilitate the efficient exploitation of sparsity. Furthermore, unlike the traditional l1 norm or exponent-based approximation l0 norm sparse recovery strategy, a novel variant of approximate l0 norm called AL0 is proposed, minimization of which leads to the derivation of a hybrid approach by iteratively projecting the steepest descent solution to the feasible set. Numerical simulations as well as sea trial experiments are compared and analyzed to demonstrate the superior performance of the proposed algorithm. |
| |
| Keywords: | norm constraint sparse underwater acoustic channel compressed sensing |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
