基于空域压缩采样的水声目标DOA估计方法
DOA Estimation Method Based on Spatial Compressive Sampling for Underwater Acoustic Target
查看参考文献13篇
|
文摘
|
在空域存在稀疏观测约束条件下,传统的水声目标方位DOA估计方法在水下无人航行器等小型平台的应用中往往精度低或者失效。针对这一问题,提出了一种基于空域压缩采样的水声目标DOA估计方法。该方法通过建立水下目标的空域稀疏模型,对水下目标在空域进行压缩采样,并利用联合稀疏重构实现水下目标的DOA估计。仿真实验表明:与传统方法相比,该方法在较少阵元、较小阵列间隔和较少快拍下估计精度相对提高;而在较低信噪比下估计成功率能够提高50%以上,均方误差也能降低到0.2°以下。 |
|
其他语种文摘
|
The traditional DOA (direction of arrival) estimation methods of underwater acoustic target often have poor estimation performance or provide inaccurate estimation result under the constraint of spatial sparse observation based on the small platform (such as unmanned underwarter vehicle). A new high-accuracy DOA estimation algorithm based on spatial compressive sampling for underwater acoustic target is proposed by analyzing the space sparsity of underwater target location. The algorithm is used to establish a spatial sparse description model of underwater target, and compress the underwater target in spatial domain, and then the joint sparse reconstruction algorithm is used to achieve the DOA estimation of underwater acoustic target. The simulation results show that the method can increase the DOA estimation accuracy in the case of less array elements and less snapshots, and the high success rate can be increased by more than 50%, and the root mean square error in the low SNR environment can be maintained at 0.2°or less. |
|
来源
|
兵工学报
,2013,34(11):1479-1483 【核心库】
|
|
DOI
|
10.3969/j.issn.1000-1093.2013.11.022
|
|
关键词
|
海洋工程与技术
;
水声目标定位
;
压缩感知
;
水下无人航行器
;
稀疏重构
|
|
地址
|
江苏科技大学电子信息学院, 江苏, 镇江, 212003
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1000-1093 |
|
学科
|
社会科学总论 |
|
基金
|
国家自然科学基金
;
江苏高校优势学科建设工程-船舶与海洋工程项目
;
江苏省高校自然科学基金
|
|
文献收藏号
|
CSCD:5027331
|
参考文献 共
13
共1页
|
|
1.
兰志林. 无人水下航行器发展.
国防科技,2008,29(2):13-15
|
CSCD被引
1
次
|
|
|
|
|
2.
王蓬. 军用UUV的发展与应用前景展望.
鱼雷技术,2009,17(1):7-9
|
CSCD被引
1
次
|
|
|
|
|
3.
Bilik I. Spatial compressive sensing approach for field directionality estimation.
2009 IEEE Radar Conference,2009:1-5
|
CSCD被引
1
次
|
|
|
|
|
4.
Bilik I. Spatial compressive sensing for direction-of-arrival estimation of multiple sources using dynamic sensor arrays.
IEEE Transactions on Aerospace and Electronic Systems,2011,47(3):1754-1769
|
CSCD被引
22
次
|
|
|
|
|
5.
Wang Y. Direction estimation using compressive sampling array processing.
The 15th Workshop on Statistical Signal Processing, IEEE,2009:625-628
|
CSCD被引
1
次
|
|
|
|
|
6.
Gretsistas A. A multichannel spatial compressed sensing approach for direction of errival estimation.
LVA/ICA 9th Intermational Conference Latent Variable Analysis and Signal Separation,2010:458-465
|
CSCD被引
1
次
|
|
|
|
|
7.
Candes E J. Compressive sampling.
Proceedings oh the International Congress of Mathematicians,2006:22-30
|
CSCD被引
1
次
|
|
|
|
|
8.
Candes E J. An introduction to compressive sampling.
Signal Processing Magazine, IEEE,2008,25(2):21-30
|
CSCD被引
796
次
|
|
|
|
|
9.
Donoho D L. Compressed sensing.
IEEE Transactions on Information Theory,2006,52(4):1289-1306
|
CSCD被引
3019
次
|
|
|
|
|
10.
Cai T. On recovery of sparse signals via l_1 minimization.
IEEE Transaction on Information Theory,2009,55(7):3388-3397
|
CSCD被引
14
次
|
|
|
|
|
11.
Li B. Direction estimation under compressive sensing framework: a review and experimental results.
IEEE International Conference on Information and Automation,2011:63-68
|
CSCD被引
2
次
|
|
|
|
|
12.
Lee K. Subspace methods for joint sparse recovery in compressed sensing.
IEEE Trans on Information Theory,2012,58(6):3613-3641
|
CSCD被引
14
次
|
|
|
|
|
13.
Berg E.
Joint-sparse recovery from multiple measurements,2009
|
CSCD被引
1
次
|
|
|
|
|
了解气象卫星更多信息，请访问