Detection of small target in sea clutter based on spatio-temporal chaos analysis

Zheng-zhou LI; Lei CAO; Wan-xing SHAO; Wen-hao CHEN; Bing LIU

doi:10.3788/OPE.20182601.0193

您当前的位置：

首页 >

文章列表页 >

Detection of small target in sea clutter based on spatio-temporal chaos analysis

Information Sciences | 更新时间：2020-08-13

- Detection of small target in sea clutter based on spatio-temporal chaos analysis
- Optics and Precision Engineering Vol. 26, Issue 1, Pages: 193-199(2018)
- 作者机构：
  
  1.重庆大学通信工程学院, 重庆 400044
  2.中国科学院光束控制重点实验室, 四川成都 610209
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182601.0193
  CLC： TN99
- Received：12 May 2017，
  
  Accepted：15 July 2017，
  
  Published：25 January 2018
- 稿件说明：
移动端阅览
Zheng-zhou LI, Lei CAO, Wan-xing SHAO, et al. Detection of small target in sea clutter based on spatio-temporal chaos analysis[J]. Optics and precision engineering, 2018, 26(1): 193-199.
DOI：

Zheng-zhou LI, Lei CAO, Wan-xing SHAO, et al. Detection of small target in sea clutter based on spatio-temporal chaos analysis[J]. Optics and precision engineering, 2018, 26(1): 193-199. DOI： 10.3788/OPE.20182601.0193.

摘要

为了提升海杂波中小弱目标检测能力，通过研究海杂波在空域和时域上的动态特性，提出基于空时混沌分析的海面小弱目标检测方法。首先，在海杂波混沌动力系统相空间重构的基础上提取海浪序列图像的空域混沌参数和时域混沌参数，验证海杂波在空域与时域具备混沌性；然后，采用径向基函数神经网络学习海杂波的空域混沌重构函数、时域混沌重构函数和空时耦合系数，联合空域和时域混沌特性综合重构海杂波在时间与空间上的传播规律。多种复杂度海面目标检测试验结果表明，与空域混沌和时域混沌方法相比，预测误差可降低10%，检测概率可提高20%，基于空时混沌重构的海杂波抑制能力与小目标检测性都得以显著提升。

Abstract

To improve the performance of the clutter suppression and small target detection

a detection algorithm for a small target in sea clutter was proposed

based on the spatio-temporal chaos analysis. First

the sea clutter phase space was reconstructed as a chaotic dynamical system

and the chaotic parameters of the sea clutter sequence image were extracted to verify that the sea clutter owns chaotic properties in the spatial and temporal domains. Furthermore

the spatial chaotic reconstruction function

the temporal chaotic reconstruction function

and the space-time coupling coefficient were estimated by the radial basis function neural network. Finally

the spatial and temporal chaotic functions were integrated jointly to reconstruct the spreading regularity of the moving sea clutter. Some experiments were induced on the small target in various fluctuating sea clutter

and the experimental results show that the proposed algorithm

can improve the performance of the sea clutter suppression and enhance the small target detection ability

with the prediction error reduced by 10% and the detection probability increased by 20%.

关键词

Keywords

references

曾文静, 万磊, 张铁栋, 等.复杂海空背景下弱小目标的快速自动检测[J].光学精密工程, 2012, 20(2):403-412.

ZENG W J, WAN L, ZHANG T D, et al.. Fast detection of weak targets in complex sea-sky background[J]. Opt. Precision Eng., 2012, 20(2):403-412. (in Chinese)

HAYKIN S, PUTHUSSERYPADY S. Chaotic dynamics of sea clutter[J]. Chaos, 1997, 7(4):777-802

MELIEF H W, GREIDANUS H, VAN GENDEREN P, et al.. Analysis of sea spikes in radar sea clutter data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(4):985-993.

DOYURAN U C, TANIK Y. Expectation maximization-based detection in range-heterogeneous Weibull clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(4):3156-3166.

ROY L P, KUMAR R V R. Accurate K-distributed clutter model for scanning radar application[J]. IET Radar, Sonar & Navigation, 2010, 4(2):158-167.

裴立力, 罗海波, 耿彦峰, 等.基于小波变换的海面目标自动检测[J].兵工学报, 2008, 29(4):401-404.

PEI L L, LUO H B, GENG Y F, et al.. Automatic sea target detection based on wavelet transform[J]. Acta Armamentarii, 2008, 29(4):401-404. (in Chinese)

徐芳, 刘晶红, 曾冬冬, 等.基于视觉显著性的无监督海面舰船检测与识别[J].光学精密工程, 2017, 25(5):1300-1311.

XU F, LIU J H, ZENG D D, et al.. Detection and identification of unsupervised ships and warships on sea surface based on visual saliency[J]. Opt. Precision Eng., 2017, 25(5):1300-1311. (in Chinese)

HAN S K, RA W S, WHANG I H, et al.. Linear recursive passive target tracking filter for cooperative sea-skimming anti-ship missiles[J]. IET Radar, Sonar & Navigation, 2014, 8(7):805-814.

张仲瑜, 焦淑红.多特征融合的红外舰船目标检测方法[J].红外与激光工程, 2015, 44(S1):29-34.

ZHANG ZH Y, JIAO SH H. Infrared ship target detection method based on multiple feature fusion[J]. Infrared and Laser Engineering, 2015, 44(S1):29-34. (in Chinese)

HAYKIN S, LI X B. Detection of signals in chaos[J]. Proceedings of the IEEE, 1995, 83(1):95-122.

LI B X, HAYKIN S. Chaotic detection of small target in sea clutter[C]. Proceedings of 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, 1993: 237-240.

李正周, 陈静, 沈美容, 等.基于混沌神经网络的海上目标图像的海杂波抑制方法[J].光电子·激光, 2014, 25(3):588-594.

LI ZH ZH, CHEN J, SHEN M R, et al.. Sea clutter suppression approach for target images at sea based on chaotic neural network[J]. Journal of Optoelectronics·Laser, 2014, 25(3):588-594. (in Chinese)

何四华, 杨绍清, 石爱国, 等.基于图像区域Lyapunov指数的海面舰船目标检测[J].物理学报, 2009, 58(2):794-801.

HE S H, YANG SH Q, SHI A G, et al.. Detection of ship targets on the sea surface based on Lyapunov exponents of image block[J]. Acta Physica Sinica, 2009, 58(2):794-801. (in Chinese)

XIN ZH H, LIAO G SH, YANG ZH W, et al.. A deterministic sea-clutter space-time model based on physical sea surface[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(11):6659-6673.

张淑清, 贾健, 高敏, 等.混沌时间序列重构相空间参数选取研究[J].物理学报, 2010, 59(3):1576-1582.

ZHANG SH Q, JIA J, GAO M, et al.. Study on the parameters determination for reconstructing phase-space in chaos time series[J]. Acta Physica Sinica, 2010, 59(3):1576-1582. (in Chinese)

LEUNG H, HENNESSEY G, DROSOPOULOS A. Signal detection using the radial basis function coupled map lattice[J]. IEEE Transactions on Neural Networks, 2000, 11(5):1133-1151.

EGOLF D A, MELNIKOV I V, PESCH W, et al.. Mechanisms of extensive spatiotemporal chaos in Rayleigh-Bénard convection[J]. Nature, 2000, 404(6779):733-736.

LEI Y W, DING L X, ZHANG W SH. Generalization performance of radial basis function networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(3):551-564.

Views

373

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Multi-feature cross UAV image detection algorithm under cross-layer attentional interaction

Identification and suppression of stars under large range of star field density variation

Target detection using spectral unmixing

Mandibular fracture detection with 3M-YOLOv5 network based on enhanced feature extraction capability

Lightweight target detection network for UAV platforms

Related Author

ZHANG Zhihao

DU Lixia

HOU Yue

HAO Ziwei

YIN Jie

YANG Yukai

GU Jan

WANG Janli

Related Institution

College of Electronic and Information Engineering， Lanzhou Jiaotong University

Unit of PLA

Jilin Provincial Key Laboratory of Intelligent Wavefront Sensing and Control

University of Chinese Academy of Sciences

Changchun Institute of Optics，Fine Mechanics and Physics，Chinese Academy of Sciences

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰